Fullstory Blog

Introducing Fullstory MCP: Enable intelligent digital experiences anywhere

Fullstory — Thu, 19 Mar 2026 05:00:00 GMT

The most sophisticated AI models in the world don’t actually know what your users are doing. While Large Language Models (LLMs) reason through complex logic, they remain disconnected from the human context of your digital product. These models rely upon training data and what you tell them (logs, tickets, and manual exports), rather than what they can see.

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Introducing Fullstory MCP

Today, we are excited to announce the Fullstory Model Context Protocol (MCP) server, now in beta. Fullstory MCP enables your team, regardless of technical ability or Fullstory knowledge, to ask questions about the human context that makes or breaks your digital products. By leveraging the open MCP standard, Fullstory provides the digital sight that your AI stack has been missing.

We have come to expect digital experiences to be intelligent and adaptive, but the process of improving them remains slow and reactive. When a conversion rate dips, the "why" is usually buried under a mountain of manual analysis and event lists.

This reactive cycle means that while your team is busy analyzing yesterday’s data, your customers are experiencing that friction right now. The Fullstory MCP server collapses this analysis time from hours to seconds. Other tools require manual tagging and deliver event streams, an approach that we believe will leave you with an incomplete and flawed picture. Conversely, Fullstory enables your AI to see the full human context of your product, helping it solve customer struggles in real time.

Stop querying. Start asking.

The Fullstory MCP server transforms your analytics workflow from complex SQL queries into a natural conversation. Instead of spending hours trying to spot the struggle in your data, you simply ask. This gives your team immediate performance answers by automatically drawing context from your sessions, segments, metrics, and more.

Going forward, you will be able to provide your AI stack with direct access to Fullstory’s behavioral intelligence:

Improve your product for your target market: Surface trends and friction points within any specific user cohort. Ask, "What are the top frustration signals for iOS users on the new billing page?" to see exactly how your most valuable segments are struggling.
Increase conversion by having AI watch sessions: Ask AI to watch sessions or cohorts for you, surfacing the exact behaviors that drive conversion. Prompt: “Pretend that you are the best UX analyst on the planet. After reviewing sessions, make a few recommendations that will improve engagement and conversion.”

Get quick answers on key metrics: Ask, “What is the conversion rate from search to checkout, and how is the conversion rate trending over the last 30 days?” to get quick answers on top metrics without coming to Fullstory.
Reduce friction and improve conversion: Leverage Fullstory’s most powerful diagnostic engine, StoryAI Opportunities. Ask, "What are the top three ways I can improve conversion for our signup funnel?" to have your AI surface and rank the highest-impact friction points and likely root causes automatically.
Improve data storytelling with visualizations: Generate charts and tables directly inside your LLM interface for instant, presentation-ready reporting.
Proactively find UI improvements: Have AI suggest optimizations and UI fixes based on behavioral anomalies detected in the background.

Agentic optimization

Text-based insight retrieval is only the starting line. We are aggressively building toward an agentic workspace where the Fullstory MCP server doesn't just answer questions; it optimizes experiences. Over the coming months, we will roll out more powerful capabilities that allow our MCP server to not only surface the insights about the user experience, but also allow your AI agent to quickly fix the friction or optimize the experience: It’s time to stop guessing and start building intelligent digital experiences powered by human context.

Join the beta program

We are currently rolling out the Fullstory MCP server to a select group of customers. If you want to be among the first to converse directly with your behavioral data, we want to hear from you.

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How Vio.com creates smooth booking experiences for over 100M travelers

The Fullstory Team — Tue, 21 Mar 2023 04:00:00 GMT

Founded in 2016, Vio.com is an Amsterdam-based online travel agency serving over 100 million global users every year. Travelers around the world use Vio.com to compare rates and find the best deals on hotel accommodations at their destination.

In addition to helping travelers find the lowest rates with major travel suppliers like TripAdvisor, Kayak, and more, Vio.com aims to make the booking experience as seamless and stress-free as possible. With Fullstory’s Digital Experience Intelligence platform, Vio.com’s teams resolve bugs faster, save money on headcount, and conduct valuable UX research for improving their website.

With Fullstory’s industry-leading approach to data privacy, Vio.com builds better digital customer experiences while guarding end-user data and PII.

4 ways Vio.com is using behavioral data to make booking travel a smooth flight

1. Add important context to product analytics data

The Vio.com team uses multiple tools to monitor user behavior quantitatively, but only Fullstory reveals the why behind the trends they identify.

For example, they noticed conversion rates drop sharply for some users on a particular area of their website. When they investigated the pattern with Session Replay in Fullstory, the Vio.com team was quickly able to identify the issue: the button that users were meant to click to move forward was hidden behind another element on the page, and users didn’t know how to proceed.

Without Fullstory, uncovering the reasons behind changes in key metrics like this one would require Vio.com to hire additional analysts and take much longer to resolve, without any guarantee of understanding user behavior.

2. Diagnose and fix bugs faster

Vio.com’s engineering team relies on Fullstory to quickly understand what’s driving errors that crop up on their site. Engineers use behavioral data to see exactly how users experienced errors without having to manually recreate them—saving time, speeding error resolution, and granting clarity into how errors impact the user experience.

3. Spark empathy and innovation from the top down

Vio.com cares about customers above all else, and they never want a customer to feel frustrated during the booking process. There’s nothing like seeing a pixel-perfect recreation of a user Rage Clicking (and then abandoning) your website to ignite empathy and inspire action—and Fullstory provides just that. The leadership team at Vio.com finds Fullstory to be an irreplaceable tool for keeping in touch with their customers’ experiences and building empathy for how visitors feel on their site. Plus, watching recreations of anonymous user sessions promotes innovative ideas from team members at all levels of the organization.

4. Conduct roadmap-influencing UX research

Vio.com has used many methods for conducting user experience research: Surveys, usability studies, interviews, focus groups, and more. And while these tools have their merits, only Fullstory lets Vio.com’s teams see how users interact with their site in the wild—all while keeping user privacy as a top priority.

What’s more, when Vio.com’s UX team identifies a behavioral pattern they want to investigate, Fullstory makes it easy to drill down into the qualitative data surrounding user trends to understand exactly why something is happening. These insights help the product team prioritize new features for their roadmap and monitor each release to ensure the digital experience is top-tier for each of their customers.

How to master ecommerce conversion rate optimization with behavioral data

The Fullstory Team — Mon, 15 Jun 2026 11:00:00 GMT

Whether you're running ads, doing SEO, or a bit of both, you're likely spending a significant budget driving traffic to your website. But if visitors aren't turning into buyers, it might be time to add ecommerce conversion rate optimization (CRO) to the mix.

Ecommerce CRO refers to using data and user feedback to increase the number of visitors who complete a purchase. It relies on analytics tools that show not just what users are doing, but why, so you can identify what actually drives conversions.

Below, we'll break down ecommerce CRO in three actionable stages: establishing your baseline, applying the right CRO strategies, and building a compounding testing process.

What is ecommerce conversion rate optimization (CRO)?

As mentioned, ecommerce conversion rate optimization is the process of using user data and feedback to convert more visitors into buyers. It involves understanding what users say, do, and experience throughout their journey and adjusting your platforms and conversion funnels accordingly.

Even small optimizations can help you reduce cart abandonment, decrease drop-offs, and ultimately drive more purchases. And even when the results don't sound impressive, they can significantly impact your bottom line—for example, increasing your rate from 2% to 3% translates to 50% more revenue.

Your current and desired conversion rates should guide your CRO efforts. You can calculate them using the following formula:

(Total Conversions / Total Visitors) x 100

For example, if you had 70 sales from 5,000 visitors last month, your conversion rate is just above one percent:

(70 / 5000) x 100 = 1.4%

Beyond macro-conversions, or completed purchases, you should also track micro-conversions. These are smaller actions, such as adding items to the cart or wishlist, that indicate interest and help you understand where your funnel leaks.

For example, if 500 visitors are adding items to their carts but only 10 are making the purchase, there may be checkout friction worth addressing.

What's a good ecommerce conversion rate?

On average, ecommerce brands convert roughly 3% of users, while top performers can exceed 5%.

However, "good" conversion rates depend on multiple factors, such as:

Traffic source: Organic traffic typically converts better than paid social traffic.
Device: Mobile converts at roughly half the rate of desktop.
Product type – Luxury goods typically convert lower than everyday items.
Industry/vertical: Different industries have different benchmarks.

For example, the food and beverage industry consistently has some of the highest conversion rates, with averages reaching up to 6% in 2026. In contrast, the luxury and jewelry industry traditionally sees conversion rates of around 1%.

Overall, it can be useful to know industry benchmarks, but your only goal should be beating your own results every month. Otherwise, you might underestimate or overestimate the real potential impact of your CRO experiments.

Why quantitative data isn't enough

Most ecommerce brands only collect quantitative data, like page views and the number of purchases, which only show what happened but not why.

For example, GA4 might show you that users are dropping off at checkout, but not whether that's because of a broken field, a hidden fee, or a form that didn't work on mobile.

Determining this requires combining quantitative data with behavioral signals, such as:

Heatmaps
Rage clicks
Thrashed cursors
Session replays

These signals give you clues on which fixes to implement. For example, heatmaps can reveal that users drop off before seeing the CTAs, so you can test simply moving the CTA above the fold.

The best product analytics tools for ecommerce, like Fullstory collect these behavioral signals in the background while users interact with your website or app. Fullstory Analytics combines the data in a single dashboard and automatically captures user interactions for your review. You can analyze them at any point, without predefining what you want to track at setup.

For instance, if you suddenly notice spikes in drop-off rates, you can review historical user interactions to identify friction points.

Building your CRO baseline

That's enough theory; let's start building your CRO program by establishing your baseline. First, consider which metrics you want to improve, and note down where they currently stand.

Here are some typical metrics used for CRO:

Overall conversion rate
Add-to-cart rate
Cart abandonment rate
Checkout completion rate
Revenue per visitor (RPV)
Average order value (AOV)

Baseline metrics can help guide your CRO program. For example, high add-to-cart but low checkout completion rates suggest the issue is in your payment flow, not the product page. So, instead of fixing the latter, you can focus on the former.

Funnel mapping and drop-off analysis

Baselines can show you which metrics to improve, but not where to apply the fixes. This requires you to understand your current funnel and where the biggest leaks occur—the starting point for conversion funnel optimization.

Start with funnel mapping. Identify all the steps users take to move from initial awareness to purchase, like arriving at your landing page, adding items to the cart, and proceeding to checkout.

You can then measure how many visitors move through each step, where they drop off, and investigate what's causing the leak.

Let's say that 70% of users drop off on the product page. It's obvious that something isn't working, but it's still not clear what. Tools like Fullstory's session replays can help you identify the issue.

Replays let you review historical user sessions without waiting for new data to come in. You can access them at any point and quickly identify the root cause. For instance, you might see users hit a mobile navigation issue, bounce when they see the shipping cost, or struggle to find the CTA button. This tells you exactly what to fix.

Ecommerce CRO strategies

Your data should always guide your ecommerce optimization strategy. Still, it's useful to know some common issues, general CRO recommendations, and conversion rate optimization examples. Further, to actually implement these strategies and measure their impact, you will need a reliable stack of appropriate ecommerce CRO tools.

Let's start with one of the first funnel touchpoints: the product page.

Product page optimization

Product pages either convert users or cause them to exit. While standard analytics tools like GA4 can show you which outcome is more dominant, behavioral analytics tools help you understand what’s driving it.

Here's what they might uncover:

Heatmaps could reveal that 50% of users scroll past the most important elements on your product page, such as the price or the CTA. You could fix this by making these elements more prominent through design, placement, or even pop-ups.
Scroll maps could show that only 20% of users reach your reviews section. You could address this by moving star ratings and review snippets closer to the add-to-cart button.
Session replays might reveal that 70% of users are trying to open your size charts or zoom in on product images. Consider enabling these interactions to decrease user frustration.

Beyond identifying what doesn't work, these tools also help you understand what does. Sessions from users who converted can show you which elements drive purchase decisions—such as trust badges, product videos, or even specific image angles.

While reviewing the data, pay special attention to key CRO elements and related behavioral signals:

CRO element	Signals it may need fixing
High-quality photography	Limited zoom or interaction with visuals
Benefit-driven product descriptions	Low engagement with product descriptions
Transparent pricing, including shipping costs	High drop-offs after shipping costs become visible late in the funnel
Visible social proof	Users scrolling past reviews or trust badges without engagement

User-generated content (UGC) and video also have an increasingly important role in helping shoppers feel confident enough to buy. If they're missing from your product page, consider adding them.

Checkout optimization

Checkout abandonment rates average around 70%, so don't be surprised if you're seeing similar levels of drop-off. Luckily, this leak is easily fixed when you understand what’s causing it.

Here are some common culprits and fixes:

Issue	Fix
Unexpected shipping costs appearing late in the journey	Show them earlier in the funnel.
Forcing users to create an account	Switch to guest checkout as the default.
Confusing or lengthy forms	Add autocompletion, real-time inline validation, and numeric keyboards for zip code fields.
Missing payment options	Add frequently requested payment options.

Session replays can show you which elements cause friction. For instance, you can see which form fields cause hesitation or exits, so developers can fix the actual problem rather than guessing what it is.

As for the general CRO recommendations, trust signals play a big role in converting more cautious users. Consider adding the following:

SSL badges
A clear return policy
Recognizable payment icons, like Apple Pay and PayPal

Where you place them can be just as important as adding them at all. If they’re in the wrong spot, users might scroll right past them without noticing. Heatmaps and session replays can also help validate the current placement.

Mobile CRO

For many ecommerce stores, mobile traffic exceeds desktop but converts at roughly half the rate. The issue is not just the design, but often the structure, too.

Mobile experience requires different CRO elements than desktop, such as:

Simplified navigation
Sticky add-to-cart buttons
Native payment options that eliminate typing, like Apple Pay and Google Pay

Mobile page speed is another big one. Average webpages take 70% more time to load on mobile than on desktop, and most mobile users will abandon pages that take more than 3 seconds to load. So, optimizing your mobile page speed isn't just a nice-to-have, but a technical priority.

Some friction points, like small tap targets or intrusive pop-ups, also often appear only on mobile, so they can be hard to detect.

StoryAI surfaces mobile-specific friction automatically, flagging issues like small tap targets or device-specific scroll problems without requiring manual session review. Use these insights to guide your next development sprint.

Personalization

Personalization can power dynamic content that adapts to user signals, so visitors spend less time searching for what they need and convert more quickly.

For example, your homepage hero can change based on users' location, while CTAs can look different for returning versus first-time visitors.

There are two approaches you could take:

Rule-based personalization, such as "show a 10% off banner to new visitors."
Behavioral personalization, such as "show a boot-focused homepage banner to a user who just browsed three boot product pages."

Behavioral personalization uses more specific signals and aligns content more closely with users' real intentions and needs.

Take recommendation engines: they typically use session behavior and browsing history to surface "Frequently Bought Together" and "Recommended for You" sections, which work better than simply relying on product category rules.

For example, if a customer bought a DSLR camera, a product category rule might pair it with a camera bag, while a behavior-based rule could pair it with previously viewed memory cards and lens cleaning kits.

Tools like Fullstory’s StoryAI automatically surface similar personalization opportunities, so you don't have to review every individual user session yourself or spend hours digging through large volumes of data.

Abandoned cart recovery

When users abandon their carts, it's a good idea to send follow-ups that remind them of what they were about to buy or offer incentives, such as discounts.

Generally, you'll want to:

Send the first message within 60 minutes of abandonment
Send a follow-up within 24 hours
Send an SMS at 48 hours if still unconverted

But good timing is rarely enough. Follow-ups work best when they address the real reason why the user abandoned their cart—perhaps because the shipping costs were shown too late, or because the total with taxes was higher than expected.

The problem is that most analytics tools don't help you understand these reasons. They log all abandoned carts in the same way and trigger the same generic messages, like "you left something behind."

Tailored follow-ups require behavioral data. For example, Fullstory Anywhere captures users' behavior and automatically triggers different workflows for different signals. A user who rage-clicked three times before leaving gets a different recovery message than one who simply closed the tab.

These insights make your recovery messages more effective. Instead of offering a generic discount blast, you can offer just the right incentive or support prompt.

The CRO testing framework

You probably already have some ideas you want to test. However, instead of running CRO experiments at random, we suggest prioritizing them based on three key factors: impact, confidence, and ease.

This is known as the ICE framework, an assessment tool that helps you evaluate if a CRO experiment is truly worth running.

So, gather your ideas and compare them against these factors:

Impact: How big of an impact can CRO actually have here?
Confidence: How certain are you that the experiment will deliver the desired outcome?
Ease: How much effort and resources will this experiment take?

Confidence and impact should always be backed by behavioral data. A test idea supported by 500 rage clicks is a stronger candidate than one based on a stakeholder's design preference.

You can double-check whether you're making strategic decisions or acting on hunches by connecting your hypothesis with behavioral evidence:

"We expect that making the product images clickable will increase purchases because 19% of users attempt to open the images before dropping off."

When you're happy with your hypothesis, you can start running your first tests.

Running and analyzing A/B tests

A/B tests help you compare two variants of the same page, element, or experience against the same KPIs and determine which one works best.

Well-run A/B tests follow a few crucial rules. They:

Test one variable at a time.
Run tests during standard traffic periods.
Run until they hit statistical significance (95%+).

For example, seasonal peaks, like Black Friday, are likely to skew your results, so avoid running experiments during these periods.

StoryAI Summaries can help interpret your results by aggregating insights from the losing variant to surface friction points worth avoiding in future tests.

Scaling the program

One-time CRO programs are typically reactive. Teams conduct them when issues like frequent cart abandonments become painfully obvious. In contrast, ongoing CRO programs identify and address issues as they emerge.

The choice should be clear: CRO experiments should become a regular part of your internal workflows.

However, consistently identifying behavioral signals worth addressing can be difficult to do manually. That's why StoryAI Summaries automatically generates aggregate insights from user interactions and extracts the biggest opportunities for you.

Use it to identify the signals you want to work on, and document your decisions. This will help you avoid retesting the same ideas in the future.

How Fullstory powers ecommerce CRO

Not all tools are suitable for proactive CRO programs. Some don't capture user behavior unless you define what to track upfront, which makes it practically impossible to identify unexpected issues or opportunities.

Fullstory takes a different approach. It indexes every interaction, so teams can investigate friction and uncover insights they didn't even know they needed.

StoryAI Opportunities also surfaces friction and automatically quantifies its revenue impact. You don't have to wait for conversion drops to show up in reports; issues and anomalies are detected and prioritized for you.

With Fullstory Anywhere, these behavioral insights further enrich your stack, including your CRM, data warehouse, and messaging tools. This lets you personalize every touchpoint, from cart recovery workflows to customer support interactions.

Watch the ecommerce product tour →

Downloadable resources

Form abandonment: How to avoid it and increase your conversion rates

Fullstory — Wed, 19 Aug 2026 06:00:00 GMT

Form abandonment happens when someone starts filling out a form and leaves before finishing it. It's one of the most common ways digital friction costs a business money, lowering conversion rates and directly impacting revenue.

Fixing form abandonment starts with understanding why it happens in the first place. This guide breaks down what it is, why it's worth addressing, and how AI-powered behavioral data can help you decrease abandonment and boost conversions.

What is form abandonment?

Form abandonment is when a user begins a form but exits without submitting it. It can happen on any type of form, including lead-generation forms, newsletter signups, checkout flows, and contact forms, and every instance chips away at a business's conversion rate.

Common causes include a technical bug, a confusing layout, or simply too many fields to complete.

Why is addressing form abandonment important?

Form conversions are a key success metric for most businesses. A form might kick off a sales cycle, such as a lead-generation form, or close one, as with an ecommerce payment form. When users abandon those forms, they cost more than a missed submission:

Lost sales
Missed opportunities
Wasted time and marketing spend

That's why they deserve close attention within your conversion funnel, starting with how customers get there in the first place. Visualizing their path with customer journey mapping tools often reveals a customer's intent before they reach your form. That’s a strong signal for whether they'll complete it.

What causes form abandonment?

Form abandonment tied to trust and intent usually comes down to a few specific factors:

Low perceived value: Users don't feel they're getting enough in return for the information they'd have to hand over.
Lack of trust: Visitors who aren't yet familiar with your brand hesitate to commit their information to a form.
Security concerns: Even a short, simple form gets abandoned if users aren't confident their data will be protected.
Low intent: Some visitors are only browsing and never plan to convert. No form change will win them over.
Intrusive fields: A field can feel unnecessary or invasive if it doesn't clearly serve the form’s purpose, like asking for a phone number when only an email is needed.

These same factors show up across other types of digital friction, including cart abandonment. Fixing them sitewide tends to have the biggest impact.

Pro Tip: For the first user interaction, whether it’s a form or another workflow, reducing friction through an onboarding checklist guide will lead to fewer drops.

How to measure form abandonment

Measuring form abandonment starts with tracking how many visitors begin a form versus how many complete it, which gives you a baseline abandonment rate to work from. It's worth checking bounce rate on the same page too, since a form nobody starts is a different problem than a form people quit halfway through

From there, look deeper into where and why users are dropping off:

How long does it take visitors to complete the form?
Which fields cause the most hesitation or errors?
Where in the form does abandonment tend to happen?
What actions do users take after completing or abandoning the form?
Which forms convert better than others, if you have more than one?

If you're using an event-based tool like Mixpanel, unplanned drop-off patterns go unmeasured, which is why teams often look at Mixpanel alternatives built for automatic capture. Fullstory's behavioral data platform captures this kind of detail automatically, so you don't have to piece it together manually. You can capture this evidence by using the best product analytics tools for ecommerce.

Tips for improving form abandonment rate

Reducing form abandonment starts with how you design the form itself. The tips below focus on minimizing friction at the field level, tying directly into conversion rate optimization and reducing broader user friction across your site.

Optimize your form format

Once you've minimized fields, the next priority is making sure the form itself works well for every visitor. A few formatting fixes go a long way toward reducing abandonment:

Accessibility: Make sure fields are responsive and function properly across every device.
Clarity: Cut confusing copy and unnecessary questions, especially for mobile users.
Speed: Optimize image compression and page load time, particularly for multi-page forms.
Incentives: Give users a reason to finish, like a discount or a piece of gated content.
Security: Use SSL, encrypt submission data in transit and storage, and stay compliant with regulations like GDPR and CCPA.
Transparency: Link clearly to your privacy policy so users know how their information will be used.

Install exit-intent CTAs

Exit-intent popups trigger when a visitor is about to leave the page, giving you one more chance to capture a lead before they abandon the form entirely.

Keep the pop-up itself short. Ask for just a name or email address rather than a full form. Offer something specific in exchange, like gated content or a limited-time discount. A generic "submit" button underperforms, so use copy that's more specific to the offer, like "Get my discount."

Test your form with data

Once you've optimized the basics, testing can help validate further changes, but it works best when it's informed by enriched customer data rather than guesswork:

Prioritize with behavioral data. Use it to see which form elements cause the most friction, then test those first.
Use a dedicated testing tool. A/B testing requires a separate platform built for running experiments.
Change one element at a time. This keeps your results reliable and easy to attribute to a single change.
Get team sign-off first. Review the test internally before launching it.

How Fullstory helps you resolve form abandonment issues

Fullstory gives you everything you need to stop guessing why users abandon your forms. StoryAI surfaces abandonment patterns automatically, showing you which fields drive the most drop-off, while Fullcapture records every interaction behind it, no manual tagging required.

If you prefer to dig in yourself, Fullstory's Abandoned Form search flags every drop-off with no instrumentation required. Behavioral data turns the guessing game into a repeatable, evidence-based process that leads to more completed forms over time.

Customer feedback: How to collect, analyze it, and turn insight into action

The Fullstory Team — Wed, 18 Feb 2026 13:00:00 GMT

Teams have more customer feedback data than ever with less clarity about what to fix next. Most teams know what users are complaining about, but they have zero clue why. By connecting direct customer feedback (the what) with behavioral data like session replay (the why), you can stop guessing and start fixing the actual root causes of friction.

Takeaways

Context is king: Vague customer feedback like "this is broken" costs businesses trillions because teams waste weeks guessing at the root cause.
Timing matters: Feedback collected "in the moment" can be more accurate than surveys sent 24 hours later.
Connect the dots: Marrying quantitative data (what happened) with qualitative feedback (what they said) eliminates ambiguity.
Act, don't just track: Use behavioral insights to trigger proactive help, like smart tips or tours right when users struggle.

Customer feedback is everywhere. Actionable customer feedback is not.

Most teams are sitting on a pile of survey scores, support tickets, and “please add this feature” requests,and still can’t answer the only question that matters:

What should we fix next to make the experience measurably better?

That’s the gap between collecting feedback and running a real feedback loop.

Customers don’t just tell you what’s wrong—they show you when they abandon checkout, loop through onboarding, rage click a dead button, or quietly give up and never come back. The problem is those signals rarely live in the same place, so teams debate opinions while friction keeps costing conversions, retention, and trust.

This guide is about building a feedback system that actually works: capture what customers say, connect it to what they do, prioritize with evidence, and close the loop, in-product and out, so improvements aren’t just shipped. They’re felt.

What is customer feedback?

Customer feedback is any insight, reaction, request, or frustration customers share about your product, service, or digital experience. It’s the signal that tells you whether what you’ve built actually works for the people using it.

But here’s what many teams miss: feedback isn’t just the explicit stuff l N comments, support emails, or recorded interviews. It's the implicit signals that customers never consciously communicate:

Explicit input: Survey responses, feature requests submitted in-product, reviews on sites like G2 or Trustpilot, complaints to your customer service team, and written feedback in post-purchase emails.
Implicit signals: Cart abandonment, rage clicks triggered by errors, rising cancellation reasons, time spent struggling on a checkout page, or repeated form submissions that suggest confusion.
Lifecycle coverage: Feedback appears across the entire journey, through discovery, onboarding, day-to-day usage, renewal, and churn.
Concrete examples: A post-purchase CSAT survey after a release. A spike in “app is slow” complaints logged by support. A sudden increase in abandoned carts after a pricing redesign.

When you define customer feedback this broadly, you stop relying on the small percentage of customers who fill out surveys. You start seeing the full picture of how customers actually experience your product.

There's a cost of low-context customer feedback

Here’s the reality: we’re drowning in feedback. NPS scores. CSAT ratings. Social media mentions. Support tickets. Plenty of data but very little clarity.

Low-context feedback isn’t just frustrating, it’s expensive. Globally, a staggering $3.7 trillion in sales is put at risk annually due to bad customer experiences. That’s a lot of money to lose because we didn't understand what a customer meant by "it's broken."

You can usually spot the symptoms inside your own Slack channels:

Vague, unhelpful feedback. Comments like "I don't like it" leave teams paralyzed. It stresses the receiver and derails progress because there is no clear path to a solution.
The "mystery" bug backlog. Engineering fills up with tickets they can’t reproduce. They waste days trying to guess the user's context, only to close the ticket as "cannot replicate."
Generic solutions. When you don't know the root cause, you apply a band-aid. But that doesn't fix the trust you lost. In fact, 32% of customers say they’d walk away from a brand they love after just one bad experience.

Traditional approaches try to close this gap: focus groups, delayed email surveys, post-purchase follow-ups. But asking someone what went wrong days later is like asking what they had for lunch last Tuesday. They might remember the headline, not the details. And detail is everything.

Feedback captured in the moment of experience is significantly more accurate than feedback collected even a day later. If you want the truth, you have to be there when it happens.

Why customer feedback is important

Customer feedback turns assumptions into evidence. It shows whether customers understand your value, where they struggle, and why they stay or leave. Without it, teams build based on what they think customers want instead of what customers actually experience.

And the impact shows up everywhere.

Product decisions become grounded in reality

Feedback exposes friction that metrics alone can’t explain.

Checkout feedback that reveals confusing form fields can translate directly into higher conversion.
Repeated feature requests over time often signal gaps that quietly affect retention.
Strong negative feedback after a release can justify rolling back a change before churn spreads.

Instead of debating opinions, teams can point to evidence.

Customer experience improves in measurable ways

When teams listen early, they fix problems before frustration compounds.

Complaints about slow response times lead to smarter support routing and higher satisfaction.
Sentiment tracking after releases surfaces issues before they become systemic.
Customers who see their feedback reflected in the product are more likely to stay and advocate.

Feedback doesn’t just describe experience, it shapes it.

Churn risk becomes visible sooner

Most churn signals appear long before revenue drops.

Repeated complaints about pricing or policy changes warn of larger retention issues.
Social and community conversations reveal perception shifts before dashboards do.
Support ticket patterns often uncover bugs or UX failures before they scale.

Feedback acts as an early-warning system if teams are listening.

Strategy aligns with real customer needs

The highest-leverage use of feedback isn’t tactical. It’s strategic.

Roadmaps prioritize problems that truly matter.
Positioning reflects real customer language and pain points.
Sales and marketing speak to lived experience, not assumptions.

This is where feedback stops being reactive and starts guiding direction.

How to collect customer feedback data

No single channel captures the full customer experience. Strong feedback programs combine direct input, in-product signals, and behavioral evidence, so patterns become clear instead of anecdotal.

Each method plays a different role:

Guides and surveys provide structured, comparable sentiment.
Interviews uncover depth and nuance.
Support and social channels reveal real-world friction.
Behavioral data shows scale and business impact.

Used together, they turn scattered opinions into actionable insight.

1. In-app surveys

Surveys remain the backbone of most feedback strategies because they generate consistent, trackable data over time, whether through NPS, CSAT, CES, or targeted questions.

What makes surveys effective:

Focus on one goal per survey. Don’t mix satisfaction, feature discovery, and support quality.
Keep surveys short and easy to complete.
Use neutral wording that doesn’t bias responses.
Include at least one open-text question to capture context.
Trigger surveys based on behavior, not arbitrary timing.

Well-timed surveys, like a CSAT prompt after support resolution or an exit survey during checkout abandonment, turn passive sentiment into usable insight.

2. Customer interviews and focus groups

Surveys show what customers feel. Interviews reveal why.

One-on-one conversations and moderated group sessions surface motivations, confusion, and unmet needs that structured surveys miss entirely.

Best used for:

Early discovery before building something new
Understanding churn or failed onboarding
Validating UX changes before launch
Exploring problems you don’t yet know how to measure

Run interviews in small batches and look for patterns, not anecdotes.

3. In-product and in-app customer feedback

The highest-quality feedback happens in the moment of experience, inside your product, not days later in an email.

In-product prompts, micro-surveys, and feedback widgets capture context that traditional surveys lose.

Principles that matter:

Ask at meaningful moments in the journey.
Keep prompts lightweight and unobtrusive.
Pair responses with behavioral context to see what happened before and after.
Be transparent about data collection and privacy.

This is where feedback shifts from retrospective to actionable, because teams can understand and respond while the experience is still unfolding.

4. Customer service and support interactions

Support teams hear problems first. Every ticket, chat, and call is feedback, whether or not it’s labeled that way.

To turn support into insight:

Tag conversations by theme and track trends over time.
Look for recurring issues or rising volume.
Pair CSAT scores with qualitative notes for full context.
Share patterns with product teams continuously, not quarterly.

Handled well, negative feedback becomes one of the fastest paths to meaningful improvement.

5. Social media, community, and online reviews

Public feedback is often the most candid and the most influential.

Customers share frustrations and praise on social platforms, review sites, and community forums long before they submit formal feedback.

Effective monitoring focuses on:

Recurring themes, not isolated comments
Sudden spikes in sentiment that signal emerging issues
Centralizing high-signal feedback for analysis
Thoughtful public responses that demonstrate listening

These channels shape perception as much as they reveal experience.

6. Behavioral and analytics-driven feedback

Customers don’t always explain problems but their behavior does.

Click paths, drop-offs, repeated errors, and stalled journeys expose friction at scale. This passive feedback often reveals issues customers never articulate. You can use product analytics tools for ecommerce to uncover these insights.

To use behavioral insight effectively:

Connect funnel data with qualitative feedback to explain root cause.
Analyze cohort retention and journey breakdowns for hidden patterns.
Use session replay and journey analysis to see real experiences.
Validate whether fixes actually improve outcomes.

Behavior turns feedback from opinion into evidence and evidence into action.

Where actual customer insight comes from

Collecting feedback isn’t the objective here, clarity is.

The most effective teams don’t rely on a single signal. They connect what customers say, what support hears, and what behavior proves until the right next step becomes obvious. That convergence is what turns feedback into forward motion.

Real insight appears when you connect what users say with what they actually do.

A comment like “checkout is broken” is vague on its own. But paired with session replay, the picture sharpens: repeated clicks on “Apply Discount,” an unreadable error flash, then abandonment.

Suddenly, “broken” becomes “the error message timing is wrong.” And that’s actionable.

Combining qualitative feedback (the what) with behavioral evidence (the why) removes ambiguity. You don’t need to guess. You can see the experience, and prioritize fixes that truly move CSAT, conversion, and revenue. Here is a simple, three-step strategy to make this happen.

1. Collect feedback in context

Stop asking for feedback after the user has left the building. You need to capture their sentiment in the moment.

This is where in-app surveys shine. While email surveys often languish with response rates of 15-25%, well-designed in-app surveys can average between 20-30% or higher.

But it’s not just about the numbers. It’s about relevance. This is where tools like Fullstory Guides and Surveys change the game. Instead of blasting every user with a generic "How are we doing?" popup, you can trigger a survey based on actual behavior.

Did a user just rage-click on a button? Did they loop through the same three pages? That is the moment to ask, "Having trouble finding what you need?"

Is it intrusive? That depends. Are you being helpful, or are you just watching? Think of it like a good shop assistant. If they follow you around the store, it’s creepy. If they step in exactly when you look confused at a size chart, it’s a service. Timely, relevant help builds trust.

2. Analyze feedback with behavioral data

A single piece of feedback is an anecdote. Ten pieces is a pattern.

You need to analyze patterns to understand the scale of a problem. Fullstory acts as a force multiplier here. It allows you to connect the dots between that one angry comment and your broader user base.

When a user tells you they struggled with a feature, you can use that insight to segment your users. How many others encountered that same error? How much revenue was impacted? Which specific browser is affected?

This turns a support ticket into a business case. You move from "I think we should fix this" to "This issue is costing us $50,000 a month." That is how you get actionable customer feedback that leadership listens to. While customer feedback tells you what users say, pairing it with behavioral-stage ecommerce CRO tools allows you to see what they actually do on your site.

3. Close the feedback loop with proactive guidance

The ultimate goal isn't just to fix bugs faster. It's to stop them from bothering your users in the first place.

Why wait for a complaint? If you know users are dropping off at a specific step, you can intervene. A platform that combines behavioral data with in-app surveys allows you to close the feedback loop completely.

You can deploy a "smart tip" to explain a complex setting. You can launch a "tour" to guide users through a new workflow. You can show a "banner" about a known issue so they don't waste time trying to fix it themselves.

This is proactive customer support. It solves the problem before the user even thinks about typing a negative review.

The shift worth making

The future of customer feedback strategy isn't about collecting more data. We have enough of that. It’s about making the feedback you already have actionable. That is how you build products that people actually love to use.

Stop looking backward at what went wrong. Start looking at what’s happening right now, and guide your users to where they want to go. The companies that win aren’t the ones collecting the most feedback. They’re the ones who understand it fast enough to change the experience while the customer is still there.

Ready to stop guessing? Get a demo to see how you can turn behavioral insights into in-app action. Or try Fullstory for free and start understanding the "why" behind your customer feedback.

User sessions: Definitions and how to track and analyze them

The Fullstory Team — Thu, 17 Aug 2023 04:00:00 GMT

While checking metrics like conversion rate and bounce rate can be beneficial, you can and should also track and analyze user sessions, critical metrics that can tell you a lot about how visitors interact with your brand.

For example, when you analyze those sessions in a tool like Fullstory, you have the power to fully understand your target audience members and anticipate the needs of your site visitors.

But what exactly are user sessions, and how do you analyze them? Let us break down the answers to these questions.

What is a user session?

In simple terms, a user session is a series of "user actions" performed by an individual on your website or web application within a specific timeframe. It can span multiple pages on your website, but there are certain limiting factors to consider:

The user stays on your website
The user session lasts only for a certain amount of time (the standard is 30 minutes)

User sessions are also sometimes called "journeys," "clickpaths," and "visits." Think of a user session as all the things a visitor did and all the pages a visitor went to in a single visit to your website. User sessions can include:

Clicks
Scrolling actions
Page views
Service requests
Downloads
Logins
Social interactions
Other user interactions

For example: A visitor checks out your store website for the first time after finding it on Google. They visit a few product pages, then use their browser to navigate away. That's a user session.

User sessions work similarly for mobile apps. A user may open your proprietary or branded app. They scroll through a few pages, place an order, then close the app once they finish their business.

It’s just as important to know what a user session is not. It’s not:

A purchase or any other conversion (e.g., a subscription)
A download
A sign-up

Typically, website analytics or product analytics tools will assign a session ID to a single session. Sessions are tracked using browser cookies.

Further reading: The best website analytics tools on the market today

How do user sessions differ from users?

Users are the agents that have user sessions. A user is either:

A website visitor – someone who visits your website by clicking on it
An app user – someone who downloads and opens your application

What is number of sessions per user?

“Number of sessions per user” is an ancillary metric you should track alongside user sessions. It’s the average number of sessions initiated by each visitor or user for your website or application. Think of it as a description of how often the average user visits your website or opens your app.

Why track user sessions?

There are lots of great reasons why you should track user sessions. For example:

You can see where the average user starts their journey on your website, plus where they leave or “bounce”
You can see how long the average user visits your website before leaving or making a purchase

Both of these pieces of information may provide key insights into your target audience members’ behavior, needs, and connection with your brand.

For example, say that you analyze user sessions and find that 99% of visitors arrive at your landing page. However, 75% of those visitors leave after visiting the store part of your website. That can tell you a few potential things, like:

Your store needs a revamp in terms of navigability
Your site loads too slowly
People see the prices of your products and leave
Something else, depending on the specifics of your site

Bottom line: you should track user sessions to better understand your target audience members. With these insights, you can market to them more effectively and implement smarter conversion rate optimization strategies to boost both conversion rates and profits. You can also improve the user onboarding flow to reduce any initial friction.

Time-based expiration

You can track user sessions with two different session expiration formats, the first of which is time-based expiration — or, more simply, session duration.

In simple terms, time-based expiration means a session continues until a visitor remains inactive for a specific period, known as a session timeout. On Fullstory, the expiration time for a session is set at 30 minutes.

Imagine a user visits your brand’s website for the first time. They visit a few product pages, then stop navigating around between pages. After 30 minutes of inactivity — defined as no clicking, scrolling, etc. — the existing session is “over” since it’s likely the visitor stepped away from the computer or otherwise stopped interacting with your site.

Then, the next time they visit, it will reset and default as a new session. This session will count as an entirely separate, new user session.

Campaign-based expiration

Campaign-based expiration means a session stops whenever a user’s campaign source changes. The “campaign source” is the origin point of the visitor, such as a Google ad, a social media ad, a Google search, etc.

For instance, you have a visitor who visits your site via a keyword search on Google. Then they open your site again with another keyword. Both times the user opens the site count as separate sessions, even though they take place within five minutes of each other.

Campaign-based expiration can tell you a lot about visitor traffic sources.

What defines a user session in Fullstory?

In Fullstory, a session starts when a user visits your site. The session ends after 30 minutes of inactivity. Alternatively, a session ends after 24 hours of active session capture in the event that a visitor continues using your site for a full day!

How are user sessions related to session replay?

All this information is vital to understand and fully leverage session replay on Fullstory. Fullstory allows you to reproduce a user’s interactions on your website or web app exactly as they performed those interactions, including:

Where they clicked, and in what order
Specific mouse movements
How the user scrolled through a page or app
And much more

In these ways, session replay allows you to truly put yourself in the shoes of site visitors and users like never before. With session replay and Fullstory’s analytics suite, you’ll be well-equipped to understand how your target audience members interact with your site and what you can do to improve their experiences across the board.

User onboarding checklist: What to include (+ template)

Fullstory — Fri, 02 Oct 2026 07:00:00 GMT

A comprehensive user onboarding checklist can still leave new users stuck, if you don't cover the right steps.

A user onboarding checklist should guide new users to their activation milestone, and the best ones adapt as you learn where and why users actually struggle.

This guide covers what to include, what to cut, examples by product type, a ready-to-adapt template, and how to build one from real user behavior instead of guesswork.

What is a user onboarding checklist?

A user onboarding checklist is a short, in-app list of three to five actionable tasks that guide new users from sign-up to their activation milestone. A good checklist tracks what’s done and disappears when finished, driving users straight to value without friction or confusion.

The most important item is the one tied to that initial milestone: the specific action that decides if users stay or drift off. For a project tool, it could be assigning a task; for an analytics platform, it might be installing the tracking snippet. Everything else on the list exists to enable that first breakthrough.

Onboarding checklists that hit the mark link every item directly to an action that proves your product’s value.

But even the best steps won’t work if you fail to keep the users hooked. That’s where a visual progress indicator comes in. When you show users exactly where they are in the process, they feel less anxious and more motivated to move forward. These visual cues drive:

Momentum: Checking off the first task gives users a sense of satisfaction and the confidence to keep going. Even better, if the first task is already marked complete before the user starts, the destination looks closer. A consumer-based study backs this up, finding that 34% of people who received an artificial boost completed tasks, versus 19% who didn’t.
Clarity: A finite list of three to five steps prevents overwhelm by showing exactly what they need to do next and avoids annoying surprises later.
Completion: The progress bar triggers a natural desire to see through what they’ve started, encouraging them to sprint toward the finish line.

What a user onboarding checklist is not

It’s not a generic setup list that every account sees regardless of role or specific reason for signing up.

A static list treats a solo freelancer the same way as an enterprise admin. Both expect different flows. One needs to publish something before lunch. The other needs to provision seats and connect Single Sign-On (SSO) before anyone can do anything.

Every irrelevant task you include frustrates users and actively delays their time-to-value. Users don’t stick around to filter out the noise; they simply leave.

What to include in a user onboarding checklist

Momentum is great, but it only matters if you point them at the right tasks. Before you list actions, your customer onboarding checklist needs a solid structure. No matter what steps you choose, every effective list includes:

A personalized welcome header
A visual progress indicator (e.g., a progress bar or step count)
Action-oriented task titles
The estimated time to complete
Contextual help links (or embedded tooltips)
A dismiss or skip button
A final success state (or completion reward)

Next, outline the key tasks that lead to activation. Start from the milestone and work backward. Ask what a user must have in place before the core action becomes possible, then include only those things.

When mapping out your sequence, pull from these key categories:

Account and profile setup

Steps like adding a profile photo upfront aren’t necessary. Connecting a data source, generating an API key, inviting a teammate, or naming a workspace are. Stick to the absolute minimum actions required for the product to function.

If a task doesn’t block that first milestone, cut it. When you keep the process concise, users reach value faster.

The core activation action

This is the moment users experience their first win (or not) and decide your product’s worth. Make sure they achieve it effortlessly. Major payoffs like rapid script installations or smooth data imports justify why they signed up.

Just remember that cluttering the path with text-heavy tooltips or mandatory tours is the fastest way to spike early churn and revenue loss.

Feature-discovery tasks

Show one or two of your platform’s deeper capabilities (e.g., configuring automated alerts, setting up custom dashboards) that users might miss. Pick the ones that correlate with retention, not the ones your team is proudest of. These “sticky” features turn casual sign-ups into long-term power users.

Keep it restrained, though; dumping every advanced tool onto this initial list will quickly overwhelm beginners.

Segment-specific steps

Design tasks based on the user’s specific role or goal. A developer generating API keys shouldn’t wade through the same steps as a marketing lead building a dashboard. Customizing this flow proves your product understands their daily reality.

If you force-feed everyone the same onboarding tasks, the platform will feel disconnected and irrelevant right from the start.

An optional feedback checkpoint

Add a quick one-click rating or micro-survey when the user completes the list to gauge sentiment and identify hidden friction in the product’s onboarding. This gives you instant visibility into where new users get stuck.

Don’t force it, and keep it short. Few users appreciate a mandatory, multi-question form right after they’ve used a product once.

What not to include in the checklist

If you fill users’ screens with difficult or low-value tasks, their initial excitement of trying a new product fades quickly. Deciding what to leave off is just as important as what to keep.

Avoid these recurring failure modes that add to the cost of digital friction and drive users away:

High-friction tasks: Even a short list can cause churn if the tasks themselves are painfully hard. Asking a new user to map complex schemas or pull off an integration before a smaller win kills momentum. Reserve your initial checklist strictly for helping users reach their activation moment effortlessly.
No clear tie to value: If every step is pure configuration that doesn’t produce a tangible result, the bounce rate can surge. Cut steps that only serve your internal CRM (like asking for company size) and ensure every item leads users to their primary goal.
Rigid, unskippable sequences: Don’t treat your checklist as an automated assembly line, where steps must follow a fixed order. When you force users through steps, a single missing piece of information (like an API credential or brand asset) halts the entire process. Ensure the checklist is flexible enough to let users jump around and complete what they can.
Including email-friendly information: Nurture sequences and webinar invitations live outside the product. If you’re working from a customer onboarding email template, treat it as a parallel track to avoid congesting the in-app list.

User onboarding checklist examples

Here are three onboarding checklist examples organized by product type:

1. The network-effect list (collaboration tools)

Activation milestone: a second person takes an action in the workspace

Since the core product value lies in a multi-player environment, solo users can’t experience its full potential. Instead of forcing users to configure settings, the checklist makes “bringing a colleague into the workspace” the first key milestone.

Every pre-invite task must make the request worth sending, and everything after should keep the second person engaged once they accept.

For example, in a project management app, a pre-invite task like “Create your first board” ensures the workspace isn’t empty when the guest arrives. Similarly, a post-invite task like “Assign a ticket” lets the new user start working instead of staring at a blank screen.

A sample checklist for a project management software:

Create your first project (pre-completed if they named a workspace at signup).
Add three tasks.
Invite a teammate.
Assign a task to them.
Turn on notifications.

If you bury the invite at position five, few users will ever reach it.

2. The staged threshold (fintech and compliance apps)

Activation milestone: first verified identity or successful transaction

These products carry unavoidable friction, like document uploads and waiting periods. The checklist earns its place by making the wait legible.

By breaking a massive goal into smaller, phased targets, you give users a taste of achievement before the heavier tasks arrive. For instance, you could split an intimidating “account approval” into “verify your email” and “upload your ID”.

A sample checklist for a wealth management platform:

Verify your identity.
Link a funding source.
Review your account limits.
Make your first transfer.

To make the entire sequence feel doable, tell people what’s happening between steps. A warning like “Verification usually takes two hours” avoids the refresh-and-panic loop that generates needless support tickets.

3. The opt-in launcher (developer & flexible tools)

Activation milestone: first successful API call or data flowing into the dashboard

When a product is highly flexible, the list should be, too. A rigid list alienates users whose use case you didn’t anticipate. Instead of a fixed script, create a checklist with optional, self-serve launchers that trigger contextual actions based on what the user actually came to build.

A sample checklist for an event tracking API:

Generate an API key.
Install the SDK.
Send a test event.
See data appear in your dashboard.

Steps three (the work) and four (the payoff) are deliberately separate. If you collapse them into one item, you erase the exact moment when your product proves itself.

User onboarding checklist template

Below are two templates you can adapt today.

Structure matters more than wording. Both templates follow the same shape: Name the activation milestone first, then list only the steps standing between a new user and that outcome.

Template A: Team-based B2B product

Activation milestone: [The first action that predicts retention in your data, e.g., creating and sharing a custom report]

Step 1: The already completed task (e.g., confirm your email – Completed)
Step 2: The single blocking configuration (e.g., connect a data source)
Step 3: The core action in the user’s words—the language users would actually use when doing this task, rather than using proprietary feature names (e.g., build your first report)
Step 4: The feature most strongly correlated with month-two retention (e.g., add a filter to narrow results)
Step 5: The network-effect trigger (e.g., share the report with a teammate)

Template B: Self-serve solo product

Activation milestone: [The first output that the user would be annoyed to lose, e.g., exporting a finished graphic]

Step 1: The pre-checked step from signup (e.g., create your account – Already done)
Step 2: The data or asset import (e.g., import your brand assets)
Step 3: The core action in the user’s words (e.g., draft your first graphic)
Step 4: The value-preservation step (e.g., export the final file)

How to build a user onboarding checklist from real user behavior

A template gets you to a first draft. Behavioral data gets you to the one that actually works. Here’s how to build your sequence based on how real users use your product (or drop it early):

1. Find your real activation milestone

Most teams choose their activation milestone in a meeting, based on assumptions. Instead, dig into your product’s usage data to quickly find a more accurate one. Take the users who were still active in month two, then work backward: What did they do in their first week that the churned cohort didn’t?

Traditional website analytics rarely answer this question. They count visits rather than linking early actions to retention. More advanced product analytics platforms go deeper. They allow you to compare segments (retained vs. churned users) to see which early action separates them.

The output of this step must be a single, measurable sentence: "Users who [specific action] within [timeframe] retain at [rate]." If you can’t finish that sentence with an actual number, you aren’t ready to write the checklist yet.

2. Find where users actually get stuck

Next, watch what goes wrong along the way. This is where quantitative and qualitative analysis differ: The funnel tells you which step leaks, but session replays show why.

Filter for frustration signals like rage clicks, dead clicks, and thrashed cursors on your new-user segment and onboarding pages. Patterns will emerge:

A cluster of dead clicks on a seemingly interactive element
Clicks concentrated on the integration step
Form abandonment on a field asking for information not available immediately

Ten session replays of new users failing at step two will settle more internal debate than a month of opinions. This behavioral data also produces better checklist copy, because you hear the friction in users’ terms rather than your own.

3. Write and sequence the checklist items

With the behavioral evidence in front of you, apply these three rules when you draft your list:

Order by dependency, not importance: If a user can’t send a test event before installing the SDK, the sequence is clear.
Put the core action in the middle: Don’t leave the heaviest lift for the end. Let early momentum carry users through critical tasks.
Segment by role or plan: Admins' initial concerns are configuration and access; end users care about creating something. The same steps can’t serve both. They only bloat the product and push users away.

4. Implement it without engineering

Building a checklist manually eats too much of your engineers’ time. To solve this, use a no-code editor with live previews, like Fullstory Guides and Surveys, to create one that matches your app’s native branding and styling.

Then, focus on how you show task completion. Signal it with a page event on a specific element or a URL the user lands on after finishing, so your reporting measures actual progress. Next, apply precise triggering logic: Show the checklist immediately after signup, hide it by plan segment, or launch it after a tour completes.

(Note: The Guides and Surveys module is currently available for web applications. For mobile, Fullstory captures the behavioral data, but the checklist requires a native delivery path.)

5. Measure and iterate

The most important metrics are completion rate, activation rate, and time to value.

Pay attention to how many people complete the checklist versus how many actually reach activation. High completion rates without actual activation indicate checkbox ticking. The reverse means your checklist is mostly ornamental. Optimizing conversion rates requires both to drive meaningful outcomes.

Then pair the numbers directly with Fullstory’s behavioral context to see why users do what they do throughout their onboarding experience. If an item’s drop-off spikes, you can instantly pull up the session replays to find the exact friction point and adjust the checklist accordingly.

This is the loop most teams skip. The checklist isn’t a one-off project; it’s reporting you keep returning to. Setup steps that move with feature updates or tier changes make a real difference.

User onboarding checklist best practices

Most user onboarding best practices come down to ruthless restraint. Whether you’re building a collaborative workspace or a complex developer tool, these rules hold up across product types:

Cap the list at five items. Attention is the scarce resource, not screen space. If you have a sixth item, one of them is unnecessary.
Tie every item to value, not just setup. If a step doesn’t actually move your user closer to the milestone, cut it and see whether anyone notices.
Show progress and start above zero. A visible progress bar with one item already checked (e.g., Account created) gives users an immediate psychological head start.
Personalize by segment. Role, plan, and signup source provide enough context to produce meaningfully different flows. Avoid generic lists that irritate users with steps unrelated to what they need.
Let users dismiss it. If users can’t close the checklist, they'll see it as a snag rather than guidance. Allow users to minimize it, but keep a launcher available so they can reopen it when needed.
Name items in the user’s words. Pull your task phrasing directly from support tickets and customer feedback rather than using your internal, company-centric feature names.
Define completion precisely. Decide up front whether an item completes on a click or on an actual event. The first option inflates your numbers without proving value.
Don’t stack it against a full tour. Running a checklist alongside a mandatory, multi-step product tour splits attention and buries both.
Explain the waits. When a step depends on data processing or backend approval, state clearly how long it takes or provide a realistic estimate. Silence reads as failure.
Iterate using fresh data. Products keep evolving. A checklist written 18 months ago describes a product that no longer exists. Revise your logic every quarter to reflect what’s new.

Effectively guide your users with Fullstory

A checklist built once and left alone quickly drifts out of sync with what users actually need. Not because the original thinking was wrong, but because the product moved, the audience shifted, and nobody noticed.

That’s why you need both the analysis and intervention in one place. Checklists in Fullstory’s Guides and Surveys are built directly on behavioral data. The same platform that reveals where users get stuck by capturing quantitative and qualitative friction data is the one you use to build and adjust the checklist itself.

You eliminate the cost of maintaining a standalone no-code widget bolted onto a separate analytics tool. That means you no longer need to waste effort reconciling disconnected segments and hoping they agree.

The outcome is clear: less guessing about what belongs on the list, and faster iteration when a step stops converting. The unified platform lets you spot a problem and immediately ship the fix without the burden of engineering tickets.

Best product analytics tools for ecommerce shopper journeys

Fullstory — Tue, 29 Sep 2026 07:00:00 GMT

Most generic reporting tools show revenue leaks between cart creation and order confirmation, but few can pin down why. In fact, according to Fullstory’s Cost of Digital Friction Report, 65% of teams drain resources fixing the wrong friction problem.

Product analytics tools for ecommerce solve this. On top of logging traffic, they capture deep behavioral data that connects discrete shopper actions (like comparing three products before dropping off) directly to business outcomes.

However, these tools vary widely in their approach. Some rely on predefined event-based tracking, like GA4 and Mixpanel, while others capture full behavioral context, like Fullstory and Contentsquare.

Below, you’ll find how winning teams use these tools, the best options on the market, and a practical guide to pick the right one for your stack.

What are product analytics tools used for in ecommerce?

Ecommerce product analytics tools track and analyze how users move through your site or app. They help you analyze entire user journeys—from initial product discovery to purchase confirmation—and tie those actions directly to revenue.

Adding a behavioral layer to your product analytics software stack is now table stakes. Modern shoppers expect frictionless, personalized experiences; using real behavior to prioritize improvements can help teams focus on changes most likely to improve conversion and retention.

In the list below, you’ll see how each platform handles this root-cause analysis. While some stop at counting events, others go deeper to uncover behavioral insights that drive effective product decisions.

Best product analytics tools for ecommerce overview

Let’s get started with a summary of the best product analytics tools for ecommerce.

Tool	Best for	Ecommerce fit
Fullstory	Full behavioral capture across user journey	Fullcapture records the full journey; StoryAI surfaces friction anywhere it occurs, including checkout
Amplitude	Retention, cohorting, product-led growth	Strong on cohort/retention framing; dedicated ecommerce comparison content of its own
Contentsquare	Enterprise digital experience analytics	Zone heatmaps and journey analysis built for retail/ecommerce
Mixpanel	Event-based tracking, cart-level analysis	Cart Analysis feature (2025–2026) adds ecommerce-specific depth
Google Analytics 4	Free baseline ecommerce reporting	Native ecommerce event schema (add_to_cart, begin_checkout, purchase), no "why" layer
Quantum Metric	Automated friction and UX issue detection	Closest positioning match to Fullstory on this list
PostHog	Developer-first, open-source data ownership	Self-hosted control; not ecommerce-specific out of the box

1. Fullstory

Best for: Full behavioral capture across the ecommerce shopper journey.

One of the market leaders, Fullstory is a comprehensive behavioral data platform that completely redefines how product teams approach behavioral data.

What sets it apart is its patented Fullcapture technology. Built for modern stacks, it automatically indexes every user interaction—from product views, searches, and filters to cart actions and checkout steps. You can also analyze past behavior retroactively without waiting for new tags.

On top of that data sits StoryAI, which automatically extracts actionable insights from behavioral logs. Here’s how it acts as an intelligent assistant to fine-tune your products faster:

Proactively identifies and scores friction signals like dead clicks, page errors, form abandonment end-to-end.
Summarizes entire sessions into context-rich insights so you don’t have to sift through every session manually.
Investigates funnel leaks and suggests relevant fixes.
Reveals personalization opportunities to deliver superior user experiences.

Fullstory’s DOM-based (Document Object Model) session replay is more secure than traditional recording tools that make it hard to mask sensitive data. By dynamically reconstructing the page into lightweight text packets that avoid PII capture, you get sharp, retroactively queryable session analysis that supports compliance with privacy laws like GDPR and CCPA.

The only notable gap is that Fullstory doesn’t ship with built-in A/B testing, but you can run experiments through integrations with dedicated testing platforms.

Verdict: Fullstory complements standard reporting software with deep behavioral analysis of customer interactions for fast-growing and enterprise ecommerce teams.

Pros

Eliminates the need for a resource-heavy, pre-planned event taxonomy across shopper journeys.
Instantly populates historical replays for newly defined events and metrics.
StoryAI automatically surfaces pain points anywhere in the user journey (including checkout) and helps you prioritize high-impact conversion issues.
Unifies quantitative (funnel and dashboard data) and qualitative (user behavior and open-ended feedback) analysis in one platform.
Protects sensitive data with a Privacy-by-Default architecture and ISO 42001-certified AI governance, supporting data security and regulatory compliance.

Cons

Lacks native A/B testing tools, but integrates with popular experimentation platforms to run data-driven tests.
Unlike some tools with self-serve pricing, Fullstory requires a demo or a quote request for customized pricing.

Pricing

Fullstory offers a free plan that includes 30K monthly sessions and 12 months of analytics data retention. For larger volumes and advanced analytics features, you can choose one of three paid plans:

Business: Built for fast-growing teams that need deep product analytics and behavioral intelligence. Includes funnel analysis, sentiment signals, privacy compliance, and more.
Advanced: Designed for businesses that must discover patterns and apply optimizations at scale. Adds retention analysis, deep journey visualization, and conversion analysis.
Enterprise: Built for data teams that want to build predictive models or feed behavioral data directly into in-house AI pipelines. Adds enterprise-grade security features like RBAC and SSO.
Add-ons: Mobile analytics, Guides and Surveys, and StoryAI Premium.

Review

“The session replay quality and filtering capabilities are excellent. Being able to watch actual customer behavior at scale rather than relying on aggregated metrics has fundamentally changed how we approach UX research. The search functionality lets us quickly isolate specific user segments or problematic journeys, which is invaluable for both understanding friction points and reproducing bugs that customers report.”
- Lee A., Head of Product Management (Review on G2)

2. Amplitude

Best for: Retention and cohort analysis

Amplitude is an event-based product analytics platform that runs its own ecommerce-focused comparison pages. If you rely on predefined events (standard milestones, specific feature clicks) to analyze product performance, Amplitude is probably already on your radar.

Known for retention analysis and behavioral cohorting, Amplitude lets teams instrument key actions, then build cohorts to see which behaviors correlate with higher conversions. Beyond core analytics, Amplitude’s expanded suite now offers feature flags for rolling out segment updates and experimentation tools to run A/B tests.

Where teams struggle most is pricing. Amplitude charges by Monthly Tracked Users (MTUs), with event caps tied to that volume. When traffic spikes suddenly (e.g., during seasonal campaigns or flash sales), costs can scale fast. As users multiply and your team exceeds usage limits, so does your bill, often faster than expected.

Verdict: Amplitude's ideal users are product managers and growth teams at mid- to large-sized brands with dedicated instrumentation resources.

Pros

Offers near real-time event streaming with strong retention and cohort analysis.
Features an intuitive interface for non-technical teams (design, product) to build custom visualizations independently of engineering support.
Includes additional features, such as feature flags and experimentation, in its expanded suite.

Cons

Depends on predefined event classification; new questions often require re-instrumentation or waiting for new data.
Requires careful instrumentation to set up proper event taxonomy, tracking plans, and user properties.
Can get expensive at scale due to MTU-based pricing, especially for high-traffic ecommerce sites.
Overall spend can increase as teams adopt add-on features like experimentation.

Pricing

Amplitude offers a free Starter plan with usage capped at 2M events/month. Paid plans are tiered based on event volume:

Plus: Includes 2M events free/month and scales up to 70M events; offers features like heatmaps, alerts, and 2-year data retention.
Growth: Custom pricing suitable for scaling businesses; includes advanced behavioral analysis, 20K monthly session replays, and more.
Enterprise: Custom pricing model designed for larger enterprises; offers advanced features, including 50K monthly session replays, security controls, and predictive analytics.

Review

“I like Amplitude Analytics' pretty nice UI, which makes it easier to view user sessions and identify points of friction in UI interactions as well as drop-off points in funnels. I also appreciate the communication it facilitates between engineering and product teams through features like shared dashboards and events.
It's easy for engineers to come in and see the events they've created reported in the data tab, and add documentation to help explain when they are triggered. Having one centralized place for things like events and properties is particularly helpful.”
- Anton N. (Review on G2)

3. Contentsquare

Best for: Enterprise-scale experience analytics

Contentsquare is an enterprise-grade digital experience platform built specifically for retail and ecommerce. Dedicated product analytics and UX teams can make data-driven optimization decisions at scale, with journey analysis, zone-based heatmaps, and AI-enabled natural language queries.

Over the years, Contentsquare has expanded its capabilities by acquiring tools like Heap, Hotjar, and ClickTale. For example, Heap’s autocapture and retroactive analysis allow teams to turn journey-wide behavior data into actionable insights.

However, integrating multiple tools can risk data fragmentation and privacy compliance issues, unlike unified platforms in this product analytics tools comparison.

Verdict: Contentsquare fits well into brands that prioritize visual UX mapping and have the bandwidth to manage a complex stack.

Pros

Provides enterprise-level digital experience analytics for high-volume, multi-site retail environments.
Goes beyond click counting with zone-based heatmaps and in-depth journey analysis that support informed product improvements.
Offers AI-driven session summaries and issue detection, helping teams identify friction and apply fixes faster.

Cons

Smaller teams and startups may find the enterprise pricing significantly prohibitive.
Requires dedicated team members to implement and manage the complex, multi-tool environment.
Currently lacks a single source of truth (due to ongoing platform-consolidation work), adding complexity for teams analyzing data across products.

Pricing

Contentsquare offers a free Product Analytics plan that allows 10K monthly sessions and 6-month data retention. You can also choose one of the paid plans for larger session volumes:

Growth: Custom pricing; starts at 17K monthly sessions; adds AI capabilities, customizable charts, and 12-month data retention.
Pro: Custom pricing; includes 33K+ monthly sessions; adds account analytics, an engagement matrix, and optional add-ons.
Enterprise: Custom pricing; 1M+ monthly sessions; adds behavioral targeting for personalization, unlimited projects, and advanced user permissions.

Review

“I appreciate Contentsquare for its granular visual analytics, like zone-based heatmaps and customer journey analysis, which provide deep visibility into user navigation on complex funnel webpages. Contentsquare's intuitive interface makes it easy to make data-driven decisions quickly, improving our UX and conversion rate optimization (CRO).”
- Tiberiu A., Digital Expert (Review on G2)

4. Mixpanel

Best for: Event tracking and cart-level analysis

One reason Mixpanel is on this list of the best product analytics tools for ecommerce is its Cart Analysis functionality (rolled out through 2025-2026).

Ecommerce teams can inspect cart contents at any point in the journey, down to item category, brand, and price. This means you can tie specific product or price points to drop-offs and recovery opportunities.

For revenue analysis, warehouse connectors sync data from Snowflake, BigQuery, Databricks, and Redshift.

However, like other event-focused platforms, Mixpanel requires manual instrumentation, with limited ability to autocapture common interactions. To track specific actions, you must define events in advance.

Verdict: Mixpanel works best for technically mature growth teams willing to invest in upfront tracking effort in exchange for granular revenue and product-level insights.

Pros

Cart Analysis identifies the specific item and price categories that drive users to abandon carts or complete purchases.
Strong event-based segmentation supports activation, retention, and lifecycle flows.
Custom dashboards with real-time insights help teams spot and resolve issues quickly.

Cons

Requires explicit event tagging in advance to track user interactions.
Needs dedicated technical experts and a concrete roadmap to set up event tracking correctly.
Pricing can jump steeply once your usage volumes across events and replays grow past free limits.

Pricing

With Mixpanel, you can either choose the free plan (up to 1M events/month, 10K replays/month, 1K experimentation testers/month) or go for one of the two paid tiers:

Growth: First 1M events free every month, then usage-based pricing; supports up to 20M events/month, 500K replays/month, and 100K experiment users/month.
Enterprise: Custom pricing for large deployments. Includes 1T events/month, custom volumes for replays and experimentation, advanced features like unlimited error detection and priority support.

Note: Mixpanel can also be one of the best product analytics tools for startups. The reason: its pricing page promises a first-year-free Startup plan for early-stage companies that meet eligibility criteria.

Review

“I think the live demos in Mixpanel are incredibly valuable. Being able to see exactly where users click and what they don't click is super helpful. I really like how Mixpanel obfuscates much of the information so it doesn't reveal any sensitive data.
I also appreciate having a full, holistic, end-to-end understanding of how our users use each system. It's also easy to create new environments in Mixpanel, so we can run different products in different environments, such as development and production. This allows us to test features and ensure observability before releasing them.”
- Aaron K., VP of Product Management (Review on G2)

5. Google Analytics 4 (GA4)

Best for: Free baseline ecommerce reporting

GA4 is a popular, free website analytics platform that helps you track basic ecommerce metrics such as traffic, engagement, and conversions across digital properties.

One strength of GA4 is its native ecommerce event schema (a set of predefined event names). With this baseline feature, your team can track key shopping behaviors without building everything from scratch.

Examples of built-in ecommerce events in GA4 include view_item (fired when a user opens a product page) and begin_checkout (fired when a user starts checkout).

Although GA4 is great at quantifying events, it leaves a blind spot for qualitative user experience data. In-depth behavioral analytics (session replay, friction detection, granular journey analysis) are currently missing from the GA4 suite.

Verdict: Many teams use GA4 to monitor high-level acquisition trends, and pair it with a qualitative behavioral tool like Fullstory when they need deeper answers.

Pros

Free to use and integrates deeply with Google Ads and BigQuery.
Offers native ecommerce event-based tracking out of the box.
Adequate for high-level funnel tracking and revenue reporting at no additional cost.

Cons

Relies heavily on data sampling and modeled estimates for complex queries and high-volume traffic, which can reduce precision.
Limits the ability to monitor live user behavior due to processing latency of 24 to 48 hours for standard properties.
The paid version (GA4 360) can cost six figures annually—relatively more expensive than some specialized analytics platforms.

Pricing

GA4 (standard): Offers free access, with limits on data retention, event volumes, and data exports. Includes basic cross-platform analytics, event-based tracking, and user segmentation.
GA4 360: Custom-priced enterprise tier for teams with more advanced needs. Includes higher caps for data processing, retention, and exports, plus enhanced technical support and SLAs.

Review

“Overall, Google Analytics has been a dependable part of how we track performance at CargoX. The interface takes a bit of getting used to at first given how much data it surfaces, but once you're familiar with it, navigating between reports becomes second nature. It integrates well with the rest of our marketing and web tools, pulling data together without much friction.
“We're using the free version, which offers strong value for a startup since we're not paying anything for the level of insight we get.”
- Muhammed A., Technical Product Manager (Review on G2)

6. Quantum Metric

Best for: Automated friction and UX issue detection

Quantum Metric is the closest match to Fullstory on this list. Both deliver deep behavioral analytics, prioritizing automated friction detection over manual funnel analysis.

Instead of waiting for technical teams to predefine events, Quantum Metric continuously captures frustration signals, such as repetitive scrolling, forced reloads, and app crashes. The AI layer then automatically suggests solutions and ranks them by business impact, allowing teams to prioritize and resolve issues faster.

Although this platform delivers large-scale behavioral monitoring and revenue impact quantification, the enterprise-inclined sales model and complex implementation lift may scare off teams that seek budget-friendly pricing and agility.

This is where Quantum Metric deviates from Fullstory’s value propositions: fast, plug-and-play snippet deployment, zero ongoing tag maintenance, and pricing plans for businesses big and small.

Verdict: Enterprise-grade pricing and dense initial setup make Quantum Metric best suited to large retail/ecommerce organizations with dedicated analytics teams familiar with complex event instrumentation.

Pros

Automated discovery of friction and UX issues at scale, and AI-driven anomaly detection with revenue impact scoring.
Highly suitable for complex, high-traffic retail environments across web, mobile, and other digital channels.

Cons

Offers only a custom-quoted, enterprise pricing model; lacks self-serve or SMB-friendly tiers for budget-constrained teams.
Requires substantial resources upfront to deploy the complex tracking environment, unlike lighter, ready-to-use analytics tools.
Some users report a dense interface that makes it harder to find specific sessions than more intuitive tools.

Pricing

Quantum Metric’s pricing page emphasizes “enterprise plans built around your business.” This means you can get custom quotes after discussing your requirements with sales.

Costs scale with your needs and depend on factors such as the number of digital properties, session volume, and required support.

Review

“Quantum Metric truly allows us to identify what went wrong, rather than just guessing based on logs. It has also been extremely useful in uncovering UX issues and making our analytics more statistically meaningful. Thanks to Quantum Metric, we have been able to better prioritize improvements to our UX and product overall.”
- Arpan K., Quality Engineer (Review on G2)

7. PostHog

Best for: Developer-first, open-source data ownership

PostHog is a developer-first, open-source product analytics platform that you can run on your own infrastructure. While that self-host tag appeals to engineering-led, compliance-first organizations, it also means managing deployments, security, and scaling in-house.

A key benefit is that PostHog reduces multi-vendor headaches by bundling major analytics functions, including web analytics, replay, feature flags, experiments, and surveys.

As for concerns, PostHog doesn’t come with an ecommerce playbook like GA4 or Mixpanel. Teams must define their own events (view item, add to cart, purchase) and construct funnel and revenue reports on top of that custom layer. That flexibility is powerful, but it shifts more work onto product and engineering teams.

Verdict: PostHog is ideal for highly technical teams looking to combine their analytics and experimentation tools, provided they’re comfortable owning their event design and infrastructure.

Pros

Open-source model with a self-hosting option for full data ownership and compliance control.
Unified platform for analytics, flags, experiments, and surveys, which means fewer vendors to deal with.
Transparent, usage-based pricing with a generous free tier.

Cons

Setup and ongoing maintenance are engineering-heavy, making it less friendly for non-technical teams.
No native ecommerce event schema; teams must build their own cart and checkout tracking.

Pricing

PostHog offers free, self-serve, and custom pricing options. The no-credit-card free plan includes usage limits: 1M events/month, 5K session recordings, 1M feature flag requests, and 1,500 survey responses. Once you hit the limits, you can add a card to use pay-as-you-go billing.

For larger teams, PostHog also offers premium platform packages:

Boost ($250/month): Unlimited projects, white labeling, SSO and 2FA enforcement, and collaboration features.
Scale ($750/month): Adds priority support, real-time alerts, SAML SSO, and approvals.
Enterprise (custom pricing): Adds dedicated account manager, user training, RBAC, and property access control.

Review

“What I like most about PostHog is how much visibility it gives into user behavior without needing multiple tools. We use it to track events, analyze funnels, understand drop-off points, and monitor product usage in one place. Session recordings and feature-level insights are especially helpful because they help us understand not only what users do, but also why they do it. It is flexible and provides a lot of value for product and growth teams.”
- Mahdi H., Chief Marketing Officer (Review on G2)

Driving ROI with your product analytics software

Knowing exactly why a shopper abandons their cart is only half the battle. To build high-converting digital storefronts, successful teams rely on these platforms to translate raw behavioral data into targeted, ROI-driven optimizations:

Replacing guesswork with revenue-tied behavior tracking

Direct evidence beats assumptions. Advanced tools capture granular qualitative data, including clicks, searches, filters, cart actions, and checkout steps. This behavioral context helps your merchandising and UX teams make data-driven decisions about product design and placement.

Instead of guessing, you can see clearly how users engage, where they get stuck, and what to change to recover lost sales.

Diagnosing friction and accelerating improvements with AI

The best product analytics tools for ecommerce also help you detect where users get frustrated and pinpoint exact exit points across the user path, including at checkout.

AI-powered product analytics platforms, like Fullstory, accelerate this process by automatically identifying hidden obstacles to conversion. They even rank these issues by business impact, letting your team prioritize the right fixes and resolve journey blockers faster.

How to choose the right platform for your stack

Choosing the best ecommerce analytics tool comes down to carefully considering three core factors: operational focus, team structure, and resource constraints. To find the right fit, ask yourself:

1. What ecommerce behavior matters most to you?

Identify where your biggest revenue bottlenecks and enablers lie. If you rely heavily on product catalog and discovery, you’ll need in-depth journey tracking to reveal exactly which search, filter, or recommendation features drive conversion.

Conversely, if you’re losing buyers at the finish line, go for tools with precise funnel analysis and session replays to spot checkout friction. And if maximizing lifetime value is your priority, deep cohort analysis to monitor returning users and loyalty behavior is your best bet.

2. Who uses the data?

Teams often lag in preventing revenue leaks because they’re busy learning tools. Match platform complexity to primary users so they can start reaping benefits from day one.

For instance, non-technical product and growth teams love visual query builders to run A/B tests, build cohorts, and slice data without relying on engineers for every task.

Technical teams operate differently. They typically play with raw event data, SQL access, and warehouse integrations to build data analytics models of their choice. Meanwhile, your CX teams expect session replays and AI summaries to show them exactly why customers get frustrated mid-journey or hesitate to purchase.

3. What’s your tolerance for engineering setup and cost?

The right tool fits your current technical bandwidth and doesn’t blow up your budget when usage skyrockets. Choosing the tool you actually need requires evaluating the platform against your engineering capabilities and cost constraints:

Low engineering & predictable cost: Prefer a tool that features snippet-based autocapture and retroactive event definition. Leveraging Fullstory’s Fullcapture or Contentsquare’s autocapture makes the most sense.
Moderate engineering & mid-to-high cost: Choose a platform that requires managing event taxonomy and scales pricing based on usage (events or MTUs). Amplitude and Mixpanel prevail here. Be prepared, however, for costs to spike when holiday traffic hits.
High engineering & scale-dependent cost: If you want full data control, choose a tool that offers open-source or self-hosted options. PostHog makes the cut, but can require significant setup, maintenance, and operational overhead.
Free baseline: If you’re just starting, GA4 offers basic tracking at no cost, though it lacks the deep behavioral context of premium platforms.

Pro tip: If you operate across regions with strict privacy rules (GDPR, CCPA), prioritize platforms with strong data governance, regional hosting, and privacy-first architectures. For instance, Fullstory’s Privacy-by-Default architecture helps reduce legal risk by masking sensitive data at the source while keeping behavioral data actionable.

Why Fullstory leads for ecommerce behavioral analytics

Most analytics tools show you funnel leaks and drop-off rates, but often fail to answer why or misdiagnose the issue. The real root cause is usually lost in vague metrics or inaccurate event tagging.

Fullstory flips that model by going deeper than most other product analytics tools for ecommerce. Instead of just reporting funnel metrics, it delivers full-journey behavioral analytics. Because it captures every interaction without a tracking plan, your team can respond to questions as they arise.

StoryAI bypasses heavy setups by automatically surfacing and ranking the reasons why users struggle anywhere in the journey. Guesswork takes a back seat; AI provides the precise data required to apply fixes that matter most to your revenue pipeline.

The outcomes are clear: faster friction detection, effective data-driven decisions, and fewer shoppers lost to missed opportunities.

Fullstory delivers privacy-conscious, AI-powered behavioral intelligence to help ecommerce teams turn “what happened” into “what to fix” without waiting on engineering support.

9 WalkMe alternatives for digital adoption in 2026

Fullstory — Wed, 08 Apr 2026 12:00:00 GMT

Finishing a walkthrough doesn't mean a user succeeded. They can click through every step and still not accomplish what they came to do. If you can't see what happened before, during, and after, you're measuring effort.

The digital adoption platform market divides roughly along that line. Guidance-first platforms assume you already know where friction lives. Experience-first platforms show you. Gartner predicts that by 2028, 40% of organizations will use generative AI in their DAPs.

This guide covers nine WalkMe alternatives across use-case fit, analytics depth, and setup complexity.

WalkMe alternatives at a glance

Platform	Primary use case	Key strength	Best for	Setup complexity	G2 rating (Q1 2026)
Whatfix	Employee & Customer Onboarding	Enterprise scale & governance	Large Enterprises	High	4.6
Fullstory	Digital Experience & Product Adoption	Behavior-triggered guidance & in-app surveys	Mid-Market to Large Enterprises	Medium	4.5
Tango	Employee Process Training	Guides built from a single capture, no dev work	Mid-Market IT & Ops Teams	Low	4.7
Pendo	Product Experience & Analytics	Combined analytics & NPS	SaaS, Enterprise	Medium	4.4
Apty	Enterprise Software Adoption	Speed to ROI & process compliance	Mid-Market to Enterprise	Medium	4.7
Userlane	Employee Onboarding	Simplicity & fast deployment	Mid-Market	Low	4.7
Nexthink Adopt	Process Intelligence & Adoption	Business process outcomes	Enterprise (ERP/CRM)	High	4.6
UserGuiding	SaaS Customer Onboarding	Affordable & fast setup	SMB to Mid-Market	Low	4.7
Spekit	AI-first Revenue Enablement	In-workflow Guidance for Revenue Teams	Mid-market to Enterprise Revenue Teams	Low	4.7
VisualSP	Microsoft 365 Adoption	Pre-built M365 content	Microsoft-Heavy Organizations	Low	4.9

Why teams search for WalkMe alternatives

WalkMe reports on guide engagement metrics, such as completion rates and step drop-offs. What it doesn't show is what users were doing before the walkthrough, or whether their behavior shifted after. Teams that want to understand why friction happens, not just where a guide was abandoned, eventually hit that wall.

SAP's 2024 acquisition gives buyers outside the SAP ecosystem reason to read the roadmap carefully before committing long-term. Acquired platforms tend to prioritize the acquirer's core customers over time. Whether WalkMe has shifted that way is still an open question.

A closer look at WalkMe alternatives

Whatfix

Whatfix is built for large organizations running governed walkthrough programs across multiple applications. The Mirror feature lets teams test walkthroughs in a staging environment before pushing them live. For complex enterprise rollouts, that safeguard matters.

Category: Guidance-first DAP with strong governance

Best for: Enterprises managing large volumes of walkthroughs across many applications

Strengths:

Visual flow builder with branching logic, audience segmentation, and version control. Guide-building tooling is among the most developed in the category.
Mirror validates walkthroughs in pre-production before they reach customers, cutting the risk of broken or confusing flows shipping to users.
Content governance for large teams of walkthrough authors: role-based access, rollback, and approval workflows.

Limitations:

Session replay requires defining which user segments to record; you won't capture sessions you didn't plan for in advance.
Analytics cover guide engagement only. There's no pre-guide behavioral data to show where friction exists before you build.

Fullstory

Fullstory sits at the intersection of in-app guidance and behavioral intelligence. Most DAPs let you build walkthroughs and report on completion rates. Guides and Surveys runs on Fullstory's behavioral data platform, so the intelligence is already there before you build anything. Rage clicks, dead clicks, and error patterns surface automatically across your product. Build a guide that shows users struggling, then use session replay to check whether behavior actually changed after you deploy it.

Category: Behavior-backed digital adoption

Best for: Product and onboarding teams that want to build targeted guidance informed by real usage data, not assumptions

Strengths:

Behavior-triggered in-app guidance: Product tours, onboarding checklists, and surveys fire on specific user behaviors, not just page visits or timer logic. Guides appear when behavioral signals indicate a user needs them.
Session replay validates guide effectiveness: After deploying a guide, you can watch actual sessions to see whether behavior changed downstream. Completion rate is a lagging indicator; session replay shows the real outcome.
Automatic frustration detection: Rage clicks, dead clicks, error clicks, and thrash cursors are detected across your application without manual setup. You find where users struggle, even when you weren't looking for it.

Limitations:

Newer guide builder: Guides and Surveys launched in early 2026. Functional, but offers fewer out-of-the-box templates and less conditional branching logic than WalkMe or Whatfix, which have years of iteration behind them.
No in-app employee guidance for Fullstory Workforce (yet). Guides and Surveys is on the Fullstory Workforce product roadmap but not yet available to the general public.

Tango

Tango is a browser extension that turns a task someone has already done into a step-by-step guide, then plays that guide back as live in-app guidance inside any web app. The person who knows the process clicks Start Capture, does the work once, and Tango generates the screenshots, click highlights, and written steps. Deployment runs through the extension, with no API integration or developer work, so IT and Ops teams often add it alongside a heavier DAP when the walkthrough backlog is longer than the rollout timeline.

Category: Capture-first DAP for employee process training

Best for: IT and Ops teams training employees on internal web apps during a system rollout or migration

Strengths:

Guides are built by doing the process, not by configuring flows in an editor. When a screen changes, you recapture the one affected step instead of rebuilding the guide. Live Blur hides sensitive fields in screenshots.
Guide Me turns any guide into a live walkthrough on the user's screen, with no code or dev team. It isn't licensed per application, so the same account covers the HR system, the CRM, and the ERP.
Pins place tooltips or full guides on specific pages inside an app, and the extension surfaces guides relevant to the page the user is on. Guides also embed into any knowledge base or LMS that supports iframes.

Limitations:

Less configurable than WalkMe or Whatfix. No pop-up announcements, banners, or complex conditional flows.
Guide Me requires the Tango extension on each viewer's browser. Analytics cover guide views and step drop-off, with no session replay or behavioral data from before a guide exists.

Pendo

Pendo combines product analytics with in-app guidance, making it a natural fit for product and customer success teams that want usage data and walkthroughs in one platform. It's particularly strong in SaaS activation funnel analysis and feature adoption tracking.

Category: Product analytics + in-app guidance hybrid

Best for: Product teams that want usage analytics and guidance in a single platform

Strengths:

Strong product analytics: feature adoption, click paths, retention, and funnel analysis in one interface.
In-app NPS, surveys, and product roadmapping are built in alongside walkthroughs and tooltips.
Offers a free tier for smaller teams

Limitations:

Session replay retention is capped at 30 days, or 90 with an add-on.
G2 reviewers flag complexity as a barrier at enterprise scale. Getting full value takes real ramp time.

Apty

Apty targets regulated industries where data validation inside guided flows isn't optional. Finance and healthcare teams use it when process enforcement needs to happen inside the walkthrough itself, not as a separate layer.

Category: Compliance-focused DAP

Best for: Regulated industries needing data validation and process enforcement within guided flows

Strengths:

Data validation inside guided flows: enforces required field completion and compliant process paths in real time.
Cross-application process support with a no-code guide builder designed for business users, not developers.
Apty claims an average 3.4x ROI in the first year, based on its own customer data.

Limitations:

No pre-guide behavioral data. You know where your process should break down; you can't see where it actually does before building the walkthrough.
Smaller market presence in the DAP market than WalkMe or Whatfix; fewer community resources, third-party integrations, and implementation partners.

Userlane

Userlane is a lightweight DAP built for speed. Walkthroughs can go live in days, not weeks, and the visual editor is designed for non-technical users. It's built for mid-market organizations that need fast-deploying guidance without the complexity of enterprise systems.

Category: Lightweight DAP for rapid deployment

Best for: Mid-market teams that need simple, fast-deploying in-app guidance

Strengths:

Fast deployment: walkthroughs go live in days with a visual editor that doesn't require technical expertise.
Built-in adoption analytics: guide completion rates, engagement tracking, and rollout progress across deployed walkthroughs.
Multilingual out of the box: walkthroughs deploy across languages without rebuilding content, which matters for global employee rollouts.

Limitations:

Limited analytics depth. Reporting covers guide engagement, with no behavioral data before or after guidance.
Not suited for complex, multi-app enterprise environments with heavy integration or governance requirements.

Nexthink Adopt

Nexthink Adopt connects adoption data to business process outcomes, not just to guide completion rates. ERP and CRM teams use it when the question is whether the business process actually improved, not whether users clicked through the walkthrough.

Category: Process intelligence + adoption

Best for: Enterprise IT teams focused on measuring actual business-process completion

Strengths:

Process intelligence that ties adoption data to business outcomes, not guide metrics.
Cross-application journey mapping for ERP and CRM environments where workflows span multiple tools.
In-app guidance and process analytics in one platform.

Limitations:

Smaller market presence than WalkMe, Whatfix, or Pendo. Fewer community resources, third-party integrations, and implementation partners.
No automatic frustration detection. Rage clicks, dead clicks, and error patterns don't surface.

UserGuiding

UserGuiding is a no-code onboarding tool for startups and small SaaS teams. Setup takes hours, pricing is transparent, and it doesn't try to do enterprise analytics or governance.

Category: Affordable onboarding for SMBs

Best for: Startups and small teams that need basic in-app guidance at entry-level costs

Strengths:

Transparent, affordable pricing with a clear tier structure, no sales call required to understand cost.
No-code guide builder with fast setup. Live in hours, not weeks, with product tours and onboarding checklists.
Self-service resource centers for in-product knowledge delivery without additional tooling.

Limitations:

Not built for enterprise-scale or multi-app environments. Analytics are basic compared to Pendo or Fullstory.
Guide completion is the only available success metric. There's no way to verify whether user behavior actually changed after a guide was deployed.

Spekit

Spekit is an AI-first revenue enablement platform that combines governed knowledge, learning, coaching, content creation, and digital sales rooms, with guidance delivered inside the tools reps already use. For revenue teams evaluating WalkMe to improve sales onboarding, CRM adoption, and in-workflow execution, Spekit offers a purpose-built alternative that brings enablement directly into daily selling workflows.

Category: Revenue enablement and in-workflow execution

Best for: Mid-market and enterprise revenue teams that need governed knowledge, coaching, and content inside daily selling workflows

Strengths:

Guidance extends beyond CRM. Spekit delivers approved answers, content, coaching, and next actions inside Salesforce, Gmail, Outlook, Gong, Slack, LinkedIn, and other browser-based tools
The governed Knowledge Engine supports content ownership, version history, permissions, freshness controls, and AI-assisted creation. The same approved content powers Learning Paths for onboarding and personalized digital sales rooms for buyer engagement.Analytics connect content adoption, rep learning, buyer engagement, and Salesforce outcomes, including which assets correlate with accelerated pipeline and closed-won deals.

Limitations:

Purpose-built for revenue enablement, not company-wide employee or customer software adoption across every department and application.
No session replay, frustration detection, or broad product-behavior analytics. Teams needing those capabilities would pair Spekit with a digital experience analytics platform.

VisualSP

VisualSP is a Microsoft 365-focused DAP that ships with pre-built content for SharePoint, Teams, and Office. Organizations already in the Microsoft ecosystem can get up faster than they would building guides from scratch.

Category: Budget-friendly DAP with Microsoft ecosystem strength

Best for: Organizations heavily invested in Microsoft 365

Strengths:

Pre-built Microsoft 365 content cuts setup time for SharePoint and Teams rollouts.
Transparent pricing with a free tier, accessible without a sales process or procurement cycle.
Simple setup and administration, practical for lean IT teams without dedicated DAP resources.

Limitations:

Built specifically for the Microsoft ecosystem. Coverage across non-Microsoft enterprise tools is limited.
No session replay, no behavioral data, no frustration signals. Analytics are guide-engagement only.

Build guidance on behavior, not assumptions

Whatfix and Pendo are the right call when walkthrough volume and content governance are your priority. Fullstory is the right call when you need to understand where friction lives before you build anything: where you go when you're done guessing where users struggle and want the data to show you.

If your priority is...	Evaluate first...
User guidance backed by behavioral data, verified through session replay	Fullstory Guides and Surveys
Guide builder with governance at enterprise scale	Whatfix or WalkMe (if SAP-aligned)
In-app guidance for internal tools without dev queue	Tango
Product analytics + guidance	Pendo
Compliance-focused process enforcement	Apty
Fast, simple deployment for mid-market	Userlane
Process intelligence tied to business outcomes	Nexthink Adopt
Budget-friendly SMB onboarding	UserGuiding
Revenue enablement & in-workflow guidance	Spekit
Microsoft 365 adoption	VisualSP

Stop guessing where users struggle

Walkthrough volume and content governance at scale? Whatfix and Pendo can handle those. Already in the SAP ecosystem? WalkMe's roadmap may still work in your favor.

Fullstory Guides and Surveys runs on behavioral data. You know where friction lives before writing a single guide, you build for users who need it, and you use session replay to check whether anything actually changed.

See how it works →

Desire paths: the clearest shortcut to better digital experiences

Justin Owings — Mon, 17 Apr 2017 16:30:00 GMT

Finding the shortest distance from point A to point B is a problem we’re always solving. Whether it’s the way to work or the way through the grocery store, we try to optimize our route.

When walking the same ground enough, we wear down the low-lying vegetation — the grass — and we create a visible path in the dirt.

A trail where there was none before. No authority planned it. No one designed it. Yet there it is as clear as day — a path to follow that sorta just makes sense.

A path was desired.

What is a desire path?

A desire path is simply an unofficial trail, a shortcut people or even animals create by repeatedly moving between two points.

Think of it as a clear line worn into the grass or dirt, not because someone planned it, but because it's the most intuitive or efficient way to get from here to there. It’s the result of individual choices aggregating together and creating something meaningful. Spontaneous, emergent order.

But there’s no official name for desire paths, really, and that makes a lot of sense.

Desire paths are born from life; wherever you find life, you find desire paths. So what do we do with them?

Are desire paths good or bad? Is taking them wrong?

The paved sidewalks and chained-in grassy areas on college campuses and public parks suggest they are a problem to be solved.

“Please stay off the grass!”

Desire paths should be stopped — they’re a nuisance. Taking a shortcut through the landscaped common area in lieu of a paved route is not the way things were designed. You’re supposed to follow the marked sidewalk.

Does anyone actually believe this?

There’s research that suggests we make choices based on perceived effort[1] — preferring the apparent “path of least resistance.”

Really, it’s human nature to question the rules — particularly those rules that feel arbitrary. Adhering to rules that cost us dearly (And what’s more expensive than our time?) are especially unlikely to be followed.

So while desire paths may scar a meticulously designed and beautifully maintained landscape, objecting to or trying to stop their use is a Sisyphean task.

If stopping desire paths is a waste of time, what are we to do with them?

The opportunity in plain sight : pave the desired paths

When Walt Disney created Disneyland, he didn’t fence in the grassy areas until he saw where people walked. The most frequently used walkways — as indicated by desire paths — got codified into official sidewalks[2]. Frank Lloyd Wright was said to apply the same tactic.

Or take universities. Instead of trying to predict, in advance, where students will walk the most, some campus planners plant the grass and just sit back and wait — the paths present themselves in short order once school is in session. From there, they need only pave them.

Student lore at Michigan State University (seen below), Ohio State University, and Reed College[3] all talks of how this was the process that drove their campus sidewalk layouts.

There’s a power in desire paths that simply requires a change of frame — desire paths aren’t a problem to be solved so much as a solution that has been rendered.

Desire paths at the Ohio State University

The layout of "The Oval"—the central hub of campus life at Ohio State University—might look a little strange at first glance: a green lawn, criss-crossed by a seemingly random pattern of paved trails.

In reality, these paths are far from random. As students made their way from building to building across the lawn, they left a trail over time. Only then did the university pave the paths.

Essentially, even the best-planned walkways would be inferior to the ones chosen organically by students.

So, what does a desire path on a college campus have to do with behavioral data?

Find the path and make it better

As you’ve likely realized, desire paths aren’t limited to dirt. Desire paths are symbolic of how people figure out new and easier ways to get a job done.

If you see a desire path, you’ve got a blinking light of opportunity: take the rough-hewn path and make it better. This principle applies just as much to digital experiences.

The internet is a great green, grassy space

The internet is a frontier for creation — new sites, services, platforms, apps, whatever. Within these creations, users have immense freedom to go their own way. Desire paths are a natural outcome.

Even within this frontier space, there are limitations and constraints.

Connectivity to the Internet leaves users feeling time-constrained — there are so many possibilities it can be overwhelming. Thanks to our always-on, always-available status, not a moment goes by when we aren’t able to pursue something. We’re left distracted, anxious, and irritated in the event that our changing whims can’t be satisfied.

Yet these constraints drive innovation: create a better way to get from point A to B — desire paths and the Internet go hand-in-hand.

Let’s consider a few examples.

Messaging services — AIM to WhatsApp to WeChat

Messaging has been around for awhile and it’s gone through a number of iterations — messaging desire paths get paved, things change, and then they’re replaced by better solutions.

At the most basic level, messaging emerged as a direct, instant way to communicate by text with other people. It came about in the 1990’s through ICQ and AOL Instant Messenger (AIM) and sidestepped email by being a faster, less formal way to communicate asynchronously.

We’ve come a long way since then, but the evolution of messaging is far from over. Paint a broad stroke and you see how SMS and MMS limits — in number of messages or the expense of one-off messages — led to users taking the desire path of using over-the-air services like iMessage, Google Hangouts, WhatsApp, or Facebook Messenger.

While it’s not taken off in the U.S. as much as abroad, the simplicity of chat as an instant interface to transact has led to an explosion of chat functionality in apps like WeChat in China (which all sorts of e-commerce can take place within the app, itself).

While the idea of shopping by chat may feel foreign to many in the U.S., it’s native behavior to many in China.

Separately, there’s the trend to “chatbot all the things.” Here, messaging can be seen as a way for users to interact with a business however they choose, and if the chatbot works, users build their desire path on-the-fly.

And a third messaging-related example is chat-based customer support services (like Intercom). When users feel the marked paths leave them feeling lost with no clear way to go, these services give them a paved path toward getting answers.

Smartphones — desire path engines

Smartphones exploded in popularity because they paved the path that previous connected devices were only lightly facilitating — that is, browsing the Internet.

Before the iPhone, using OTA messaging services, taking and sharing photos, etc., in feature phones like the Blackberry Curve, while possible, was clunky and painful. The digital experience for users felt bolted-on instead of native.

The iPhone (and Android) paved the desire path for using mobile Internet by making it the prime function of the phone.

Once that path was paved — access and functionality on mobile — smartphone platforms became the wide open greens that facilitate a near limitless potential for desire paths.

Taking it even further, you can see apps as paved desire paths for what users would otherwise do on a browser. And today, as smartphones get more sensors, faster connections, and more processing power, the frontier of mobile grows.

Twitter and Instagram — social streams of constraints and desire

Twitter is a study in constraints and desire

Hashtags and at replies were early desire paths that users carved into the Twitter-sphere that Twitter ultimately made native to the platform.

Image-sharing is another example. Twitter didn’t build in this functionality so users created the desire paths — sharing links to image-sharing services to work around the problem. That desire path got paved when Twitter brought the functionality in-house but it arguably happened too late — a separate image sharing service had paved it better.

Meet Instagram.

Or look at Twitter’s character limit. While it’s not going away, Twitter users can easily circumvent the character limits using images. If you want to share more than 140 characters — maybe you want to share a quote from an article or a book or you wrote a note on your phone — simply do a screenshot on your phone and share that.

Shifting to Instagram. Here’s a platform where image sharing is primary. Smartphones exploded in popularity because they paved the path that earlier devices were only lightly facilitating — that is, browsing the Internet.and text secondary. Many social media influencers (e.g. celebrities) have amassed huge Instagram followings and use Instagram as their primary channel.

So what do these influencers do when they have something to say on Instagram? Like on Twitter, they screenshot their phones — either a written note or whatever — and share that[4].

Benedict Evans has pointed to screenshots on phones as desire paths to fill unmet needs within apps.

Other examples of Instagram paving desire paths abound — from collages to carousels to (arguably) co-opting Snapchat’s story functionality.

Google

Google is a desire path paving machine. Where do you want to go? Google takes you there — “10 blue links” that get you from Point A to B in milliseconds.

Google is well-known for paving desire paths.

For example, Google has started surfacing the answers right in the search engine results page, requiring no further work on the part of users , as is the case with Knowledge Graph cards, featured snippets, and more notably, AI Overviews.

In some cases, Google surfacing quotes from top results for a query has dramatically impacted the downstream websites from which the answers came.

Another example of Google paving a desire path is when the answer to a query surfaces right there in the auto-suggest box — before you even execute the search!

The Internet is a massive green space across which desire paths are constantly being cut in apps, devices, and websites.

What other examples can you think of?

Finding desire paths in a digital world

Desire paths are powerful because they don’t require you to know any answers in advance. Simply give an audience an objective, allow them to find a way to achieve that objective, observe what they do, and then make their solution better.

Then, as mentioned, you must pave the paths.

As useful as desire paths can be for landscape architects who design physical spaces, digital space desire paths are considerably harder to see.

Users can't blaze a trail through JavaScript and HTML. Plodding mouse movements, swipes, and clicks of users don’t cut desire lines into the browser or the workflows of a mobile app.

Unlike OSU, how do product managers, UX designers, or software developers see their users’ desire paths if they never get made?

The answer lies in on-page signals.

Desire paths and UI design

To build a successful website—or SaaS—you have to drive users to sign-up, successfully onboard them, and do the job promised well.

Developers have struggled to see how users engage with their websites, apps, and products. Outcomes could be measured using analytical services (e.g. Google Analytics), but a metric like bounce rate only tells you someone left, not what they were trying to do before they gave up. Actually seeing where users are going and intuiting their intent was nearly impossible.

That’s why Fullstory session replay is so powerful—it reveals where the “paved sidewalk” (a web site’s layout, app UX/UI, whatever) isn’t working as intended as well as visualizing, literally where the users are trying to go. Using session replay to see user flows helps developers and designers improve them.

It’s a little-known fact that the original code name for Fullstory was CowPaths.

Separately from surfacing user desire paths, Fullstory also helps with the desire paths of designers, product managers, and engineers — by allowing them to search user interactions for whatever behavior they need to understand. What users click a certain button? Use a certain feature? Abandon their cart or get lost in your knowledge base? Just search it.

Just as much Google search is a desire path paving engine for bringing users to the world’s information, Fullstory provides that power for an organizations’ user interactions on their web app.

With or without a service like Fullstory, understanding a user’s desire paths on your site can make the difference between success and failure. No business is immune to the constraints on time and attention for users — getting them from point A to point B on your site so they can do whatever it is they’re trying to do — well, that’s our job as builders of products and services.

And it helps to mind the desire paths.

What desire path is your life on?

Hope is like a path in the countryside. Originally, there is nothing — but as people walk this way again and again, a path appears. — Lu Xun, Chinese Essayists, 1921

We all make plans, but life happens anyway. Desire paths result.

Instead of relegating ourselves to plodding down the same desire paths we’ve walked countless times before, perhaps it’s time to make them better — whether in business, in design, or in life.

What desire paths do you take? What paths have you paved? Where is the path now on taking you?

See Perceptual decisions are biased by the cost to act, Hagura, Haggard, and Diedrichsens. ↩︎
See the this write-up on Walt Disney’s attention to detail. ↩︎
See this Reddit thread and photo by u/Epithymetic. ↩︎
See A Brief History of Celebrities Using the Notes App to Share Their Thoughts With the World via Jezebel. ↩︎

What is data processing? Definition, steps & methods

The Fullstory Team — Thu, 14 Mar 2024 12:00:00 GMT

So you've been hearing a lot about data processing but still don't quite understand it? We're here to help.

Imagine data processing as akin to a chef preparing ingredients for a meal. Just like a chef carefully selects, chops, and combines raw ingredients to create a delicious dish, data processing involves the careful selection, organization, and transformation of raw data into insightful, useful information.

It's about turning the unstructured and raw into something valuable and meaningful, much like cooking turns basic ingredients into culinary masterpieces.

Key takeaways:

Data processing involves transforming raw data into useful information
Stages of data processing include collection, filtering, sorting, and analysis
Data processing relies on various tools and techniques to ensure accurate, valuable output

What is data processing?

Data processing is the series of operations performed on data to transform, analyze, and organize it into a useful format for further use.

Various stages and methods are used to manipulate raw data into relevant or consumable formats. These stages often include collecting, filtering, sorting, and analyzing the data.

The goal is to extract pertinent information that can be applied in decision-making processes or support existing technologies. To achieve this, data engineers and data scientists employ a range of data processing tools and techniques, ensuring that the output is both accurate and valuable.

Let's dive deeper into each stage.

6 steps in data processing

1. Data collection

The first stage of data collection involves gathering and discovering raw data from various sources, such as sensors, databases, or customer surveys. It is essential to ensure the collected data is accurate, complete, and relevant to the analysis or processing goals. Care must be taken to avoid selection bias, where the method of collecting data inadvertently favors certain outcomes or groups, potentially skewing results and leading to inaccurate conclusions.

2. Data preparation

Once the data is collected, it moves to the data preparation stage. Here, the raw data is cleaned up, organized, and often enriched for further processing. This stage involves checking for errors, removing any bad data (redundant, incomplete, or incorrect), and enhancing the dataset with additional relevant information from external sources, a process known as data enrichment. Data preparation aims to create high-quality, reliable, and comprehensive data for subsequent processing steps.

3. Data input

The next stage is data input. In this stage, the clean and prepped data is fed into a processing system, which could be software or an algorithm designed for specific data types or analysis goals. Various methods, such as manual entry, data import from external sources, or automatic data capture, can be used to input data into the processing system.

4. Data processing

In the data processing stage, the input data is transformed, analyzed, and organized to produce relevant information. Several data processing techniques, like filtering, sorting, aggregation, or classification, may be employed to process the data. The choice of methods depends on the desired outcome or insights from the data.

5. Data output and interpretation

The data output and interpretation stage deals with presenting the processed data in an easily digestible format. This could involve generating reports, graphs, or visualizations that simplify complex data patterns and help with decision-making. Furthermore, the output data should be interpreted and analyzed to extract valuable insights and knowledge.

6. Data storage

Finally, in the data storage stage, the processed information is securely stored in databases or data warehouses for future retrieval, analysis, or use. Proper storage ensures data longevity, availability, and accessibility while maintaining data privacy and security.

Want to find out where your company stands with behavioral data? Download the Fullstory Data Maturity Matrix and take our quiz to find out.

Types of data processing

Data processing utilizes various methods to convert raw data into meaningful information. These methods can be classified into several types, each catering to different scenarios and requirements.

In this section, we will briefly discuss the following data processing types: batch processing, real-time processing, multiprocessing, online processing, manual, mechanical, electronic, distributed, cloud computing, and automatic data processing.

Batch processing

Batch processing involves handling large volumes of data collectively at predetermined times, making it ideal for non-time-sensitive tasks. This method allows organizations to efficiently manage data by aggregating it and processing it during off-peak hours to minimize the impact on daily operations.

Example: Financial institutions batch process checks and transactions overnight, updating account balances in one comprehensive sweep to ensure accuracy and efficiency.

Real-time processing

Real-time processing is essential for tasks that require immediate handling of data upon receipt, providing instant processing and feedback. This type of processing is crucial for applications where delays cannot be tolerated, ensuring timely decisions and responses.

Example: GPS navigation systems rely on real-time processing to offer turn-by-turn directions, adjusting routes based on live traffic and road conditions to ensure the fastest path.

Multiprocessing (parallel processing)

Multiprocessing, or parallel processing, involves utilizing multiple processing units or CPUs to handle various tasks simultaneously. This approach allows for more efficient data processing, particularly for complex computations that can be broken down into smaller, concurrent tasks, thereby speeding up overall processing time.

Example: Movie production often utilizes multiprocessing for rendering complex 3D animations. By distributing the rendering across multiple computers, the overall project's completion time is significantly reduced, leading to faster production cycles and improved visual quality.

Online processing

Online processing facilitates the interactive processing of data over a network, with continuous input and output for instant responses. It enables systems to handle user requests immediately, making it an essential component of e-commerce and online services.

Example: Online banking systems utilize online processing for real-time financial transactions, allowing users to transfer funds, pay bills, and check account balances with immediate updates.

Manual data processing

Manual data processing requires human intervention for the input, processing, and output of data, typically without the aid of electronic devices. This labor-intensive method is prone to errors but was common before the advent of computerized systems.

Example: Before the widespread use of computers, libraries cataloged books manually, requiring librarians to carefully record each book's details by hand for inventory and retrieval purposes.

Mechanical data processing

Mechanical data processing uses machines or equipment to manage and process data tasks, a prevalent method before the digital era. This approach involved using tangible, mechanical devices to input, process, and output data.

Example: Voting in the early 20th century often involved mechanical lever machines, where votes were tallied by pulling levers for each choice, simplifying vote counting and reducing the potential for errors.

Electronic data processing

Electronic data processing employs computers and digital technology to process, store, and communicate data with efficiency and accuracy. This modern approach to data handling allows for rapid processing speeds, vast storage capabilities, and easy data retrieval.

Example: Retailers use electronic data processing at checkouts, where barcode scans instantly update inventory systems and process sales, enhancing checkout speed and inventory management.

Distributed processing

Distributed processing involves spreading computational tasks across multiple computers or devices to improve processing speed and reliability. This method leverages the collective power of various systems to handle large-scale processing tasks more efficiently than could be achieved with a single computer.

Example: Video streaming services use distributed processing to deliver content efficiently. By storing videos on multiple servers, they ensure smooth playback and quick access for users worldwide.

Cloud computing

Cloud computing offers computing resources, such as servers, storage, and databases, over the internet, providing flexibility and scalability. This model enables users to access and utilize computing resources as needed, without the burden of maintaining physical infrastructure.

Example: Small businesses leverage cloud computing for data storage and software services, avoiding the need for significant upfront hardware investments and allowing easy scaling as the business grows.

Automatic data processing

Automatic data processing uses software to automate routine tasks, reducing the need for manual input and increasing operational efficiency. This method streamlines repetitive processes, minimizes human error, and frees up personnel for more strategic tasks.

Example: Automated billing systems in telecommunications automatically calculate and send out monthly charges to customers, streamlining billing operations and reducing errors.

Data processing technologies & tools

Several emerging technologies and tools play vital roles in extracting valuable insights from raw data. This section covers three aspects: Databases and Data Warehouses, Artificial Intelligence & Machine Learning, and Cloud Technology & Data Analytics Platforms.

Databases and data warehouses

Databases are essential in storing structured data, providing the foundation for data processing tasks. They enable efficient querying, updating, and retrieval of information. Examples of popular databases include SQL-based systems like MySQL, PostgreSQL, and Microsoft SQL Server.

On the other hand, data warehouses are large-scale storage systems that accumulate data from various sources. They are optimized for querying and analyzing vast datasets to support business intelligence and decision-making. These warehouses often employ big data technologies such as Hadoop, Apache Spark, and data lakes, providing a centralized repository for massive amounts of data.

Artificial intelligence & machine learning

As the backbone of many modern data processing methods, artificial intelligence (AI) and machine learning (ML) help organizations uncover patterns and make predictions based on available data. Popular ML languages include Python, R, and SAS, offering flexibility and a wide range of libraries for the data processing workflows.

Some of the most impactful ML techniques in data processing are:

Supervised learning: Training models with labeled data to make predictions
Unsupervised learning: Extracting patterns from unlabeled data, such as clustering or dimensionality reduction
Reinforcement learning: Improving actions on-the-fly based on feedback from the environment

These ML approaches have facilitated breakthroughs in diverse fields, from speech recognition to medical diagnostics.

Cloud technology & data analytics platforms

Cloud technology has revolutionized the way businesses handle data processing, offering scalable, cost-effective, and location-independent solutions. Some of the highest-ranked cloud providers include Amazon Web Services, Microsoft Azure, and Google Cloud Platform. These services enable organizations to deploy data analytics platforms and infrastructure without the need to maintain on-premise hardware.

Cloud-based data analytics platforms offer tools and frameworks for ingesting, processing, and visualizing data. Typical components of these platforms include:

Data storage: Storing data in data lakes or other distributed storage systems
Data processing: Running large-scale data operations like ETL workflows and analytics jobs
Data orchestration: Coordinating data processing tasks across different systems and tools
Data visualization: Presenting processed data in an easily digestible manner for decision-makers

Applications of data processing

Business intelligence & strategies

Data processing plays a crucial role in business intelligence and strategies, as it helps organizations transform raw data into usable information. This information is then used for data analysis, visualization, and reporting, which ultimately drive the decision-making processes. For instance, companies use insights derived from processed data to identify trends and growth opportunities and enhance their competitive edge in the market.

Moreover, data processing ensures legal compliance, such as adhering to GDPR regulations, by filtering and securely storing valuable data.

Healthcare, e-commerce, and finance

In healthcare, data processing is employed to analyze electronic health records, monitor patients' health conditions, and optimize treatment plans. For instance, healthcare providers can utilize data analysis to predict and manage disease outbreaks more effectively.

E-commerce platforms leverage data processing to analyze customers' shopping patterns, personalize the shopping experience, and streamline supply chain management. Online retailers can optimize their pricing strategies and enhance customer satisfaction by analyzing and understanding user data.

Finance is another industry that extensively harnesses data processing. Stock trading software employs processed data to generate accurate reports, forecast market trends, and facilitate well-informed investment decisions.

Social media & customer relationship management

Data processing is indeed indispensable in social media and customer relationship management (CRM). Popular platforms such as Facebook and Twitter rely on data processing to curate and personalize content for users, while analysis of user behavior and interactions allows for targeted marketing campaigns.

Data processing enables businesses to segment their customers, track their preferences and interactions, and tailor their marketing efforts accordingly. Furthermore, CRM tools with advanced data processing capabilities can provide real-time insights that help companies improve customer retention and growth.

Convert raw data into action with data processing

Data processing is the key to unlocking the potential of raw data, transforming it into the knowledge that shapes our future. By systematically analyzing and interpreting data, organizations gain critical insights that inform strategic decisions, streamline processes, and drive innovation.

As the volume and complexity of data continue to expand, the ability to understand and effectively process it will become even more essential for success in a data-driven world.

Fighting back against chargeback fraud

David Loesch — Wed, 12 Jun 2024 05:00:00 GMT

The odds are that if you’re reading this, you’re part of an online retailer team facing chargeback credit card fraud, and it's becoming more than just a nuisance.

It may not even be part of your core job description, but nevertheless, here we are. Let’s cover the what and why of this type of scam and then dive in on exactly how Fullstory can make a difference.

What is chargeback fraud?

For the most part, chargeback fraud is a crime of opportunity where a consumer looking to get a product or service for free submits a fraudulent less-than-truthful chargeback request with their banking institution. The bank then reimburses the consumer for the purchase and places the burden squarely on the shoulders of the online merchant to prove the validity of the purchase. And they don’t make it easy.

Why is this a problem?

Initially, banking institutions needed to develop a way to protect consumers from shady online merchants and instill purchasing confidence. Banks allow consumers to request a chargeback against the merchant if the product they ordered online wasn’t as described, damaged, didn’t arrive, or ordered by mistake. There are many reasons why a person could call their bank or credit card company and ask for their money back.

What makes this a crime of opportunity is that, today, a growing number of consumers are scamming the system by making frivolous chargeback claims in the hopes that they will keep the product they purchased and get their money back. This puts the merchants on their heels with little wiggle room to fight back. Fighting a chargeback claim is often more costly to the merchant than allowing the consumer to get a free product leaving online retailers with a “that’s just the cost of doing business” mentality. This is called friendly fraud, the number one type of chargeback scam that merchants face today.

But wait, that’s not all! Not only is the merchant now out of revenue and inventory, but there’s a whole laundry list of other fees, penalties, and consequences the merchant faces as a result. In fact, according to Mastercard, it's estimated that merchants “incur $15 to $70 in operational costs for every card dispute.” Not to mention the ding against the merchant's risk profile from credit card payment providers like Visa and Mastercard. Not a great outcome.

How can Fullstory help?

Fullstory captures every interaction users take within your site and notates that event. From those notations, we can recreate the series of events into a picture-perfect session replay. These events are also searchable at scale, so you can visualize your data within Fullstory to uncover insights and trends.

Immediate relief from chargeback fraud

For teams that are just getting to grips with fighting “friendly” fraud, here’s how Fullstory can quickly and easily provide the evidence that banks are looking for.

Type in a Transaction ID for the product or service purchased (or any other key identifier for the transaction), snap a screenshot of the session replay at the point when the user completes the purchase (or, depending on the chargeback code, provide relevant evidence like product description or the user clicking on the Terms and Conditions box), and then send it to your bank.

It's that easy! Many of our customers, such as Brightline Trains and Goldbelly Foods, have successfully used Fullstory in this way to streamline the process and achieve swift resolutions.

Long-term solutions

For teams that are ready to take on fraud in a more proactive approach, there’s good news! Fullstory customers can now have all the incredibly detailed behavioral event data synced directly to their warehouse using Data Direct. What does this mean exactly, and why should we care? Well, for one, it means your data engineering team doesn't need to spend hours transforming the data into a usable format. Fullstory delivers data in a ready-to-go JSON format alongside DBT templates to help get you started accessing the data synced every hour.

OK, cool, but what can we DO with that data?

Well, the answer here is seemingly endless possibilities. Perhaps you’re working on generating predictive score modeling to know which users accessing your site have a high-purchase intent or which users act in ways that indicate fraudulent behavior. Even if you don’t know exactly who the user is, you may know things about them, like if they stayed on that blog page for more than 5 minutes and scrolled to the bottom (maybe part of a “Reader” behavioral group, for example). Or perhaps a user conducted multiple searches while on your site; we could categorize this user as a “Searcher.” Knowing user behavioral data could then unlock new ways to curate their experience.

What if we could update their experiences dynamically in real time?

Let’s look back at our original topic (but not limited to!). What if we have a user on our site indicating behavioral patterns associated with fraudulent behavior? What if we could trigger an additional security check to help confirm if this is a bad actor or a legitimate customer without adopting a “blanket” approach? The same could be said for customers showing high purchase intent but deviating from the checkout flow; perhaps we could tease a coupon code just enough to entice the user to return and complete the checkout flow.

Leveraging our API, we can get this information in real time! This means that your team can trigger and automate custom experiences at scale!

Key takeaway

Behavioral data adds a critical layer of insight to your existing customer knowledge. It captures the nuances of customer actions and interactions on your platform. By understanding these behaviors, your team can craft highly personalized experiences that reduce friction and ensure a superior digital journey. This deep understanding of customer behavior is critical to maintaining happiness and loyalty among your users, ultimately delivering an outstanding digital experience.

Best web analytics services for behavioral data insights

The Fullstory Team — Tue, 04 Aug 2026 04:00:00 GMT

Your dashboards can tell you that your bounce rate went up and conversions went down, but not why. That's the disconnect most website analytics tools leave open, especially for enterprise teams running complex web and mobile experiences where a dozen small friction points can hide behind one bad week of numbers.

This guide looks at web analytics services through that missing behavioral depth layer. We'll cover what these tools capture, where traffic and product analytics tools fall short, and how to evaluate a platform built to explain the why behind your data.

What are web analytics services?

Web analytics services capture, structure, and surface data about how users interact with a website or digital product. Businesses use that data to improve website performance, increase conversions, and make more informed decisions. Most services fall into one of three categories:

Traffic analytics: page views, traffic sources, and bounce rates (Google Analytics, Adobe Analytics)
Product analytics: funnel analysis, retention, and event tracking (Amplitude, Mixpanel)
Behavioral analytics: full behavioral context, friction signals, and AI-powered insights at the session level (Fullstory, Contentsquare, Glassbox)

Behavioral analytics pulls from that same traffic and product data, but connects the dots between what happened and why, instead of leaving you to piece it together yourself.

Why behavioral data matters for enterprise teams

The connection between what happened and why is where enterprise teams see the biggest payoff. Behavioral data shows exactly where users hesitated, the moments that confused them, and the paths that finally worked.

That's a stronger signal for measuring what's actually driving results, refining SEO and content decisions, and understanding an audience beyond traffic numbers, especially when it's real-time data instead of a delayed report.

Behavior analytics tools put that behavioral context to work on issues like a checkout flow losing conversions or an onboarding step causing drop-off, tracing each back to what actually happened on the page.

Best web analytics services overview

Enterprise teams typically mix traffic analytics, product analytics, and behavioral analytics, and the right combination depends on how much context you need behind the numbers.

Tool	Category	Best for	Behavioral depth
Fullstory	Behavioral analytics	Enterprise teams connecting high-resolution behavioral data to business outcomes	High: Fullcapture indexes every interaction automatically, server-side
Contentsquare	Behavioral analytics	Enterprise page-level journey and zone analysis	Medium: zone-based, acquisition-built data model
Glassbox	Behavioral analytics	Regulated industries requiring compliance-grade behavioral data	High: strong compliance controls and mobile depth
Quantum Metric	Behavioral analytics	Retail and financial services quantifying revenue impact of UX issues	Medium-high: strong anomaly detection and revenue quantification
Heap	Product analytics	Teams wanting retroactive event data without manual tagging	Medium: autocapture, limited high-resolution behavioral data
Amplitude	Product analytics	Product teams running funnel and retention analysis	Medium: strong on funnels, limited page-level behavioral context
Mixpanel	Product analytics	Self-serve product teams running event-based analytics	Low-medium: instrumentation-dependent, no behavioral capture
Pendo	Product analytics	Product-led growth teams tracking in-app behavior and adoption	Low-medium: in-app focus, limited web behavioral depth
LogRocket	Product analytics	Engineering teams debugging frontend errors and performance issues	Medium: strong for devs, limited for product/UX teams
PostHog	Product analytics	Developer-first teams wanting open-source data ownership	Medium: near real-time capture, engineering-heavy setup
Google Analytics	Web/traffic analytics	Baseline traffic and audience reporting	None: traffic-level only
Adobe Analytics	Web/traffic analytics	Enterprise marketing teams running multi-channel attribution	Low: traffic and attribution focus
Hotjar	Lightweight UX	Entry-level heatmaps and qualitative feedback	Low: shallow quantitative analysis, no real-time data
Matomo	Open-source	Teams requiring self-hosted, privacy-first analytics	Low: traffic-focused with basic behavioral features
Datadog	Monitoring	Engineering teams unifying digital experience monitoring with infrastructure	Low: monitoring-first, behavioral analytics is secondary

1. Fullstory

Best for: Enterprise digital and product teams that need high-resolution behavioral data connected to AI-powered insights

Fullstory is a behavioral data platform that gives your team deep insight into how users interact with their digital products. Fullcapture is the foundation, automatically indexing every interaction across web and mobile with no manual tagging, and its full retroactivity means teams can analyze past sessions for insights they didn't think to track at the time.

Most tools rely on manual instrumentation or limited autocapture, so missing data weakens the AI insights built from it. StoryAI avoids that problem, drawing on Fullcapture's complete context to surface friction points, summarize sessions, and answer plain-language questions without an analyst in the loop.

Privacy by Default masks sensitive data at the source, including on mobile, so depth never comes at the cost of compliance.

Key features:

Fullcapture: High-resolution, server-side behavioral data capture across web and mobile; no manual tagging; full retroactivity
StoryAI: AI agents that surface summaries, answers, opportunities, and predictions; built on the full behavioral dataset
Enhanced heatmaps: Auto-configured; no setup required when pages change
Sentiment signals: Surfaces rage clicks, dead clicks, error clicks, and thrashed cursors; fully automatic
Fullstory Anywhere: Exports structured behavioral data to your data warehouse; supports BI and advanced modeling

Pros

Comprehensive insights into user behavior across web and mobile
Unified behavioral data in a single platform, with no separate tools or data models to stitch together
Privacy-first approach that protects sensitive data by default

Cons

Feature-rich platform that may take new users time to fully explore
Custom pricing, not published upfront

Pricing

Fullstory offers a free tier and custom pricing for its Business and Enterprise plans. It stands out for its ability to understand and visualize customer interactions in a granular, user-friendly way.

Review

Fullstory is an insightful, user-friendly tool that has greatly impacted quoteline improvements at Everyday Insurance. We receive unparalleled support from the CSM team - shout out to Alana Martino-Burke for going above and beyond to help with configuration and analysis. There are always new features demonstrating that the platform is industry-forward and constantly reviewing its usability. I recommend Fullstory as a behavioural tool to improve product, optimisation, compliance and QA.
- Shantelle L., Verified G2 Review

2. Contentsquare

Best for: Enterprise teams analyzing page-level zone behavior and customer journeys

Contentsquare is a direct competitor to Fullstory in the enterprise behavioral analytics space, with zone-based heatmaps and journey analysis as its main strengths. But years of acquisitions (Heap, Hotjar, Clicktale) have left it with a fragmented data model: features don't share a consistent structure, and AI can't apply retroactively to data nobody configured it to track from the start.

Its capture also depends on instrumentation that can miss dynamic changes, so single-page apps, route changes without a URL update, and modal windows often need manual configuration to track correctly. Some users report data accuracy issues and longer integration timelines

If a simpler, less fragmented setup matters more than an all-in-one suite, check out our Contentsquare alternatives guide for options.

Key features:

Zone-based heatmaps
Customer journey analysis
Friction scoring, AI-assisted insights
Revenue attribution per zone

Pros

Wide enterprise adoption and long-standing brand recognition
Legacy Heap product analytics combined with behavioral data in one suite
Broad documentation and support resources for common implementation issues

Cons

Acquisition-built architecture with inconsistent data models across features
Manual configuration needed for SPAs, route changes, and modals
Data accuracy issues and longer integration timelines reported by some users

Pricing

Contentsquare runs four tiers:

Free: $0/month
Growth: $39/month (billed annually)
Pro: Custom pricing
Enterprise: Custom pricing

Review

Contentsquare gives us deep insight into how visitors interact with our website through session replays, heatmaps, and journey analysis. The interface is intuitive and makes it easy to spot usability issues, refine user journeys, and make data-driven decisions. Overall, it helps us improve the customer experience and strengthen our website’s performance.
- Kunal D., Verified G2 Review

3. Glassbox

Best for: Regulated industries requiring compliance-grade behavioral data

Glassbox targets financial services, insurance, and regulated retail, and its ISO 27001 and SOC 2 Type II certifications verify its data handling meets industry security standards. Struggle detection, session replay, and funnel analysis all build on that compliance-grade handling, including an SDK for capturing mobile sessions without hurting app performance.

That focus makes it narrower than Fullstory as a general-purpose enterprise behavioral analytics platform. It's also sales-led, with no public pricing or free plan, so getting an actual quote takes a conversation.

Key features:

Compliance-grade session replay with audit trail
Struggle detection
Native mobile masking
Funnel analysis and journey mapping

Pros

Deep compliance credentials trusted by regulated industries
Struggle detection that ties friction directly to business impact
Strong reputation among financial services and insurance customers

Cons

Narrower fit outside regulated industries
No published pricing, requiring a sales conversation for a quote
No free plan or self-serve trial

Pricing

Glassbox prices custom quotes based on three factors: the package selected, data retention period, and session volume across web or app. There's no published pricing or free plan, so getting a number requires contacting sales directly.

Review

Like utilizing Glassbox for its ease of use and intuitive interface, which makes it an essential tool for analysis and investigation. It offers comprehensive session replay capabilities, allowing for detailed tracking of user behavior and interactions on the websites. It also supports deep insights into customer journeys to identify customer issues more effectively.
- Anna Marie C., Verified G2 Rating

4. Quantum Metric

Best for: Enterprise retail and financial services teams quantifying the revenue impact of UX issues

Its anomaly detection surfaces behavioral pattern changes automatically and quantifies what they're costing, giving retail and financial services teams a dollar figure to justify fixes with.

It sits at a similar position and price point to Fullstory in the enterprise market. The trade-off is accessibility: implementation is complex enough that it typically requires dedicated analyst resources, whereas Fullstory's StoryAI is built for self-serve use by non-technical teams.

Key features:

Session replay and journey analysis
Automated anomaly detection
Revenue quantification tied to UX issues
Funnel analysis

Pros

Anomaly detection tied directly to revenue impact
Strong track record in retail, financial services, and travel
Real-time monitoring that catches issues as they happen

Cons

High implementation complexity, often requiring dedicated analyst support
Custom, enterprise-scale pricing only
Steeper learning curve for non-technical teams

Pricing

Custom quote, based on data volume, seats, retention, and add-ons

Review

What I like most about Quantum Metric is the ability to clearly see customer journeys and flows end-to-end. It makes it much easier to understand how users actually interact with the site, where they drop off, and what might be causing friction. The session replay feature is particularly useful, as it allows us to view real customer experiences rather than relying on assumptions. This helps our team quickly identify issues, prioritise improvements, and make more data-driven decisions.
- Verified G2 User in Leisure, Travel & Tourism

5. Heap

Best for: Teams wanting retroactive event data without manual tagging

Heap sits closer to behavioral analytics than most product analytics tools. Autocapture records clicks, page views, and form submissions from a single snippet with no upfront event planning, and retroactive analysis lets teams define new events against data they already collected, instead of waiting on future instrumentation.

Since its 2023 acquisition, Heap's future now depends on Contentsquare's product priorities rather than its own. Autocapture removes manual tagging, but it's still client-side and limited to a defined set of interactions, a real shortfall next to Fullcapture's server-side, high-resolution indexing.

Key features:

Automatic event capture without manual tagging
Retroactive funnel analysis
Session replay
User segmentation and journey analysis

Pros

Autocapture with no upfront engineering lift for event tagging
Retroactive event definition on historical data, not just going forward
Free tier available for smaller-scale use

Cons

Client-side capture that misses some interactions server-side tools catch
Roadmap now tied to Contentsquare's broader ecosystem post-acquisition
Paid tiers requiring a sales conversation, with no published enterprise pricing

Pricing

Free tier: up to 10,000 monthly sessions, 6 months of history
Growth, Pro, and Premier: custom quote

Review

I like that heap is so easy for non-technical team members to be able to create tracking events without the need for any coding knowledge or getting the engineering team involved.
- Milos D., Verified G2 Review

6. Amplitude

Best for: Product teams running event-based funnel and retention analysis

Amplitude is a strong product analytics platform, and its funnel visualization, retention analysis, and cohort comparisons are genuinely good. It's added autocapture and Session Replay in recent years, narrowing some of the distance from dedicated behavioral analytics platforms.

Insight quality still depends heavily on instrumentation and data governance. Teams commonly report that messy event taxonomies and inconsistent naming undermine funnels and segmentation months into using the platform.

Getting the most out of Amplitude's deeper features, including its AI layer, typically requires dedicated analyst support rather than self-serve use. If dedicated analyst support isn't in the budget, our Amplitude alternatives guide breaks down options built for self-serve teams.

Key features:

Event-based funnel analysis
Retention and cohort reporting
Autocapture and Session Replay
A/B testing and feature flag integration
Custom dashboards and data warehouse connectivity

Pros

High-quality funnel, cohort, and retention analysis
Autocapture and Session Replay reducing manual instrumentation needs
Strong experimentation and feature-flagging tools built in

Cons

Insight quality dependent on a clean, consistent event taxonomy
Advanced features and AI capabilities requiring dedicated analyst support
Steeper learning curve for teams without a dedicated data function

Pricing

Free (Starter): up to 10,000 monthly tracked users, 2 million events/month
Plus, Growth, Enterprise: custom, sales-led pricing at higher tiers

Review

I appreciate Amplitude Analytics' easy-to-use UI, which allows non-technical teams like design and product to access data without relying on us. The segment and funnel charts are a lifesaver for monitoring and analysis, and I find the ability to download data from a chart very helpful. I value the real-time connectivity and data flow because it's easy to integrate and ensures that events are tracked in real time, which helps reduce the turnaround time for anomaly detection. The initial setup was very easy, and we didn't face much trouble setting it up.
- Haider A., Verified G2 Review

7. Mixpanel

Best for: Self-serve product teams running event-based analytics

Mixpanel is a product analytics platform that centers on event tracking, funnels, and retention analysis. It's added autocapture and native session replay in recent years, bringing in more behavioral context without full manual instrumentation.

Its core strength is still event-based analysis: cohort building, retention charts, and self-serve funnel work that non-technical teams can run without much setup. It's a solid complement to a full behavioral analytics platform, especially for teams that want clean, self-serve reporting without a heavier, more governance-focused setup process.

If an all-in-one web analytics solution is the goal, our Mixpanel alternatives guide breaks down the best fit for that specific need.

Key features:

Event-based funnel analysis
Retention and cohort analysis
Autocapture and native session replay
Custom dashboards and user segmentation

Pros

Autocapture and session replay reducing manual instrumentation lift
Self-serve setup approachable for non-technical teams
Strong cohort and retention reporting

Cons

Less enterprise governance and schema control than Amplitude
Pricing that can scale quickly as event volume grows
Still leaning toward event analysis over full behavioral depth

Pricing

Free: $0/month
Growth: Starting at $0, scales with usage
Enterprise: Custom pricing

Review

Instant session replays, error reporting, and AI agent analysis that allows me to gain insight on usage using natural language.
- Jarolin V., Verified G2 Review

8. Pendo

Best for: Product-led growth teams tracking in-app behavior and feature adoption

Pendo automatically tracks clicks, page views, and custom events without manual instrumentation, and layers in session replay across web, iOS, and Android at its Core tier and above. What sets it apart is: in-app guides, onboarding flows, and feature adoption tracking that tie directly into that behavioral data.

That focus makes it a strong fit for SaaS teams running product-led growth motions, but it functions primarily as an adoption and guidance platform with analytics attached.

Key features:

Automatic event tracking with no manual instrumentation
In-app guides and onboarding flows
Session replay (Core tier and above)
NPS and user feedback collection
Product analytics dashboards

Pros

Automatic tracking with no upfront instrumentation work
In-app guides tied directly into behavioral and adoption data
Strong fit for teams running product-led growth motions

Cons

Session replay gated behind a paid tier
Built primarily around adoption and guidance, not general enterprise behavioral analytics
Pricing that can scale quickly with MAU volume and add-ons

Pricing

Free: $0/month
Base: Custom pricing
Core: Custom pricing
Ultimate: Custom pricing

Review

I use Pendo to check customer satisfaction scores and draw insights from them, which I find quite useful. It's great for seeing the way users engage with our platform easily and helps me analyze how users interact with the product, including the percentage of users accessing certain parts. I also use Pendo as an intercept tool for surveys, which is handy. Additionally, Pendo has really helped with onboarding by directing us to specific bottlenecks and reducing friction.
- Gonzalo M. G., Verified G2 Review

9. LogRocket

Best for: Engineering teams debugging frontend errors and performance issues

While Fullstory targets product and UX teams, LogRocket skews toward engineering. It captures console logs, network requests, and Redux/Vuex state changes alongside session replay, so a developer can reproduce a bug exactly instead of guessing from a support ticket.

Behavioral data is part of the platform, but debugging is still the core use case rather than understanding user experience at scale.

LogRocket's Galileo AI ranks issues by severity across sessions, giving engineering teams a prioritized list instead of a flat feed of bugs. Funnel analysis, cohort data, and Galileo itself all sit behind higher-priced tiers. Overall, the platform leans more technical than what product managers or CX leaders typically need for self-serve behavioral insight.

Key features:

Frontend error monitoring
Session replay with full DOM, network, and console context
Redux/Vuex state capture
Galileo AI issue prioritization and severity scoring

Pros

Deep technical context for reproducing and fixing bugs
Galileo AI ranking issues by real user impact
Strong fit for engineering-led debugging workflows

Cons

Funnels, cohorts, and Galileo AI gated behind higher-priced tiers
More technical than most product or CX teams need for self-serve use
Primarily built around debugging, not broad behavioral analysis

Pricing

Team: starting around $69 to $99/month
Professional: starting around $295/month
Enterprise: custom pricing

Review

I use the session replay and the network usage constantly. I'm a software developer, and so this really helps me debug my software. It's really useful to see the user details and scrub through the times and see how they interacted with the app. Usually a customer reports an issue, and then I have their email. I immediately go to log rocket and type in their email and then pull up their relevant session.
- Alexander F., Verified G2 Review

10. PostHog

Best for: Developer-first teams wanting open-source analytics with full data ownership

PostHog is open-source and self-hostable, with usage-based pricing that stays transparent as long as a team is willing to model its own event volume. It combines product analytics, session replay, feature flags, A/B testing, and surveys in one platform, saving engineering teams from stitching several tools together themselves.

That flexibility comes with tradeoffs: self-hosting needs dedicated DevOps, and even cloud usage rewards careful instrumentation. PostHog fits technical teams and startups wanting infrastructure control, but a lighter feature set and steep learning curve limit its scale for non-technical teams or fixed-price procurement.

Key features:

Open-source with self-hosting option
Product analytics and web analytics
Feature flags, A/B testing, and surveys
Session replay and event capture

Pros

Full data ownership with self-hosting available
Transparent, usage-based pricing with a generous free tier
Multiple tools (analytics, flags, experiments) combined in one platform

Cons

Dedicated DevOps resources required for self-hosting
Steep learning curve for non-technical teams
Usage-based pricing that's hard to predict at scale

Pricing

Free: $0/month, generous usage limits
Pay-as-you-go: Usage-based, starting near $0
Enterprise: Starting around $2,000/month base fee, plus usage

Review

What I like most about PostHog is the level of visibility it gives us into real user behavior, while still staying very developer-friendly.
Session recordings, funnels, feature flags, and the event exploration tools are especially valuable. They make it straightforward to debug issues, validate experiments, and see how users interact with new features, without needing a massive analytics setup.
- Natanael G., Verified G2 Reviews

11. Google Analytics

Best for: Baseline traffic and audience reporting

Google Analytics is the default starting point for most analytics stacks. Broad adoption, a free tier, and deep integration with the rest of the Google ecosystem make it an easy first install. Its real-time report shows active users, top pages, and traffic sources almost instantly, letting a team catch a traffic spike or broken page as it happens.

Standard reports lag 24 to 48 hours before data fully settles. And regardless of speed, Google Analytics 4 (GA4) has a clear ceiling: it shows what's happening, not why, with no friction detection or behavioral depth. It works best as the traffic layer, paired with a behavioral analytics platform for deeper insight.

Key features:

Traffic and audience reporting
Near-instant real-time active user dashboard
Goal and conversion tracking
Google Ads integration

Pros

Free tier with broad adoption and easy setup
Deep integration with Google Ads and the wider Google ecosystem
Near-instant visibility into active users and traffic sources

Cons

Standard reports lagging 24 to 48 hours before data fully settles
No behavioral depth or friction detection, traffic-level only
Requires a paired behavioral analytics tool to explain the "why"

Pricing

Free: $0/month
Google Analytics 360 (enterprise): Custom pricing

Review

Google Analytics has been the standard for free website analytics for the 2+ decades I've been in the industry. It has changed quite a bit over that time but it has always provided powerful analytical tools at no cost to the user.
- Lindsey M., Verified G2 Review

12. Adobe Analytics

Best for: Enterprise marketing teams running multi-channel attribution and traffic analysis

Adobe Analytics is an enterprise web analytics tool and attribution platform, strong for marketing teams managing complex multi-channel measurement. It captures traffic, conversion, and attribution data well, but stops short of explaining why users behave the way they do on a page or in a product.

Implementation costs run high: full deployments commonly cost $20,000 to $100,000-plus through implementation partners, on top of licensing. It fits teams already using Adobe Experience Cloud, where attribution data connects directly to Target, Campaign, and Experience Manager.

Key features:

Traffic and conversion reporting
Multi-channel attribution modeling
Audience segmentation
Adobe Experience Cloud integrations

Pros

Deep attribution across complex, multi-channel marketing programs
Tight integration with Adobe Experience Cloud (Target, Campaign, Experience Manager)
Enterprise-grade scale for high-traffic properties

Cons

High implementation cost and complexity, often $20,000–$100,000+ for deployment
Specialist support typically required to get full value
No behavioral depth, traffic and attribution focused only

Pricing

Custom pricing only, commonly starting around $48,000+ annually for enterprise implementations

Review

I like the many different visualizations in Adobe Analytics that help tell the story we're looking for. Building custom metrics to filter and dice KPIs in various ways is great too. It makes it easy to find issues or anomalies, and the interface is easy to use. Depending on the use case, we use a fallout for general funnel understanding or Freeform to get a deeper understanding. The flow visualization is interesting to see how members are getting places and where they are going. I also like the breakthrough at fallout or fall through. It's also easy to add new tags and eVars to build upon what we have.
- Talia I., Verified G2 Review

13. Hotjar

Best for: Entry-level heatmaps and qualitative user feedback

Hotjar's heatmaps, session recordings, and surveys are now live inside Contentsquare, following a merger completed in July 2025. Hotjar still serves as the entry point to that ecosystem: a lighter, cheaper way to access heatmaps and recordings before stepping up to Contentsquare's full behavioral analytics suite.

Hotjar's Sense AI surfaces friction points and summaries at the Growth tier and up, but it's still built for directional feedback, not enterprise depth. Lower tiers sample sessions instead of capturing everything, with no native mobile app.

Key features:

Heatmaps and session recordings
User surveys and feedback polls
Funnel analysis
Sense AI friction detection (Growth tier and above)

Pros

Low-cost entry point into behavioral analytics
Genuinely free tier with a generous session allowance
Simple setup for basic heatmaps and recordings

Cons

Lower tiers sampling sessions instead of capturing all traffic
No native mobile app
Migration into Contentsquare's ecosystem and pricing required to grow past entry-level

Pricing

Free: $0/month
Growth: Starting around $49/month
Pro: Custom pricing
Enterprise: Custom pricing

Review

What I like most about Hotjar is that it really allows me to understand my users. I see exactly where they get stuck, what catches their attention, and where they drop off. It transforms abstract behaviors into concrete and actionable insights. The tool is very easy to use.
- Verified G2 User in Information Technology and Services

14. Matomo (formerly Piwik)

Best for: Teams requiring self-hosted, privacy-first analytics with full data ownership

Matomo is a widely used open-source web analytics platform. It's free to self-host, with all data staying on your own servers, making it a common choice for teams with strict data residency requirements. Core traffic analytics, visitor behavior, and real-time updates come free; heatmaps, session recordings, and funnels are premium add-ons on self-hosted installs, or bundled into Matomo Cloud.

Self-hosting demands installation, server management, and ongoing maintenance, not a self-serve option for non-technical teams. Matomo is a solid choice for teams that can't use cloud-hosted platforms due to regulatory constraints, but it's not a replacement for the behavioral depth an enterprise platform provides out of the box.

Key features:

Self-hosted or cloud-hosted option
Visitor behavior analysis and real-time data
Heatmaps and session recording (premium add-ons)
Privacy compliance controls (GDPR, CCPA)

Pros

Full data ownership with self-hosting
Free core analytics with no per-event billing on self-hosted installs
Strong privacy and compliance controls out of the box

Cons

Extra cost for heatmaps, session recordings, and funnels on self-hosted installs
Technical expertise required to install and maintain
Smaller ecosystem and less behavioral depth than enterprise platforms

Pricing

Matomo offers two hosted tiers:

Business: Starting around $26/month for 50,000 hits, scaling with traffic.
Enterprise: Custom pricing

Plus a free, self-hosted On-Premise option with core analytics, where heatmaps, session recordings, and other advanced features are separate paid add-ons.

Review

It’s very easy to sort through and view data. It feels like a big improvement over other data collection platforms, especially with the ability to customise reports and customer journeys in a straightforward way.
- Michael B., Verified G2 Review

15. Datadog

Best for: Engineering teams unifying digital experience monitoring with infrastructure observability

Datadog is primarily a monitoring and observability platform; real user monitoring and session replay exist inside it as a complement, not the core product. It's a strong fit for engineering teams that want frontend, backend, and user experience data in one place.

Datadog's RUM has added heatmaps and rage-click detection alongside session replay, closing some of that behavioral distance. But it still lacks the AI-powered behavioral insights and full capture fidelity of a dedicated behavioral analytics platform, and pricing is volume-based per session, which can get expensive fast for high-traffic or highly interactive apps. Teams evaluating Datadog for behavioral analytics usually do better by pairing it with a dedicated platform than replacing one.

Key features:

Real user monitoring (RUM)
Session replay with rage-click detection and heatmaps
APM and infrastructure monitoring
Log management and synthetic monitoring

Pros

Frontend, backend, and infrastructure observability unified in one platform
Session replay now including heatmaps and friction signals
Strong fit for engineering-led, full-stack monitoring

Cons

Behavioral analytics secondary to its core observability focus
No AI-powered behavioral insights comparable to a dedicated platform
Volume-based session pricing that can scale quickly with traffic

Pricing

Free: $0/month
Pro: Starting around $15/host/month
Enterprise: Starting around $23/host/month
RUM & Session Replay: Priced separately, starting around $1.50–$1.80 per 1,000 sessions/month

Review

I like how easy datadog is to integrate with existing systems. Once set up it provides an incredibly useful view into the status and state of application health. It's dashboards are very easy to create and are a valuable method for gathering key information all in one place.
- Gunther C., Verified G2 Review

How to evaluate web analytics services

A few questions worth asking before choosing:

How does the platform capture data? Manual instrumentation, autocapture, or high-resolution behavioral capture all lead to different outcomes, and the answer determines what your AI can actually work with.
Can you answer questions retroactively? If a platform can't analyze behavior from before you thought to track it, you lose access to that context permanently.
What dataset powers the AI layer? A full behavioral record produces different insights than one built on predefined metrics alone.
Does mobile behavioral depth match web? Ask for a direct demo of mobile capture and masking rather than a general compliance claim.
What's the total cost of ownership beyond licensing? Implementation time, instrumentation maintenance, professional services, and engineering overhead all affect the real cost.
Can a product manager or CX leader reach a meaningful insight without filing a ticket? Self-serve access changes how fast a team can act on what it finds.
Can behavioral data move to your warehouse, CRM, or marketing tools? Data that stays siloed limits what the rest of the business can do with it.

Find the behavioral layer your analytics stack is missing

Analytics without behavioral depth leaves friction undetected and revenue bleeding from checkout flows. Adding more disconnected tools doesn't solve that, it just means paying for more dashboards without getting any closer to an answer.

Fullstory helps you find those answers, using a single script and one data model to automatically index every interaction. StoryAI draws on that same enriched customer data to surface friction points and answer plain-language questions without an analyst in the loop. Fullstory gives your team everything in one platform, so you stay focused on finding the solution, not managing tools.

Best CRO tools for ecommerce: A behavioral data view for 2026

Fullstory — Mon, 31 Aug 2026 07:00:00 GMT

A few years back, optimizing a digital storefront meant relying on a single A/B testing tool. Today, solving conversion drop-offs is a stack problem, not a single-tool problem.

If you’re seeking the best CRO tools for your ecommerce business, you already know that experimentation without behavioral data analytics at its foundation is just wasted effort. Contextless testing will take you down a rabbit hole of inconclusive outcomes that fail to recover revenue.

To reverse this, you must build a tool stack that uses behavioral data to drive your testing and personalization strategies.

Unfortunately, most “CRO software” lists treat these separate tool categories as interchangeable. In reality, different tools specialize in distinct CRO stages (behavioral analytics, experimentation, monitoring, and personalization).

To fix that, we categorized the 10 best CRO software platforms across all four stages. Then, we show you exactly how to combine them into an integrated pipeline.

What to look for in a CRO tool for ecommerce

Instead of basic traffic counters and simple A/B testing software, ecommerce conversion rate optimization (CRO) tools are now sophisticated, AI-driven platforms.

The CRO process itself is now a multi-stage discipline that spans experimentation, behavioral insight, monitoring, and personalization. Ecommerce teams that rely on a single tool often fail to see the full story, which results in inconclusive tests and failed optimizations. And as more teams lean on AI to do the heavy lifting, missing that behavioral context becomes a liability. According to a Fullstory survey, only 44% of CX teams feed behavioral data into their AI pipelines, which makes AI responses unreliable.

Developing an effective ecommerce CRO strategy requires building a stack that supports every stage of your CRO implementation. While some tools only tell you what went wrong, you also need tools that explain why it did and how you can remedy the issues—often with AI-assisted insights to prioritize and accelerate fixes.

That’s why it’s important to understand how these tools work together. The best stacks operate as a continuous feedback loop.

Here’s a sample set of questions you should ask when evaluating how each tool fits into the specific stage of your optimization program:

Experimentation stage

Can non-technical teams launch A/B tests without heavy coding?
Does the tool support both simple visual edits and backend, server-side tests?
Can we validate our test results by reviewing qualitative session replays from our behavioral analytics platform?

Behavioral analytics stage

Does the platform explain why shoppers drop off, not just where?
Can we analyze user behavior retroactively without tagging events in advance?
Can we push behavioral segments directly into our experimentation and personalization tools?

Performance monitoring stage

Does the platform detect site bugs and broken checkout flows in real-time?
Can it rank technical errors by actual revenue impact?
Will it alert our team instantly when a critical flow fails?

Personalization stage

Can the tool deliver automated, AI-driven product recommendations?
Does it recognize user exit intent to trigger timely cart recovery offers?
Can we customize content in real time based on the data captured across the rest of our stack?

Best CRO tools for ecommerce overview

Before we begin evaluating the tools in detail, here is a quick look at the top ecommerce CRO tools and their key strengths.

Platform	Best for	Key features	Scale
Fullstory	Understanding why conversions happen, not just what changed	Fullcapture behavioral data capture, StoryAI Answers and Summaries, friction detection without manual tagging, privacy-by-default architecture	All sizes
VWO	End-to-end A/B and multivariate testing	Visual editor for test creation, server-side and client-side testing, heatmaps bundled in	Mid-market to enterprise
Optimizely	Enterprise-grade experimentation programs	Multi-team experiment governance, feature flagging, integration with CMS/commerce platforms	Enterprise
Kameleoon	AI-powered testing and personalization in one platform	AI-driven audience targeting, feature testing, HIPAA/GDPR/CCPA compliance	Mid-market to enterprise
Nosto	AI product recommendations and personalization	Dynamic bundling, AI-powered merchandising, strong Shopify integration	SMB to mid-market
Adobe Target	Enterprise personalization at scale	AI-predicted "next best experience," deep Adobe Analytics integration, and audience segmentation	Enterprise
Noibu	Catching conversion-blocking errors before they cost sales	Automated error detection, revenue-impact scoring, alerts for broken checkout flows	Mid-market to enterprise
Lucky Orange	Heatmaps and session recordings for lean teams	Session replay, live chat, simple setup for smaller sites	SMB
OptiMonk	On-site pop-ups and lead capture for ecommerce	Pop-up builder, A/B testing for on-site messaging, and exit-intent targeting	SMB to mid-market
Hotjar	Heatmaps, session recordings, and feedback for growing teams	Session recordings, heatmaps, on-site surveys, and feedback widgets	SMB to mid-market

1. Fullstory

Best for: Understanding why conversions happen, not just what changed

G2 Rating: 4.5/5

While most standard analytics tools only flag issues, Fullstory stands out with its AI-powered behavioral data capture engine, Fullcapture. This tool automatically logs every click, tap, and user interaction across your web and mobile apps, so you can resolve issues faster.

It lets you stop guessing why a specific user abandoned their cart. Instead, your team can watch the corresponding session replay and identify the exact friction points. This lets them retroactively query and analyze checkout friction or unusual drop-offs long after they happen, without needing to tag events.

Fullstory’s StoryAI acts as your intelligent data analyst. It’s a system of AI agents that provides session summaries, context-rich answers, and automatically surfaces anomalies that you can resolve proactively before they cost conversions.

With these insights in hand, your team can crush revenue-blocking bugs and craft shopping experiences your customers actually enjoy.

Key features

Fullcapture: Automatically indexes everything in real-time from day one, so you don’t miss a single user event, whether it’s abandoned checkouts, page errors, or rage clicks.
StoryAI: A built-in agentic AI system that summarizes user sessions, flags friction points, and answers follow-up queries with the most relevant insights.
Journey mapping: Traces complete user paths and interactions across web and mobile apps so that you can pinpoint exact drop-off moments before the final checkout.
Private by Default: Automatically masks sensitive text and users’ personal information at the source to prevent violation of privacy laws like CCPA and GDPR.

Reviews

“I like Fullstory for multiple reasons. The tool is robust and useful to help me determine the performance of our site as well as help me identify issues before the customer expresses them. The ability to match a customer concern with a session has helped us better understand issues from the video recordings.
The MCP/Claude integration also helps me explain what I am trying to discover with assistance and guidance, which begins the process of creating metrics or segments. The interface is extremely easy to navigate and use. I find the AI insights beneficial as well.”
- Bobbi M., UX Researcher (Review on G2)

Pricing

Free: 30K monthly sessions, 12 months of analytics data retention.
Business: Designed for businesses needing deep product analysis, event measurement, and quality insights.
Advanced: Adds features like Journeys and Conversions for pattern recognition at scale.
Enterprise: Built for specialized data science teams, with SSO, role-based permissions, and multi-org management.

2. VWO

Best for: End-to-end A/B and multivariate testing

G2 Rating: 4.4/5

VWO, short for Visual Website Optimizer, is an end-to-end digital experience optimization and A/B testing platform.

With its comprehensive product analytics solutions, you can run tests on your web and mobile stores, analyze customer behavior with bundled heatmaps and session insights, and personalize content to boost conversions.

Key features

Comprehensive testing: Run A/B, multivariate, and split URL tests using a visual editor without heavy coding.
Session insights: Track user actions via heatmaps, funnel analytics, and on-site surveys.
Personalization implementation: Deliver custom web experiences based on user preferences in real-time.
Experimentation: Manage server-side testing and controlled feature rollouts, including dynamic content and personalized recommendations.

Reviews

“What I like best about VWO Testing is its intuitive interface and robust A/B testing capabilities. It makes it easy to create, launch, and analyze experiments without heavy technical involvement. The detailed reporting and visitor behavior insights help make data-driven decisions that improve conversion rates and user experience.”
- Megha A., Digital Marketing Manager (Review on G2)

Pricing

VWO offers a 30-day free trial and three premium plans with custom pricing:

Growth: Includes visual and code editor, basic targeting, and 24x5 email/chat support.
Pro: Includes advanced targeting and reporting, multivariate testing, and 24x5 phone support.
Enterprise: Includes dedicated training, all targeting and reporting options, and 24x7 support.

3. Optimizely

Best for: Enterprise-grade experimentation programs

G2 Rating: 4.2/5

Optimizely is a digital experience platform and experimentation suite built for large organizations. It’s on this best CRO software list because it helps you run A/B tests, manage content, personalize user interactions, and deploy feature flags across websites and applications using data-driven insights.

Optimizely isn’t an ecommerce tool that works in isolation. Rather, it works in tandem with data analytics platforms like Fullstory. After Fullstory provides real-time behavioral insights from user sessions, Optimizely lets you test these insights to determine what works best for your customers.

Key features

Server-side testing: Extend experiments beyond the browser into backend code, mobile apps, and APIs using cross-platform SDKs.
Feature flags: Safely toggle features on or off in production without deploying new code or affecting user experience.
Integrations: Connect to popular business tools your teams already use, including Fullstory, Microsoft, and Shopify, to exchange information at every stage of experimentation.

Reviews

“It makes testing ideas very simple and quick. It is easy to use, and you don’t need much technical knowledge to run experiments. It helps us understand what works better by using real data, so we can improve the website and give a better experience to users.”
- Sai Anusha K., Product and Marketing Data Specialist (Review on G2)

Pricing

Optimizely requires you to collaborate with its team to create a subscription plan that best meets your CRO tooling needs.

4. Kameleoon

Best for: Unified AI-driven testing and personalization

G2 Rating: 4.6/5

Kameleoon is a conversion rate optimization platform that combines client-side and server-side experimentation with AI-driven personalization into a single system.

It earns its keep on this best conversion rate optimization tools list by enabling you to run data-driven tests and deliver individualized experiences to targeted cohorts. It essentially bridges the testing and personalization stages of your CRO program.

Key features

AI-driven targeting and personalization: Automatically segments audiences and delivers real-time personalized experiences driven by user behavior and CRM data.
A/B and multivariate testing: Ideate, build, and configure A/B tests and design variations using natural language text prompts.
Privacy compliance: Track and analyze user behavior anonymously with end-to-end encryption to comply with major privacy laws, including HIPAA, GDPR, and CCPA.

Reviews

“Kameleoon is an optimization solution that allows me to be completely autonomous in setting up and launching A/B tests / customizations to improve site navigation; notably thanks to PBX. In just a few minutes, simply by talking with the AI, I can turn ideas into tests. It is a real time saver and very easy to use. The results of the first tests launched via PBX are convincing.”
- Phane D., Data Analyst (Review on G2)

Pricing

Kameleoon offers a free trial for its PBX (Prompt-Based Experimentation) Starter plan, which lets you build up to three experiments. You can also subscribe to its paid plans:

PBX Starter: Starts from $495/month. Allows up to 10 experiments and 50,000 visitors per month. Includes natural language test creation, sequential testing, and other features.
Enterprise: Custom quote based on team requirements. Includes unlimited experiments, unlimited traffic, and advanced capabilities, such as feature management and personalization.

5. Nosto

Best for: AI product recommendations and personalization

G2 Rating: 4.6/5

Nosto is an AI-powered commerce experience platform (CXP) designed to help online retailers deliver personalized shopping experiences across web, mobile, and email. It tracks user behavior to automate product recommendations, search results, category merchandising, and targeted content.

Although Nosto offers some of the best personalization tools, you will still need to add analytics and experimentation platforms such as Fullstory and VWO to your conversion rate optimization software stack.

Personalized experiences only work if they are derived from regular user behavior analysis, variation testing, and site performance monitoring.

Key features

AI product recommendations: Display automated, real-time product suggestions (like “frequently bought together” or personalized selections).
Category merchandising: Automate product sorting on category pages by using rules based on product characteristics and performance metrics.
Personalized search and discovery: Use natural language processing (NLP), vector search, and typo correction to deliver relevant search results based on shopper intent.

Reviews

“The ability to schedule content ahead of time has really helped our team keep up with our busy marketing calendar. Placing content wherever we need in our template has helped us avoid additional development hours. Nosto content also loads fast and does not slow down our pages.
It is rare that we get stuck in a spot with no solution, but when it happens, our customer success manager … is always swift to help us troubleshoot and implement solutions quickly. Communication is quick, and we feel supported.”
- Jeffrey R., Senior Manager of Ecommerce (Review on G2)

Pricing

Nosto doesn’t offer pricing tiers, but its subscription includes a base platform fee and a fixed fee based on business volume, modules selected, and the level of support and scalability required. You can choose from two product suites:

Product: Includes personalized search, category merchandising, recommendation engine, and more.
Content: Includes A/B testing, content personalization, and more.

6. Adobe Target

Best for: Enterprise personalization at scale

G2 Rating: 4/5

Adobe Target is an enterprise-level experimentation and personalization platform within Adobe’s broader offering, the Adobe Experience Cloud. It enables businesses to run A/B and multivariate tests, as well as AI-driven personalization campaigns across websites, mobile apps, and other digital channels to maximize user engagement.

If you’re already using Adobe Analytics or Experience Cloud, Target is an excellent alternative.

However, many organizations use Target alongside behavior analytics platforms such as Fullstory. It’s because you can feed Fullstory’s diagnosis of user friction and drop-offs to Target’s experimentation engine to simultaneously test multiple page variations and personalizations in real-time.

Key features

A/B and multivariate testing: Compare different variations of web pages or conversion elements to see which version drives the best conversion rates.
AI-driven personalization: Use machine learning algorithms to deliver same-page or next-page personalizations based on user interaction analysis.
Adobe ecosystem: Integrate your Target environment with Adobe’s other offerings within its Experience Cloud, including Analytics and Experience Manager, to share unified visitor profiles, custom segments, and content.

Reviews

“I really value how easy it is to set up AB testing, making it possible for me to launch experiments both quickly and efficiently. The initial setup process is straightforward, and as I continue to use the platform, I find it becoming an even more dependable tool. Customer support is always accessible and very helpful, providing effective solutions whenever I encounter any issues.”
- Stephen D., Software Development Engineer (Review on G2)

Pricing

Adobe offers customized pricing for its Target ecommerce conversion rate optimization tool. Final pricing is based on several factors, such as the products chosen, volume of digital properties, and personalization channel requirements.

7. Noibu

Best for: Catching conversion-blocking errors before they cost sales

G2 Rating: 4.6/5

Noibu is an ecommerce analytics and monitoring platform designed to detect website bugs and performance issues, as well as tie them to their impact on conversions.

With Noibu, your product, engineering, and UX teams can identify errors, monitor site performance, analyze web pages, and prioritize fixes to optimize customer experiences. It aligns your technical and business teams by translating technical errors into dollar losses and surfacing insights to improve CX.

Key features

Error detection: Automatically tracks JS, HTTP, and API issues, and categorizes them into actionable errors.
AI-assisted bug prioritization: Identifies conversion blockers at every stage of the user journey and ranks them based on the revenue they're costing the store.
Instant alert system: Notifies teams of issues, such as broken checkout and payment flows, in real-time to enable quick page-level optimizations.

Reviews

“I find Noibu incredibly easy to use. The team is consistent at updating the user interface for better ease of use. The homepage quickly informs us if our website is working correctly, showing that no pages have dropped. The ease of use helps us be more efficient, allowing us to sift through data quickly and focus on other work.”
- Nicholas S. (Review on G2)

Pricing

Noibu doesn’t publish pricing tiers on its site. You can contact its team to book a demo and get custom pricing quotes based on traffic and the scope of the modules.

8. Lucky Orange

Best for: Heatmaps and session recordings for lean teams

G2 Rating: 4.6/5

Lucky Orange is an ecommerce conversion rate optimization tool that enables small-to-medium businesses to quickly analyze user behavior on their online stores.

It shows website owners why visitors aren’t converting by tracking user actions like clicks, taps, and scrolling using heatmaps and session recordings, and without a heavy enterprise setup.

The one drawback of Lucky Orange is that it doesn’t support mobile apps. With many businesses running their store simultaneously on websites and mobile apps, not having this feature means lost opportunities to optimize mobile apps for better user experiences.

Key features

Heatmaps: See where users click, where they don’t, and what part of the page interests them most.
Session recordings: Watch how users interact with the website, where exactly they stop scrolling, and when they decide to leave the page.
Live chat and surveys: Have a conversation with your visitors right when they’re going through a page, and show a quick survey pop-up if they exit without buying.

Reviews

“Lucky Orange has made it easy to track how users navigate our website, including which events they interact with and which products they click on or view the most. It has helped us prioritize our top-selling products and use our ad spending more effectively, instead of relying on guesswork like we did before using Lucky Orange.”
- Morrison O., Business Owner (Review on G2)

Pricing

Lucky Orange comes with a free 7-day trial, after which you can choose one of the following plans based on your volume requirements:

Build: $39/month for up to 3,500 monthly sessions.
Grow: $89/month for up to 10,000 monthly sessions.
Expand: $249-$739/month for 50,000 to 200,000 monthly sessions, depending on the choice selected from the dropdown menu on their pricing page.
Scale: $1,049- $2,289/month for 300,000-700,000 monthly sessions, depending on the option selected from the dropdown menu on their pricing page.
Enterprise: Custom pricing plan starting at 700,000+ monthly sessions.

9. OptiMonk

Best for: On-site pop-ups and lead capture for ecommerce

G2 Rating: 4.8/5

OptiMonk is another conversion rate optimization software platform that primarily promotes itself as an AI pop-up builder.

With this no-code CRO tool, your marketing team can capture leads, reduce cart abandonment, and increase average order value using targeted pop-ups, sticky bars, and gamified overlays without involving developers.

OptiMonk’s chief focus is on conversion rate optimization through overlays, personalized targeting, and A/B testing. But it leaves out the crucial behavioral analytics features, such as heatmaps and session replays, without which you are left guessing why a campaign succeeded or failed.

Pairing it with behavioral insight software like Fullstory ensures you understand the actual user behavior before launching your next set of pop-ups.

Key features

AI-driven campaign builder: Use AI and no-code editors to generate on-brand pop-ups, copy, and product recommendations quickly.
Exit-intent and abandoned-cart targeting: Recognize user abandonment signals to trigger last-chance offers and free-shipping reminders, which significantly reduce customer drop-offs.
A/B testing: Experiment with varying pop-ups, product layouts, and messaging techniques to understand what works best for keeping customers.

Reviews

“I use OptiMonk for abandoned cart campaign pop-ups and AI-generated content and images for product descriptions. I can easily create pop-ups for email and phone number collections without needing any code, which is really convenient. The best part is, I can create gamified pop-ups that encourage customers to submit their emails and phone numbers, which boosts sales and helps me increase my email/phone number list for marketing campaigns. Additionally, the AI-generated content and images in the product descriptions are awesome. The initial setup of OptiMonk was easy.”
- Prahlad K., Head of Operations and Supply Chain (Review on G2)

Pricing

You can use OptiMonk for free for up to 10,000 pageviews per month and scale to their paid plans as your business requirements grow:

Essential: Costs $59/month for up to 50,000 monthly pageviews; suitable for new businesses.
Growth: Costs $174/month for up to 250,000 monthly pageviews; suitable for fast-growing businesses.
Premium: Costs $374/month for up to 1,000,000 monthly pageviews; suitable for high-performance businesses.
Master: Custom pricing plan based on required monthly pageviews; suitable for enterprises with very high traffic.

[Note: OptiMonk counts a pageview every time a web page is loaded, regardless of whether a campaign is displayed.]

10. Hotjar

Best for: Heatmaps, session recordings, and feedback for growing teams

G2 Rating: 4.3/5

Hotjar, now acquired by Contentsquare, is a user experience insights platform that helps marketers analyze user behavior on their websites. It includes heatmaps, session recordings, and customer survey tools within a lightweight conversion rate optimization software package.

Focused on smaller, budget-constrained ecommerce teams, Hotjar integrates with tools like Google Analytics and Mixpanel. While these tools provide page metrics like bounce rates and conversion rates, Hotjar helps you understand why users behave a certain way and how to optimize their shopping experience.

Key features

Heatmaps and session recordings: See how your users engage with your web pages, which parts of the page they click, and exactly where they struggle to complete their order.
Customer feedback: Launch AI-powered customer surveys and link the responses to corresponding user sessions to understand why they left the website before buying.
Journey mapping: Track how users move through the purchase funnel from beginning to end to identify frustration signals and opportunities for delivering optimized journeys.

Reviews

“What I like most about Hotjar is how easy it makes it to understand user behavior without spending hours digging through data. The session recordings and heatmaps make it straightforward to spot friction points, validate assumptions, and prioritize the right improvements. The interface feels intuitive, the setup is quick, and it’s become an essential part of our workflow for continuously improving the user experience.”
- Natanael G., Sr. Software Engineer (Review on G2)

Pricing

Hotjar’s freemium platform is integrated into Contentsquare’s free-forever plan, which gives you access to basic Hotjar features, including heatmaps, session replays, and surveys. Alternatively, you can upgrade to the Contentsquare premium plans as your requirements grow:

Growth: Starts at $49/month. Includes 7k+ monthly sessions and replays, access to two months of session replays, journey analysis, and more. Price scales with specific products and the volume of monthly sessions.
Pro: Custom pricing. Includes 1m+ monthly sessions, AI-powered session summaries, and more. Price scales with specific products and the volume of monthly sessions.
Enterprise: Custom pricing. Includes custom monthly sessions, experience monitoring, AI-driven error summaries, and more.

How to build a CRO stack

Instead of basic traffic counters and simple A/B testing software, ecommerce conversion rate optimization (CRO) tools are now sophisticated, AI-driven platforms.

The CRO process itself is now a multi-stage discipline spanning four core areas:

Behavioral insight
Performance monitoring
Experimentation
Personalization

Ecommerce teams that rely on a single tool often fail to see the full story, which results in inconclusive tests and failed optimizations.

And as more teams lean on AI to do the heavy lifting, missing that behavioral context becomes a liability. According to a Fullstory survey, only 44% of CX teams feed behavioral data into their AI pipelines, which makes AI responses unreliable.

To pull off an effective ecommerce CRO strategy, you need a software stack that supports every step of the process. While some tools only tell you what went wrong, you also need tools that explain why it did and how you can remedy the issues—often with AI-assisted insights to prioritize and accelerate fixes.

That’s why it’s important to understand how these tools work together to form a continuous feedback loop.

Most teams don’t fail because of the wrong tools; they fail because they use tools in silos. If you’re only testing assumptions based on gut feelings—without first understanding what your customers actually need—you’re just playing trial and error.

“Funnel analysis without behavioral context creates the sharpest trap because it gives you a precise location without a usable explanation. You can see the exact step where users leave a defined path, but the data doesn’t tell you what they tried, what confused them, or what failed to respond. Teams then fill the gap with hypotheses, and A/B tests end up validating guesses that session-level evidence could have narrowed much earlier.”
- The Fullstory Expert Team

Winning ecommerce teams own a conversion rate optimization software stack that works step-by-step, with tools complementing each other:

Step 1: Data gathering and user behavior research

Purpose: This stage diagnoses user friction to explain why visitors aren’t converting.

To understand what users do and why, start by gathering quantitative and qualitative data.

Quantitative data (pageviews, traffic sources, drop-off/conversion rates) shows you “what’s happening” in numbers. However, numbers only reveal part of the truth.

Qualitative behavioral data shows you the full picture (the “why” and “how”) by answering questions like:

Why do visitors leave a page without buying?
Where do users click, scroll, or lose interest on a page?
What part of the checkout process causes stress?
How many unique visitors are impacted by a specific bug?
What on-page friction drove a specific audience segment to leave frustrated feedback?

Skipping behavioral analysis results in experiments based on wild assumptions instead of concrete data points. Platforms like Fullstory, Lucky Orange, and Hotjar help you make informed testing decisions.

With features like heatmaps, session replays, and AI-powered insights, you can identify user friction, quantify the revenue impact of anomalies, and prioritize conversion-boosting fixes.

Checklist to evaluate behavioral analytics platforms:

Does the platform explain why shoppers drop off, not just where?
Can we analyze user behavior retroactively without tagging events in advance?
Can we push behavioral segments directly into our experimentation and personalization tools?

Step 2: Performance monitoring

Purpose: This stage ensures your conversion funnel remains functional by catching technical errors in real-time.

Behavioral data alone doesn’t determine whether a test succeeds. If technical bugs like page errors, failing payment gateways, and JS/API failures are silently killing conversions, testing would be meaningless.

A dedicated monitoring layer ensures you’re optimizing a functional funnel rather than testing a broken one:

Detect errors automatically: Deploy monitoring tools like Noibu to catch page errors, broken checkout buttons, and third-party script crashes.
Prioritize by impact: Rank technical errors based on the actual revenue they cost your store.
Trigger instant alerts: Notify engineering teams immediately when critical flows (payments, checkouts) break down.

Continuous website monitoring allows your teams to prioritize fixes that protect baseline conversions and keep the rest of your optimization stack running smoothly.

Checklist to evaluate performance monitoring tools:

Does the platform detect site bugs and broken checkout flows in real-time?
Can it rank technical errors by actual revenue impact?
Will it alert our team instantly when a critical flow fails?

Step 3: Forming and testing hypotheses

Purpose: This stage involves testing hypotheses to validate which site changes improve conversion rates.

Once you know your site is functioning flawlessly and have enriched customer behavior explaining why users struggle, it’s time to build and run measurable tests:

Form data-backed hypotheses: Use friction points to create tests, “If we [change], then [result], because [reason].”
Prioritize: Rank tests based on the PIE (potential, importance, and ease) or ICE (impact, confidence, and ease) framework.
Execute: Use an experimentation tool on this best CRO tools for ecommerce list (VWO, Optimizely, or Kameleoon) to set up A/B or multivariate tests.
Measure: Let the experiment run until you achieve statistically significant results.

Hypotheses rooted in behavioral analytics yield higher win rates and lower wasted spend. A data-driven hypothesis says, “Because 70% of users drop off at this form field, let’s simplify it to raise conversions,” rather than blindly guessing with button colors.

Checklist to evaluate experimentation software:

Can non-technical teams launch A/B tests without heavy coding?
Does the tool support both simple visual edits and backend, server-side tests?
Can we validate our test results by reviewing qualitative session replays from our behavioral analytics platform?

Step 4: Personalization

Purpose: This stage scales your wins by delivering tailored, dynamic content to your new and returning store visitors.

Once you identify a winning experience, you can scale those learnings by delivering personalized user journeys to specific audience segments. Personalization is the final layer of the stack, turning a static site into a dynamic, high-converting storefront:

Automate recommendations: Use AI-driven platforms like Nosto or Adobe Target to deliver dynamic product suggestions based on user intent.
Target exit intent: Deploy platforms like OptiMonk to detect abandonment signals and trigger last-second cart-recovery offers.
Customize in real-time: Adapt your on-site messaging and web pages to reflect user expectations captured in the previous CRO stages.

Targeted personalization ensures you never settle for a single, generic variation. By delivering customized user journeys based on different audience segments, you can maximize the return on your teams’ CRO efforts.

Checklist to evaluate personalization tools:

Can the tool deliver automated, AI-driven product recommendations?
Does it recognize user exit intent to trigger timely cart recovery offers?
Can we customize content in real time based on the data captured across the rest of our stack?

Benefits of a behavioral data-informed CRO stack

A data-informed CRO stack replaces guesswork with a roadmap based on real-time user actions. Qualitative insights (like heatmaps, replays, and AI session summaries) uncover exact friction points and turn isolated experiments into a continuous feedback loop.

Building your CRO strategy on a behavioral data foundation sets you up for several wins that keep multiplying over the long term including:

Faster, higher-confidence test prioritization: By understanding exactly where and why users get stuck, you can prioritize what to test and fix first. Behavioral data provides a kind of certainty that you’re optimizing user journeys with a higher probability of improving your conversion rate.
Fewer wasted test cycles: Running speculation-based tests for months often results in wasted time and resources. Behavioral context explains why a test won or lost, so you never start new experiments from scratch.
Instant, actionable insights: Capturing every user interaction automatically, without time-consuming manual tagging, means your teams can immediately detect friction signals. AI-generated session summaries further cut down the time from insight to corrective action.
Better cross-team alignment: Using tools in silos risks multiple interpretations of the same metrics. When tools work together, they generate a single source of truth. This allows product, engineering, and CX teams to share the same visual evidence and avoid misinformed decision-making.

Solving conversion drop-offs is a stack problem, not a single-tool problem. When you stop looking for a magic fix and connect a behavioral analytics platform with your experimentation and personalization tools, you eliminate the guesswork.

The stack of interconnected conversion rate optimization tools is what transforms your scattered data into cohesive, high-converting digital experiences.

Choosing the best CRO tools for ecommerce

No single tool does it all. You need an integrated stack of the best CRO tools that clarifies the “why” behind user behavior and ensures your site is functional before you run any tests or offer personalizations.

Fullstory stands out among the behavior and experience analytics platforms for this very reason. It captures entire user sessions with filtering and querying capabilities, letting your team discover the exact friction points to implement high-impact fixes.

The platform’s StoryAI Summaries speeds up root-cause analysis with actionable insights, so you can quickly optimize user flows and prevent mid-funnel drop-offs. Better yet, StoryAI Opportunities automatically ranks behavior issues by revenue impact, enabling you to prioritize fixes without engineering overhead.

If you’re looking for clarity and behavioral context before creating your first A/B test, Fullstory fits perfectly into the analytics layer of your ecommerce CRO tool stack.

65% of teams have misdiagnosed digital friction, shipping the wrong fix

Fullstory — Wed, 26 Aug 2026 07:00:00 GMT

Misdiagnosing a digital friction issue affects 65% of teams, costing them time and engineering resources. What looks like a one-off mistake often signals a bigger issue.

Teams collect most of the data they use to measure digital friction analysis after a customer has already had a bad experience. By then, it's already shown up as a spike in bounce rate or a support ticket, and the damage is done. Keep reading to see where the breakdown starts and what it takes to catch friction before it gets this far.

65% of teams have fixed the wrong root cause

Nearly 2 in 3 teams have shipped a fix to a friction issue only to discover the root cause was something else entirely. This leaves the actual user experience exactly where it started, still broken for the people running into it.

In practice, that might look like noticing a rise in checkout abandonment and assuming it's due to a slow payment page. You spend a sprint optimizing load speed, only to find the real issue was a broken discount code field further up the flow. The abandonment hasn't moved, and you're starting over on a problem you thought you'd already fixed.

Competing priorities already stretch time thin, and a wrong fix eats into that same limited window. Worse, the reason teams keep missing the real cause often comes down to something simpler: how they discovered the problem in the first place.

Shipping a fix for the wrong root cause charges a compounding tax on engineering resources and customer trust. A team misdiagnoses why users are dropping off at checkout, burns development cycles on a solution that leaves the real roadblock untouched, and revenue keeps bleeding. Worst of all, you've lost the one thing that was telling you something's broken. The symptom goes away, but the problem doesn't.
- Lane Greer, Senior Manager, Strategic Solutions Specialists at Fullstory

52% of teams learn about friction from a complaint, not a system

For most teams, customer feedback, like a complaint, is still the earliest signal that something's wrong. Being responsive matters, but by the time someone reaches out, the issue has usually been affecting people for a while. Plus, the complaint itself rarely explains what went wrong or why.

Looking back at the checkout abandonment example, a customer might say "checkout is broken," which is true to what they experienced, but they have no way of knowing that a discount code field is the actual culprit.

That incomplete starting point leads to an incorrect diagnosis. But enriched customer data, such as behavioral insights, closes that distance. Instead of waiting for someone to describe the problem, it flags friction the moment it starts, giving teams the full picture of what actually happened.

There's no such thing as a proactive response to friction, since every action is a response to something. What matters is reacting before a customer voices their frustration. Most organizations rely on lagging indicators like app store reviews, surveys, and support tickets, and by then the damage is done. Shifting toward implicit sentiment, the behavioral data users generate just by acting, is what lets you catch friction early.
- Lane Greer, Senior Manager, Strategic Solutions Specialists at Fullstory

55% of IT and CX teams say friction shows up as rising support tickets first

More than half of IT, CX, and product teams point to rising support tickets as the clearest, earliest sign of digital friction. Usually, a wave of similar complaints shows up, pulling resources away from fixing the issue and toward responding to tickets, deepening the backlog in the process.

A single unresolved bug can easily generate dozens of nearly identical tickets in a week. Each one requires a support rep to work through, while the underlying issue remains untouched. The same friction driving those tickets also pushes customers away, first showing up as lower conversion rates, then, further down the line, as lost revenue.

"Rising support ticket volume is the ultimate lagging indicator. You're relying on explicit customer sentiment to find friction, waiting until the damage is done. That hits your business twice. It drains operational efficiency, turning your support team into forensic investigators. And it quietly destroys retention, because the users who bother to submit a ticket are only a fraction of the people hitting that friction. The rest just leave."
- Lane Greer, Senior Manager, Strategic Solutions Specialists at Fullstory

How Presidio found and fixed 100+ high-impact issues

Presidio is a leader in digital transformation and integration services. Its small, agile DX team was juggling competing priorities with limited analyst capacity. They needed a faster way to confirm what was actually breaking before committing engineering time to a fix.

Presidio centralized discovery, validation, and measurement using Fullstory, with CXperts, a Fullstory partner, managing configuration and reporting and tying insights directly to each code release. Findings flowed straight into engineering planning, so the most consequential fixes moved first.

Over the first year, this closed loop found and fixed more than 100 high-impact issues. Triage got faster while development cycles tightened, and CSAT climbed by 25 basis points year-over-year.

"Partnering with CXperts has allowed us to surface friction points in our experience and prioritize enhancements. As a result, we've been able to prioritize our backlog based on data and ensure that our developers are working on the most impactful changes."
— Karri Parks, Sr. Director, DX, Presidio

24% say faster detection, not more AI, is what would help most

Faster detection means you can catch friction before it becomes a larger issue, leading to conversion dips or lost revenue. Digital friction analytics makes that kind of speed possible. Fullcapture automatically indexes every user interaction, so friction surfaces the moment it occurs instead of after a customer reports it.

StoryAI takes the behavioral data Fullcapture surfaces and turns it into clear, proactive recommendations for what to fix first. That way, you work from what actually happened instead of a guess. Watch how StoryAI turns behavioral data into trusted answers.

Methodology

This survey was conducted by Centiment for Fullstory. Results are based on 647 completed surveys from U.S. professionals in Product Management, Engineering, Data & Analytics, Customer Experience/Support, and IT/Security roles, spanning company sizes and industries including retail, financial services, travel and hospitality, SaaS, and media and entertainment.

What is customer data enrichment? Overview and how to use it

The Fullstory Team — Fri, 18 Sep 2026 13:00:00 GMT

A name and email address alone don't tell your team much, making it difficult to personalize marketing campaigns based on customer preferences or to detect suspicious logins. Customer data enrichment software layers in details from other sources, giving your team context to prevent digital friction.

This guide breaks down what customer data enrichment involves, the types of data it draws from, and how to put it to work, including the behavioral data most guides overlook.

What is customer data enrichment?

Customer data enrichment is the process of augmenting existing datasets with relevant additional details from various sources, transforming raw data into a comprehensive asset for informed decision-making and deeper insights.

It's crucial for businesses as it significantly enhances data utility, driving strategic planning, customer insight, and operational efficiency by providing a richer, more actionable information base.

Key components of customer data enrichment include:

Verification: Ensuring data is current and accurate.
Supplementation: Adding missing or complementary data.
Integration: Merging data from various sources to create a more comprehensive dataset.

The role of data sources

Source quality determines whether data enrichment succeeds. Teams can draw on two types of sources to enrich data:

Internal data sources: Data already within the company's domain, like transaction histories, customer feedback, or behavioral signals from product and website activity.
External data sources: Accredited third-party data, which often includes demographic information, economic indicators, or industry trends.

Both types of sources contribute distinct layers of information, enabling you to build a more rounded and intricate profile of your customers or business environment. Behavioral data is one of the richest internal sources available, but most companies are already generating it without using it for enrichment.

Customer data enrichment requires specialized tools. Those tools need to handle vast amounts of information and integrate with existing systems, like CRMs, to streamline workflows and data management. You can significantly enhance data quality and, in turn, the overall decision-making process by carefully selecting reputable data providers.

Where behavioral data fits into customer data enrichment

Behavioral data tracks how a customer interacts with a product or website. This includes time on page, feature adoption, form abandonment, rage clicks, and repeat visits. Teams can use these signals to flag at-risk accounts and prioritize which features to fix without waiting on a third-party data purchase.

With Fullstory, teams capture that behavioral data automatically as customers interact with a product, so they can use it for enrichment right away instead of setting up manual tracking first.

5 benefits of enriched data

Data enrichment enhances the quality and utility of business data, yielding multiple strategic benefits.

Better customer experience: Deeper insight into customer desire paths enables more customized experiences.
Boosted sales efficiency: Enriched data helps sales teams secure high-quality leads and hit targets faster.
Improved decision-making: A solid analytical base helps you pinpoint opportunities more efficiently.
Cost reduction: Cleansing and updating data cuts down on the expenses tied to inaccurate records.
Increased data precision: Added context improves the precision and completeness of business data.

Data enrichment examples

Data enrichment involves adding layers to existing information to make it more useful. A few common examples:

Contact lists: A sales team's contact list often starts as just names and email addresses. Enriching it with social media profiles or purchase history turns a static list into a workable set of leads.
Customer segmentation: Marketers enrich basic demographic data with behavioral data, like which pages a group of customers visits most, to build segments they can actually target instead of broad, generic ones.
B2B sales targeting: Enriching a lead record with company size, industry, and revenue helps sales teams tailor a pitch instead of running the same script for every account.
Fraud detection: Comparing user-provided data against external databases helps teams flag suspicious transactions, like a login from an unrecognized device paired with a mismatched billing address, before it turns into a chargeback.
Support ticket context: Enriching a support ticket with a customer's history gives agents the full picture of what happened, like a failed checkout or a repeated error.

Types of data enrichment

Several types of data enrichment exist, but the most common types include:

Type	Description	Examples
Demographic	Enhances customer profiles with socio-economic data	Age, gender, income, education, marital status
Geographic	Appends location-based information to data sets	Country, city, state, region, street address
Firmographic	Defines datasets with organizational characteristics	Company size, industry, revenue, performance
Behavioral	Includes data about customer interactions	Purchasing habits, engagement level, user status
Technographic	Enriches data with technology-related insights	Device type, operating system, software, adoption stage
Psychographic	Involves appending lifestyle attributes	Lifestyle, interests, values, personality, opinions

Most of these types, aside from behavioral, require pulling in data from a third-party provider, which is where enrichment tools and vendors typically come in.

How to execute data enrichment

Data enrichment involves a few systematic steps to improve the quality and value of your data. Using the right tools keeps that data accurate and actionable at every stage.

Use the right techniques and tools

Use the right data enrichment tools to automate the process. Most teams pull external data sources directly into a CRM, refining customer profiles without manual work.

Common techniques include:

Algorithmic matching with third-party data
Append procedures to fill in missing information
Automation to streamline the process

Fullcapture takes a different approach. It captures, structures, and enriches behavioral data automatically as customers interact with a product, with no manual tagging or third-party purchase required.

Clean up your data

Clean your data before enrichment by:

Correcting inaccuracies and duplicate records
Verifying that data is still relevant and current

A clean foundation keeps enrichment from compounding existing issues instead of fixing them. It's a critical data enrichment best practice.

Integrate data into your business processes

Turning enriched data into strategic business intelligence requires solid data governance to align data management with your goals. Key elements to get right:

How data flows between systems, like from an enrichment tool into a CRM
Where automation keeps data current
Compliance with data protection regulations

Leveraging enriched data

Enriched data pays off once you put CRO tools to work across your business. Marketing builds sharper segments, sales prioritizes the right accounts, and support resolves issues faster.

Customer data utilization

You can use enriched customer data to gain actionable insight into behavior. Fullstory Analytics turns detailed customer profiles into segmentation strategies teams can act on, not just view. This targeted approach leads to personalized experiences that boost customer engagement.

Profile accuracy: Data that's both complete and accurate.
Segment actionably: Unique segments built from enriched data, ready for specific targeting.

Driving better business decisions

Enriched data informs data-driven decisions, from predicting market trends to anticipating what customers need next.

StoryAI is the AI agent layer built into Fullstory that turns enriched behavioral data into instant answers, without waiting on manual analysis. Two decisions get easier as a result:

Market adaptation: Adjust strategies as soon as market conditions shift.
Revenue growth: Spot opportunities for up-selling and cross-selling.

Enhancing customer interactions

Enriched data lets you tailor every step of the customer experience, keeping interactions relevant across channels. Rich, real-time data is what makes personalization work.

Fullstory Anywhere streams enriched behavioral signals into existing tools the moment they happen, putting real-time action within reach. The payoff shows up in how you engage:

Consistent engagement: Reach customers consistently and meaningfully.
On-point interaction: Deliver the right message at the right time.

Best practices for data enrichment

Here are some best practices to ensure accuracy, reliability, and relevance during enrichment activities:

Set specific goals: Determine clear objectives for data enrichment, like enhancing the accuracy of customer profiles with measurable targets.
Ensure source quality: The quality of enriched data directly depends on the credibility of the sources. Validate them against trusted external data.
Clean data regularly: Data enrichment should be a continuous effort of cleaning, updating, and verifying to keep data quality high.
Add valuable data: Prioritize data that adds significant insight and value, such as behavioral patterns to refine marketing tactics.
Automate: Use enrichment tools to simplify and automate processes, increase efficiency, and minimize errors.
Protect user privacy: Adherence to data protection laws is crucial. Ensure your product data enrichment processes comply with legal standards and respect user privacy.

Challenges and considerations

Data enrichment brings real challenges around data integrity and legal compliance. Poor data quality leads to bad decisions, and privacy regulations vary by location and sector. Five factors determine whether you stay ahead of both:

Consistency: Uniform data standards and formats across the dataset.
Currency: Regular updates to keep data relevant and timely.
Reliability: Verification against authoritative sources to confirm the data's validity.
Compliance: Adherence to GDPR, CCPA, and other data protection regulations, backed by Fullstory's Private by Default architecture.
Risk management: Defined protocols for data security and breach response.

Get more from customer data enrichment with Fullstory

Customer data enrichment supports conversion rate optimization when done right. It's about selecting and validating the right data from trustworthy sources, especially the behavioral data your product is already generating.

Fullcapture automatically captures that data, and StoryAI turns it into answers your team can act on instantly. See how you can use it to make faster, more informed decisions.

The best session replay software to help you know your users

The Fullstory Team — Thu, 21 Sep 2023 04:00:00 GMT

For any business that deals directly with customers with a website or app, session replay software is critical to getting more details about how that site or app is working. However, over the past few years, the sheer number of different session replay software options can make choosing one a daunting task. We’ve done the work, now we’re going to dig into the best session replay software options that you can use to know your customer.

What is session replay software?

Session replay software, also known as session recording or user replay, is an important tool for website owners and digital marketers who want to fine-tune their user experience and boost conversion rates. It lets you witness and understand user behavior by playing back real users' interactions with your website or app.

Basically, session replay tools play back entire user sessions, capturing details like mouse movements, scrolls, clicks, and other ways users interact with your site. Each session replay gives you a play-by-play of everything a user did on your website, app, or digital product.

This level of detail goes beyond raw user data or heatmap software, giving you a crystal-clear picture of the user's journey and experience.

With this tool in your arsenal, you can spot bugs, analyze your customers' digital experiences, and get a deep understanding of how users navigate your platform. With these insights, you can make informed decisions to improve your website design, remove any friction in the user's way, and streamline the whole navigation process. The result? Happy customers, improved satisfaction, and better conversion rates.

Session replay also offers insights into user behavior that can drive your business forward. By observing how users interact with your product, you can uncover opportunities to boost engagement, optimize content placement, and fine-tune CTAs. And, of course, all of this leads to an even better user experience and higher conversion rates.

Naturally, privacy is a concern. But, fortunately, industry-leading platforms like Fullstory have taken powerful steps toward protecting user privacy. Reputable session replay tools always make sure to anonymize sensitive data in accordance with data protection regulations. This way, you can analyze user behavior in a safe and secure manner.

Session replay software can supercharge your understanding of user behavior, improve your website's performance, and ultimately drive growth. It's a win-win for you and your customers.

What to look for in a session replay tool

While viewing every second of every user session could give you detailed insights, it's simply not feasible due to the sheer amount of time and data involved. That's why most session replay tools offer features that make the review process more efficient.

Adjustable playback speed lets you speed up less-eventful parts of the session while slowing down for more significant interactions. You can watch a half-hour session in a fraction of the time, without losing any critical details.

Additionally, being able to skip directly to key visitor interactions helps you quickly get to the heart of the matter. If you're looking to analyze a specific step in the user's journey, such as checkout or form submission, the ability to jump right to these moments is invaluable.

Session replay tools must offer privacy, accuracy, fidelity, performance, tabbed browsing, and omnichannel replay to truly be worth your time and money. If a tool doesn’t guarantee this, then it’s suggested you look elsewhere.

Choosing the right session replay software is crucial to gaining the insights necessary to optimize your website's user experience and improve conversion rates. Below is a list we’ve prepared of key features and considerations to keep in mind when selecting a session replay tool.

Comprehensive data capture

A great session replay tool should capture every single user interaction on your web pages or applications. This includes mouse movements, clicks, rage clicks, scrolls, and form inputs. The more detailed the recording, the better your understanding of how users navigate and interact with your site.

Adjustable playback speed and skip features

Replaying sessions at different speeds or skipping to specific interactions can save time and make the analysis process more efficient. Ensure the session replay tool you choose has these capabilities.

Privacy measures

The tool must respect user privacy and comply with data protection regulations. This includes anonymizing personal data and sensitive information in the recordings. How session replay tools handle privacy is another differentiating factor. It's essential to choose a tool that anonymizes sensitive data and is compliant with global data protection regulations like GDPR, CCPA, etc.

Powerful segmentation and filtering

The ability to filter and segment session recordings based on specific criteria (like location, device type, or user behavior) is crucial. It allows you to zero in on particular user groups or issues, making the analysis process more focused and efficient.

Integration capabilities

Integration with existing marketing, analytics, and customer service tools is a key consideration. The session replay tool should integrate seamlessly with your existing tech stack. This includes your analytics platform, customer data platform (CDP), customer relationship management (CRM) system, and more. A session recording tool that seamlessly integrates with your current tech stack can enhance its value by correlating data across platforms and providing a more holistic understanding of customer behavior.

Insightful metrics

Beyond simple replays, the best session replay tools offer insightful metrics that can aid in conversion rate optimization. Look for software that provides heatmaps, conversion funnels, form analytics, and other metrics that add depth to your understanding of user behavior.

Customer support

Finally, consider the level of customer support provided by the software vendor. Do they offer quick and reliable assistance when needed? Do they provide training or resources to help you maximize the tool's value? These are questions you should answer during your deliberations.

Pay close attention to the scope of the replayed sessions

When choosing a session replay tool, one of the critical decisions you'll have to make is whether you want a solution that captures all of your user sessions or one that only records a sampling. Both approaches come with their own set of benefits and considerations.

Full session reproduction

A session replay tool that captures all of your user sessions provides the most comprehensive view of user behavior on your site. This means every visit, every interaction, and every unique journey is recorded and available for you to replay and analyze.

This approach can be incredibly beneficial. For instance, if your website experiences a sudden drop in conversion rates or a spike in user complaints, having a full session capture allows you to review all affected sessions to identify the cause quickly.

However, keep in mind that this complete data capture might lead to a large volume of data to sift through. It might be more challenging to pinpoint specific sessions to watch unless you have robust filtering and segmentation features at hand. Full capture also requires a robust storage solution and a keen focus on privacy considerations.

Session sampling

On the other hand, session sampling captures a subset of user sessions. This strategy can be less demanding in terms of storage and easier to manage. It's typically based on a predefined percentage, a specific user segment, or randomly selected sessions.

Sampling can still give you a good sense of how users are interacting with your site. It's a more streamlined approach, focusing on the quality of insights over quantity. This might be more manageable for smaller teams or businesses with less capacity to review a vast number of sessions.

However, the downside is that you might miss out on specific user interactions or rare events that could provide valuable insights. Your understanding of user behavior will be based on a subset of data, which might not fully represent your entire user base.

The best session replay software to use on your site or app

Fullstory

Fullstory is a robust and comprehensive digital experience platform that offers session replay among other features like heatmaps and funnel analytics. Fullstory's high-fidelity session replays capture every mouse movement, click, scroll, and more, giving you a complete understanding of your user's experience. Its robust search and segmentation capabilities make it easy to find the sessions that matter most to you.

Pros of Fullstory

Full capture of all user sessions, offering comprehensive insights
Robust search and segmentation capabilities
Excellent privacy controls with GDPR and CCPA compliance
Provides detailed metrics and reports
Integrates with popular platforms such as Google Analytics, Slack, and Zendesk

Cons of Fullstory

Steeper learning curve due to its comprehensive features

Fullstory offers a free plan with limited features, perfect for small businesses. Its Business plan provides unlimited sessions, advanced analytics, and more, but pricing is custom and depends on your specific needs.

Smartlook

Smartlook is another session replay tool known for its user-friendly interface and qualitative analytics. It helps you understand user behavior, uncover UX issues, and improve your digital platforms.

Pros of Smartlook

Easy to use with a user-friendly interface
Offers event tracking and funnel analysis
Supports integration with platforms like WordPress, Shopify, and Google Analytics

Cons of Smartlook

The free plan has limited features and recordings
Advanced analytics features may require some learning to use effectively

Smartlook offers a free tier with limited features, while their paid plans (starting at $55/month) offer unlimited recordings, advanced analytics, and priority support.

Hotjar

Hotjar is another choice for session replay, offering features that help you understand what users are doing on your site.

Pros of Hotjar

Feature set including session replay, heatmaps, and form analytics
Navigable user-friendly interface
Supports integration with platforms like WordPress and Shopify

Cons of Hotjar

Does not offer full session capture, instead works on a sampling basis
Lack of advanced filtering options in session replay

Hotjar's Basic plan is free and offers limited data collection, while their Plus plan (at $32/month) offers more data, advanced features, and priority support.

Mouseflow

Mouseflow is a session replay and heatmap tool that provides insights into user behavior, helping businesses optimize their digital experiences. Mouseflow offers session replays, heatmaps, and feedback campaigns.

Pros of Mouseflow

Offers a wide range of features including session replays, heatmaps, and funnels
Advanced filtering options for efficient data analysis
Allows for easy tracking of specific events or behaviors
Provides integration with popular platforms like WordPress and Shopify

Cons of Mouseflow

Some users find the interface less intuitive
Might be more expensive compared to other options

Mouseflow offers a free tier with limited sessions and features. Their paid plans start at $31 per month, offering more features and unlimited sessions.

Lucky Orange

Lucky Orange includes a session replay tool along with other features including conversion funnels and form analytics.

Pros of Lucky Orange

Toolset including session replay, heatmaps, and form analytics
Provides real-time data on user activity
Easy integration with platforms like WordPress, Joomla, and Shopify

Cons of Lucky Orange

The interface might feel a bit cluttered due to the range of features
The session replay feature might not be as robust as other dedicated tools

Lucky Orange offers a free 7-day trial. Their paid plans start at $32 per month and offer unlimited recordings, heatmaps, and more.

Crazy Egg

Crazy Egg is a session replay tool that offers a features including heatmaps and A/B testing to optimize your website's user experience.

Pros of Crazy Egg

Featureset including session replay, heatmaps, and A/B testing
User-friendly interface

Cons of Crazy Egg

Some users find the session replay feature less robust compared to competitors
Lacks advanced analytics and reporting features

Crazy Egg offers a free 30-day trial. Their paid plans start at $49 per month and offer unlimited A/B tests, recordings, and more.

Clarity

Pros of Clarity

Provides session replays and heatmaps for thorough user behavior analysis
The insights dashboard presents key metrics at a glance
Supports integrations with platforms such as WordPress and Shopify

Cons of Clarity

The interface might not be as intuitive as some competitors
Does not offer as many features as other comprehensive tools like Fullstory or Hotjar

Clarity is free to use, making it a great option for those on a tight budget or with high-traffic sites.

Inspectlet

Pros of Inspectlet

Provides session replay, heatmaps, and form analytics
Offers robust filtering capabilities
Eye-tracking heatmaps allow you to see where users are focusing on your site
Supports A/B testing to help optimize your site

Cons of Inspectlet

The interface may not be as user-friendly as other tools
May not integrate as seamlessly with other platforms as some competitors

Inspectlet offers a free tier with limited sessions. Their paid plans start at $39 per month, offering more sessions, multiple websites, and priority support.

Each of these session replay software tools has its unique strengths, and the best one for you will depend on your specific needs, budget, and existing tech stack. Remember, what works best for one company might not be the best fit for another, so carefully consider your specific requirements before making a decision.

Frequently asked questions about session replay software

Does Google Analytics have session replay?

No, Google Analytics does not offer a session replay feature. Google Analytics is an excellent tool for tracking and analyzing quantitative data like website traffic, bounce rate, and conversion rate, but it does not provide the qualitative data that you can get from session replay.

While you can track individual user journeys to some extent with Google Analytics by setting up User ID tracking, it won't give you the visual representation of a user's actions like session replay software does. For instance, you can't watch a video of a user's session or see heat maps of mouse activity.

If you need these qualitative insights, it's best to use a dedicated session replay tool like Fullstory, which can complement the data you get from Google Analytics and give you a more holistic view of your users' behavior. Moreover, Fullstory integrates with Google Analytics, making it easy to correlate the data from both platforms for deeper analysis.

Record and Playback vs. Session Replay—what's the difference?

Melanie Crissey — Wed, 14 Feb 2018 04:00:00 GMT

Record and Playback and Session Replay tools may sound similar but they're wildly different.

Have you ever wondered:

What's "record and playback?" Is that like "session replay?"

"Record and Playback" and "Session Replay" (or "Session Playback") are two similarly named but different types of features used to help you deliver better customer experiences. Despite the names, each serves a vastly different purpose within the software development lifecycle ("SDLC").

Let's define them both, compare the two, and look at which solution will make the most sense for you.

What is Record and Playback?

Record and Playback is a common feature for automated user interface (UI) testing. With a Record and Playback tool, you can hit “record” and begin clicking on elements within a user interface on a website or web app. The Record and Playback tool will record your interactions so that later you can hit “play.” The tool will then recreate your interactions—every navigation, click, or keystroke–in the browser, exactly as if you performed them yourself.

These scripted interactions can then be manually kicked off to run later as needed. As the automated test runs, it will return error messages if the automated testing tool fails to complete one of the interactions as programmed.

Understood through the Jobs-to-Be-Done framework (JTBD), automated UI testing via record and playback does the job of repeating manual interactions as a check to make sure key functionality works as designed.

These test scripts can also be integrated with other systems to:

Run on command as part of a QA process
Run on demand as part of the build process, to prevent broken code from shipping to production (this is typically referred to as "automated testing within continuous integration / continuous delivery"), or
Run on a schedule from remote machines to monitor performance of a site or web application proactively, watching speed and reliability (this is typically called "synthetic monitoring" or "transaction monitoring")

What are some Record and Playback tools?

Common record and playback tools include:

Benefits and trade-offs: Record & Playback tools

Record and Playback tools can be helpful for:

Reducing manual work in QA and regression testing,
Preventing usability or performance bugs from going live, and
Quickly creating highly portable tests for ongoing performance monitoring

Here are some trade-offs to consider:

Recorded tests are limited. You can write transaction tests to run common user flows, but it would take a whole lot of recorded tests to get full coverage of every possible click path.
Recorded tests are fragile. Because the recorded test scripts often rely on specific CSS or XPATH to reference the correct elements with which to interact, minor changes to a site's code could cause the tests to fail when they run. Scripts must be actively and carefully maintained to reflect any changes to a site's navigation or user interface.

It's worth noting that some Record and Playback tools only work in designated browsers. If you're looking to import your recorded tests into a suite for ongoing monitoring, you'll need to make sure that the script output from your Record and Playback tool is compatible with your monitoring service. (For example, some synthetic monitoring solutions will accept Selenium IDE tests but not Java-based Selenium tests; it's important to know the difference!)

What is Session Replay?

Session replay tools help you:

Bonus: If you have a session replay tool that captures all user interactions and surfaces errors errors, you basically have 100% coverage for monitoring all of the performance problems with your live site. And, you'll never have to wonder how to replicate a bug, because you'll have the interaction and console errors logged in one place. That's super valuable!

Here are some limitations of session replay tools to keep in mind:

Session replay helps you react quickly to bugs, but it might not help you prevent bugs. It is possible to use session replay on a staging environment to record errors as they occur, and it's super helpful to use session replay to QA on production or live sites, but most session replay tools won't integrate nicely into your build process to prevent those bugs from going live in the first place. That said - the beauty of replay is that every bug will be captured as it occurs, and sessions with captured bugs can help you respond quickly and keep your time-to-resolution low (fast).
Some light maintenance may be required. While session replay itself doesn't come with the limitations and fragility of automated test scripts (everyday updates and changes to your website won't break recording) you might not completely escape the light maintenance work that comes with site updates. While recording itself might not require any instrumentation, the reports or Segments that make session replay so valuable can require some ongoing attention.

Is session replay right for you?

Let's break it down:

☞ Looking for a tool to help you record interactions to create scripts for automated testing?

Nope—session replay isn't the thing to solve that problem. But if you Google "automated UI testing," you'll probably come up with something useful.

☞ Need answers about your customer experience to make critical business decisions? Want to support customers with less back-and-forth, save time spent debugging, and optimize your site or web application?

Yes—session replay is the tool for you!

But why not both?

For most organizations, it probably makes sense to have both record/playback and session replay tools in their SDLC tool kits. These tools can be complementary. In fact, session replay can be powerful for informing which user flows will be most valuable to record and automate for proactive monitoring or testing.

For example, through watching session replays, you might uncover a popular customer journey on your website that you wouldn't have known was business-critical. Once you know how many of your customers rely on that path or set of features, you can use record and playback to build a test specifically for that critical customer flow and make sure that it never breaks when you deploy new code.

Tool-calling accuracy: Why context is the real AI agent bottleneck

Ted McNulty — Mon, 24 Aug 2026 13:00:00 GMT

Tool calls are not the problem, but context can create one.

For me, this learning journey started in an everyday code review. I was looking at a PR for one of our chart-building agents, the segment builder, where an engineer had bolted a new capability onto an existing tool instead of creating a separate one.

I argued for a separate tool.

The pushback was the conventional wisdom: too many registered tools degrade quality. The model gets distracted, loses the thread in a long context, or picks the wrong function. We both had a version of best practice on our side, so instead of arguing I devised a test.

What my test actually showed

I stress-tested tool selection as the number of registered tools grew from a handful up to a hundred, across single calls and longer sequential chains. The short version: on a current frontier model, the "too many tools" worry mostly didn't hold. It stayed reliable well past the point the old rule of thumb says it should break. An older, cheaper model was a different story, erratic as the toolset grew.

I'm not going to turn this into a benchmark you can pick apart on a trial-by-trial basis, because the exact curve isn't the point. The point is that model capability has moved, and a lot of the scaffolding we build to compensate for "dumb" models is quietly solving a problem that's going away.

So where does the hard part go?

If tool accuracy is close to solved, the hard part moves upstream, to whether the model can get the context it needs, whenever it needs it. Sometimes that means loading the context up front.

More often, in an agentic loop, it means the tool itself returns enough alongside the raw data: not just the event, but what the event means. A capable model will pick the right tool. It will not always take the most effective path to an answer, especially when the request is vague or the data is unlabeled.

That's when the real work starts: reasoning about the problem, doing discovery, running fail-fast experiments to move forward without burning cycles. The constraint is no longer whether the model can act. It's whether it can reach the context, on its own, at the moment it needs it.

And when a model doesn't have that context, it doesn't just get the wrong answer, it keeps trying, and every attempt has a token cost. More on that later.

A fresh example

Last week, I was testing agents against a customer's analytics setup, and their custom instrumentation was messy and thinly named.

A simple question, "How many people completed account registration today?", kept tripping the agent up. The event that captured the relevant data was named something like evt_4f2Complete, with no description.

The model had no way to know what that was. What helps is context.

If the agent can see that evt_4f2Complete feeds a metric called "Account registrations," it can infer its meaning and proceed with confidence. Same model, same question. The only thing that changed was whether the data around the event told the model enough to understand it.

This is a cost problem, not just an accuracy one

The token cost is what the people who approve the cloud bill should care about.

In one test, a question that lacked the right context sent the model into a loop. It burned millions of tokens and took far longer than any user would sit through, and still gave up with a barely accurate answer. The same class of question, with the context in place, came back accurate on a small fraction of that. A model that can't find what it needs doesn't fail fast. It keeps trying, and you pay for every attempt.

Two takeaways, depending on your seat

For engineers: treat tools like Unix utilities. Each should do one thing well. On a current model, stop optimizing for tool count and start optimizing for the clarity of the tool and the data it reaches.

For the people who own the cloud spend: a pricier model can be the cheaper one. We'd been looking at cheaper open-source models to cut costs. But if a cheaper per-token model takes twice the tokens to reach the same answer, it costs more, and it's slower to the user. Per-token price is not the same as cost-to-answer.

The bottom line

We spend a lot of engineering effort making models smarter about acting when the models are already good enough to act. The leverage now is upstream, in whether the data an agent reasons over is complete and self-describing enough to be understood without a human to translate it.

Capability is getting cheap. Context is the frontier. That's where I'm spending my time.

Best Amplitude alternatives for product analytics in 2026

Fullstory — Tue, 30 Jun 2026 13:00:00 GMT

Amplitude is one of the most consolidated platforms in product analytics. But teams hit real walls that drive them to look for Amplitude alternatives.

The tool can only explain churn if you tracked the right event before it happened. Most teams realize what they missed after users are already gone.

That said, when looking for better options, you may end up dealing with new issues. Complete session capture can create compliance risks if privacy controls aren't built in, and some pricing climbs faster than your actual usage.

The best option depends on your goals, team, and stack. In this guide, you’ll find the best alternatives for product analytics in 2026 to compare digital experience analytics tools and find the right fit for your needs.

Best Amplitude alternatives overview

Tool	Best for	Key features
Fullstory	Product and CX teams are analyzing behavioral data across web and mobile	Fullcapture, friction detection, funnel analysis, heatmaps, StoryAI, anomaly detection, Guides and Surveys
Mixpanel	Self-serve product teams running event-based funnel analysis	Event tracking, funnel analysis, cohort analysis, custom dashboards
Pendo	SaaS product managers building in-app guides and onboarding flows	In-app guides, product analytics, feature usage tracking, surveys, user segmentation
Contentsquare	Enterprise marketing teams focused on zone-based page optimization	Zone-based heatmaps, journey analysis, frustration scoring, session replay
Heap	Product teams wanting autocapture without manual instrumentation	Autocapture, funnel analysis, session replay, data science integrations
PostHog	Engineering-led teams running open-source, self-hosted analytics	Event tracking, feature flags, A/B testing, self-hosted deployment
Quantum Metric	Multi-brand organizations consolidating feedback across channels	Real-time feedback capture, text analytics, and role-based dashboards
Glassbox	Regulated industries requiring compliance-grade behavioral data	Session replay, struggle detection, compliance controls, funnel analysis
Google Analytics 4	Teams tracking web traffic and conversions on a free plan	Traffic reporting, conversion tracking, audience segmentation, basic funnels

1. Fullstory

Best for: Product and CX teams analyzing behavioral data across web and mobile

G2 Rating: 4.5/5

Fullstory is one of the most complete behavioral data platforms for analyzing how users interact with websites and apps. It offers native coverage for web and mobile analytics, with no separate SDKs to maintain. Security and compliance are a priority by design because sensitive data is masked at the source, not after.

StoryAI transforms behavioral data into actionable clarity with no engineering support required. It automatically flags friction points and optimization opportunities by providing instant session summaries and answering real-time questions on the fly. Engineered for an open AI ecosystem with an MCP connection to your preferred LLMs, while also deploying autonomous background agents that continuously analyze data and do the heavy lifting for you.

Fullstory addresses Amplitude’s biggest limitation: You don't need to know what to track or instrument it manually. Behavioral data is indexed automatically, so teams can answer questions retroactively.

Key features:

Session replay: indexed and reconstructed sessions provide qualitative context behind funnel numbers.
Fullcapture: automatic capture of user interactions, including clicks, taps, and page changes, that doesn’t need a tracking plan or upfront instrumentation.
Customer journey analytics: complete user paths across web and mobile.
User segmentation: build cohorts from any captured behavior.
Private by Default: sensitive data is excluded on the user’s device, so it doesn't reach any server.
StoryAI: ask direct questions about behavioral data and get a clear explanation back.
Guides & Surveys: Launch targeted contextual guides and surveys to capture feedback and quickly eliminate user friction.

Pros

Intuitive and user-friendly interface for analyzing user behavior.
Valuable and actionable insights to understand user behavior and improve the user experience.
Responsive and personalized customer support.
Granular data on user interactions.
Helps support teams validate feedback and solve user issues.

Cons

Significant learning curve, although customer support helps teams navigate it.
Customization options may not be enough for some teams.
Limited analysis and segmentation features.
Setup may require additional help to get the full value from the platform.
Data customizing and sharing options may feel insufficient in some cases.

Pricing

Free: 30K monthly sessions and 12 months of analytics retention. It includes access to basic analytics, Session Replay, and debugging tools for up to 10 users.
Business: It’s designed for companies that need deeper product analysis and quality insights to detect issues, measure events, or build dashboards.
Advanced: This plan includes everything in Business and adds more advanced product analytics features, such as Journeys and Conversions. It’s ideal to understand patterns at a larger scale to plan roadmaps and improve funnels.
Enterprise: Designed for specialized data science and analytics teams. The plan adds SSO, role-based permissions, and optional multi-org management.

Since Fullstory uses custom pricing, you need to schedule a demo to get a quote.

Reviews

"One of the things users particularly like is the intuitive user interface. Fullstory is designed to be easy to navigate, which means even those without a technical background can quickly get to grips with it. This accessibility is a significant advantage, as it allows teams across an organization to delve into user behavior data without needing extensive training."
- Federico M., Senior Product Manager

“Fullstory is an insightful, user-friendly tool that has greatly impacted quoteline improvements at Everyday Insurance. We receive unparalleled support from the CSM team - shout out to Alana Martino-Burke for going above and beyond to help with configuration and analysis. There are always new features demonstrating that the platform is industry-forward and constantly reviewing its usability. I recommend Fullstory as a behavioral tool to improve product, optimization, compliance and QA.”
- Shantelle L., Optimization Specialist

2. Mixpanel

Best for: Self-serve product teams running event-based funnel analysis

G2 Rating: 4.5/5

Mixpanel is a product analytics platform focused on tracking specific user actions. It is the closest alternative for teams that need what Amplitude is used for: event-based analytics across funnels, cohorts, and retention reports.

In the Mixpanel vs. Amplitude debate, the platform comes out as a more approachable option. Designed for small teams and startups, it has an advantage in terms of setup speed and learning curve. Teams that need deeper enterprise reporting should look for Mixpanel alternative tools.

The platform is a reasonable option for less technical teams. However, when evaluating Fullstory vs. Mixpanel, the need to instrument events upfront remains a key limitation.

Key features

Product analytics: build funnels, retention reports, and cohorts on the event model.
Mixpanel AI: surfaces the ‘why’ behind spikes and drops.
Experiments: A/B tests and feature flags managed in the same platform.
Metric Trees: living map of KPIs and drivers.
Session Replay & Heatmaps: replays are tied to analytics to show the big picture, but also zoom into individual sessions replays.

Pros

Easy to use, especially for event tracking and quick data retrieval.
Flexibility and reliability.
Efficient real-time tracking and comprehensive documentation.
Intuitive and accessible interface for different teams.
Good reporting capabilities that facilitate effortless teamwork.

Cons

Advanced features and event management are difficult to understand.
Significant learning curve for effectively managing updates.
Cumbersome event setup process. Primarily impacts A/B testing and sequence tracking.
Too expensive and complicated to maintain A/B tests and advanced features.
Incomplete integration and disorganized event naming create data management problems.

Pricing

Mixpanel offers three tiers, priced based on event volume. Growth starts free up to 1M events. Then, pricing starts at $0.28 per 1K events with volume discounts available. Enterprise plans use custom pricing.

Free: 1M events, 5 saved reports, and 10K session replays per month.
Growth: It offers unlimited reports, 20K session replays, and cohorts.
Enterprise: Includes unlimited events, advanced analytics, comprehensive data governance and security, and premium support.

Reviews

“I find Mixpanel very easy to use and intuitive, which makes it simple for me to navigate through product analytics. I appreciate having full control over all aspects of the events we capture, which means I can see all raw events and create custom ones to fit our needs. Additionally, adding events is straightforward and quick, especially when working with R&D. I like how I can measure events that come from different paths but want to analyze as one, and the integration process is very easy.”
- Ben S., Director of Product

“I think the live demos in Mixpanel are incredibly valuable. Being able to see exactly where users click and what they don't click is super helpful. I really like how Mixpanel obfuscates a lot of the information so it's not revealing any sensitive data. I also appreciate having a full holistic end-to-end understanding of what our users are using throughout each system. It's also easy to create new environments in Mixpanel, so we can have different products in different environments, such as a development environment and a production environment. This allows us to test features and ensure observability before releasing them. There are many things that I like about Mixpanel.”
- Aaron K., Vice President of Product Management

3. Pendo

Best for: SaaS product managers building in-app guides and onboarding flows

G2 Rating: 4.4/5

Pendo is a no-code product experience platform that unifies product and AI agent analytics and predictions, user feedback, and in-app guides in one place.

Its biggest limitation is that everything revolves around adoption. Analytics, replay, and feedback exist to power in-app guides, so behavioral data only goes as deep as that goal requires. Session replays expire after 30 days, or 90 with extended retention.

Other Amplitude alternatives take a different approach to data capture. In a Fullstory vs. Pendo comparison, for example, you’ll see how indexing every user interaction lets teams answer questions not defined in advance.

Key features

Data Explorer: slice and filter behavioral data in seconds.
In-app guides: onboarding flows and tooltips.
Session replay: watch how users move through the product.
Surveys and NPS: feedback collected inside the app.
Integrations: connect with tools you use like Slack, Salesforce, and WordPress.

Pros

Intuitive integration without any coding.
The integration between analytics and in-app guidance works well.
Customer support responds quickly and guides you through the implementation process.
Behavior tracking and feedback collection features are useful and reliable.
The user experience is intuitive, and the initial setup is quick.

Cons

User analysis and feedback management become more difficult as usage grows.
Recovery and rollback options are limited.
Configuring the platform takes time.
Data reports are not always accurate.
The information is sometimes slow to update.

Pricing

Pendo's pricing is based on monthly active users (MAUs). It uses custom pricing, so you need to request a demo to get a quote. It offers four plans:

Free: Up to 500 MAUs. Includes product analytics, in-app guides, and NPS.
Base: Custom MAU volume. Includes product analytics and in-app guides.
Core: Custom MAU volume. Includes product analytics, in-app guides, and session replay.
Ultimate: Custom MAU volume. Includes product analytics, in-app guides, session replay, sentiment (NPS, PMF, CSAT surveys), Orchestrate, Listen, and Data Sync.

Reviews

“Pendo provides an intuitive, well-organized UI that makes it easy to handle analytics and application integrations, such as in-app messaging, in a straightforward way. It also allows you to group metadata into segments, which helps you narrow your focus and target specific customer data more precisely.”
- Angelo A., Team Lead and Senior Software Engineer

“I really like the replays in Pendo. They allow us to look at specific paths and workflows. The great thing about replays is that they uncover frustration points that we might not already know about. It's not just about saying something is hard to use; if you can actually show someone, it's much more convincing. The clip feature is also helpful because we can quickly show three or four clips in a row related to the same thing.”
- Katy F., Principal Product Designer

4. Contentsquare

Best for: Enterprise marketing teams focused on zone-based page optimization

G2 Rating: 4.6/5

Contentsquare is an all-in-one experience intelligence platform. The current platform was formed through the acquisitions of Hotjar, Heap, and Clicktale.

While these are very powerful modules, they retain traces of their separate origins, which causes data models to differ and workflows to feel less smooth than in other Amplitude alternatives.

If you compare Fullstory vs. Contentsquare, for example, you’ll find that implementing Contentsquare can take weeks, while Fullstory deploys with a single snippet in minutes.

Key features

Session replay: captures customer experience, and you can also get a summary that highlights key moments.
Journey analysis: customers progress from entry to exit.
Web Analytics: understand the entire customer journey and how your channels and campaigns are performing.
Errors and frustration: surface technical and non-technical errors on sites and apps.
User feedback: collect and analyze your user feedback with AI.

Pros

Useful, actionable customer journey insights.
The analytics tools are intuitive and easy to use.
Helps understand how users interact with the site.
The Journey Analysis feature is useful for identifying friction points and opportunities.
It’s easy to understand common tasks.

Cons

It lacks robust qualitative feedback features.
The most advanced tools can be overwhelming.
Initial implementation requires time and configuration.
Advanced features take time to master.
Session replays have issues like missing interactions or lost actions.

Pricing

Free: 200K monthly sessions, 1 project, replays, heatmaps and funnels, error and performance monitoring, basic surveys, and connection with LLMs.
Growth: Starts at $39 per month. From 7K monthly sessions, 13-month data access, 3 projects, zone-based heatmaps, journey analysis, impact quantification, AI assistant.
Pro: From 1M+ monthly sessions, up to 6 projects, precision filtering, retroactive analysis, AI multi-session replay summaries, revenue goals and tracking, AI analyst (add-on).
Enterprise: Custom monthly sessions, unlimited projects, experience monitoring, AI error summaries, data feeds, dedicated support and SLAs, and an AI analyst (add-on).

You’ll need to contact the sales team to get pricing for Pro and Enterprise.

Reviews

“I like how Contentsquare provides detailed behavior analysis of our targeted users on both the website and the app. The visual tools - such as heatmaps, session replays, and journey analysis - give us clear, in-depth insights into what users are doing. I also enjoy creating dashboards, and I appreciate how easy it is to build them while still keeping the level of detail we need.”
- Manoj B., Assistant Manager - Digital Marketing

“I like the ease of access to data and insights that Contentsquare provides, as it means more people in the business can make data-informed decisions. The speed to insight is fast, which is really helpful. I also appreciate its configurability, as it can be set up for various business contexts.”
- Rob A., Senior CRO Manager

5. Heap

Best for: Product teams wanting autocapture without manual instrumentation

G2 Rating: 4.4/5

Heap is a product analytics platform with automatic event capture. That solves Amplitude's limitation of having to define what to track before you actually need it.

The downside is that Heap is now part of Contentsquare, so its roadmap and support model are no longer independent.

Key features

Journeys: visual maps of user flows.
Sense AI: fast insights without requiring analytics experience.
Web analytics: key web metrics integrated into the broader behavior analysis.
Segments: cohorts for actionable insights.
Capture: automatic event tracking.

Pros

Easy to use.
Autocapture feature simplifies tracking and provides insights with minimal configuration.
Automated event tracking saves time for teams.
Efficient for streamlining event tracking.
Intuitive interface for non-technical teams.

Cons

Data can be inaccurate.
Difficulty reconciling metrics and navigating large amounts of data.
Slow performance in some cases.
Steep learning curve for complex features.
Limited filtering options for large data sets.

Pricing

Free: Up to 10K monthly sessions and access to core analytics charts, unlimited enrichment sources, guides integrations, SSO, and 6 months of data history.
Growth: To estimate its price, you can use the snippet on the page. All features in Free, plus an AI-powered assistant, unlimited users and reports, chart customization, CSV exports, email support and 12 months of data history.
Pro: All features in Growth, plus account analytics, engagement matrix, report alerts, session replay as an add-on, standard support.
Premier: All features in Pro, plus data warehouse integration, behavioral targeting, unlimited projects, advanced user permissions, dedicated CSM, region-specific storage.

Pro and Premier pricing is only available after scheduling a demo.

Reviews

“I like that Heap is so easy for non-technical team members to be able to create tracking events without the need for any coding knowledge or getting the engineering team involved.”
- Milos D., Product Design Manager

“I like Heap because it supports learning and skill growth while I’m working on real projects. The work culture is friendly, and it encourages teamwork, continuous improvement, and collaboration across the team.”
- Raghavendra P., Software Engineer

6. PostHog

Best for: Engineering-led teams running open-source, self-hosted analytics

G2 Rating: 4.5/5

PostHog is an open-source product analytics platform. That is its main feature because it allows teams to maintain full control over their data without relying on third parties.

This makes it particularly appealing to teams with strict data security requirements. However, it requires a significant engineering investment and dedicated technical teams.

Key features

Product analytics: event tracking with autocapture and direct SQL access for custom queries
Session replay: recordings for both web and mobile apps
Feature flags: roll out changes to specific cohorts before going wide
Experiments: A/B testing measured against the metrics you already track
Error tracking: exceptions and alerts living next to the behavioral data
Data warehouse: query external sources alongside PostHog's own events

Pros

Easy and quick setup.
Powerful user behavior analytics.
Session replay is useful for analyzing issues and user journeys.
All-in-one platform with analytics, session replay, feature flags, and experimentation.
Provides fast and actionable insights.

Cons

Steep learning curve for advanced features.
Can overwhelm non-technical users.
Setup and documentation can be confusing.
Filters and interface elements are not always intuitive.
Mobile and session replay features feel less mature.

Pricing

Free: No credit card required. Includes 1 project, 1-year data retention, unlimited team members, community support, and monthly free usage across PostHog products.
Pay-as-you-go: Starts free, then charges based on usage after the monthly free tier. Adding a credit card unlocks 6 projects, 7-year data retention, and email support.

Reviews

“What I like most about PostHog is how much visibility it gives into user behavior without needing multiple tools. We use it to track events, analyze funnels, understand drop-off points, and monitor product usage in one place. Session recordings and feature-level insights are especially helpful because they help us understand not only what users do, but also why they do it. It is flexible and provides a lot of value for product and growth teams.”
- Mahdi H., Chief Marketing Officer

“We use PostHog daily for both web and mobile analytics. It gives us full control over event tracking, funnels, and feature flags without relying on multiple tools. I especially like the flexibility of defining custom events, running product experiments, and connecting everything directly to our data stack. It feels very developer-friendly compared to tools like Mixpanel or Amplitude.”
- Juan O., Analyst

7. Quantum Metric

Best for: Multi-brand organizations consolidating feedback across channels

G2 Rating: 4.6/5

Quantum Metric is a digital analytics platform focused on user experience. Unlike Amplitude, it shifts the focus from event-based product analytics to journey issues and technical signals across web and mobile experiences.

Its enterprise-oriented approach can mean a more complex setup and slower iteration for teams. That may feel limiting when you need faster, more flexible product analysis.

Key features

Autocapture: complete interaction data across web and mobile, with no sampling.
Felix AI: session summaries generated automatically, readable in seconds.
Session replay with heatmaps: visual context for any anomaly in the numbers.
Data streaming: pushes experience data into your own warehouse.
Journey analytics: behavior connected across each step of the digital journey.
Monitoring and alerts: real-time warnings with one-click impact quantification.

Pros

Session replay helps you understand where users struggle.
Helps identify bugs and technical issues more quickly.
Shows possible fixes based on their business impact.
Strong dashboards and reports to align teams around user behavior.
Customer support is quick and responsive.

Cons

Steep learning curve for new users.
Finding specific sessions or replays can be difficult.
Some replays may fail to load or display blank screens.
Searching and filtering can be complicated.
Some users report data inaccuracy issues.

Pricing

Customer Operations: For teams focused on understanding customer behavior.
Business Operations: For teams that need to connect digital experience issues to business outcomes.
UX Operations: For UX and design teams that need to analyze user behavior.
Developer Operations: For technical teams that need to investigate errors, bugs, and performance issues affecting the experience.

Reviews

“Quantum Metric makes it easier to see exactly how users experience a site or app. The session replay and journey insights are especially helpful for spotting issues quickly, understanding customer friction, and making faster improvements that have a real impact.”
- Miguel D., Senior Product Manager

“Interactive Tool is the best that I can use to see heatmap and engagement of site assets.”
- Ann C., Site Merchandising Manager

8. Glassbox

Best for: Regulated industries requiring compliance-grade behavioral data

G2 Rating: 4.9/5

Glassbox is a customer intelligence and digital experience analytics platform focused on privacy and compliance. That’s why it is a strong fit for financial services and healthcare teams, where data governance is non-negotiable.

The platform focuses more on identifying friction points in critical user journeys than on broader, more flexible product analytics use cases.

Key features

Tagless data capture: collects 100% of sessions, including server-side and technical events.
Session Vault: a secure repository to keep a record of customer interactions.
Performance analytics: combine performance analytics of real user monitoring (RUM) with experience analytics.
Privacy and compliance controls: data privacy and security functions to ensure you’ll never expose sensitive information.
GIA: Glassbox's AI assistant for searching and summarizing sessions.
Rapid response: detect friction in real time and trigger the right intervention instantly.

Pros

Easy session access and replay.
Strong customer behavior insights.
Helps teams troubleshoot issues faster.
Comprehensive API and network call visibility.
AI-powered struggle detection.

Cons

Session links can be incomplete or split.
Slow performance during complex queries.
Finding relevant sessions can be difficult.
Some users find the platform not intuitive.
Short data retention period.

Pricing

Production Operation: Designed to solve customer journey-level issues. Includes 100% session replay, 2M monthly sessions, and 1 month of replay and analysis retention.
Marketing/Business: For teams focused on maximizing spend, improving conversions, and uncovering hidden friction points that limit revenue. It includes 3M monthly sessions, 10% session replay, 3 months of replay retention, and 13 months of analysis retention.
Product: Designed to minimize friction and increase user adoption. It includes 3M monthly sessions, 10% session replay, 3 months of replay retention, and 6 months of analysis retention.

Glassbox uses custom pricing for its plans, depending on the selected package, data retention period, and session volume across web or app experiences.

Reviews

“I like that Glassbox uses AI, which makes it easier for me to trust them since they have a source of information from a vast majority through AI. Despite still trying other products, I find Glassbox to be one of the best.”
- Jashanpreet S., Security Guard

“I love the ease of design in Glassbox as it allows me comfort in managing everything because it handles all matters, saving me time and effort.”
- Mohamed Mostafa, Freelance

9. Google Analytics 4

Best for: Teams tracking web traffic and conversions on a free plan

G2 Rating: 4.5/5

Google Analytics 4 is a free web analytics tool. It helps teams track traffic, acquisition, engagement, and conversions across websites and apps. As part of the Google ecosystem, it integrates with Google Ads, Search Console, and BigQuery.

GA4 is geared toward traffic reporting and marketing attribution, not product analysis. The level of behavioral insight is much more limited than what teams need to understand why users are struggling at the individual session level.

Key features

Predictive metrics: uses machine learning to analyze your data and estimate purchase and churn probability.
Proactive insights: automatically detects important changes, trends, and growth opportunities.
Explorations: custom reports and funnels built without preset templates.
BigQuery export: raw data is included free, which most analytics tools charge for.
Google Ads integration: audiences built in GA4 sync directly into ad campaigns.

Pros

Detailed website traffic insights.
Reliable acquisition and conversion tracking.
Granular audience segmentation.
Strong Google tool integrations.
Flexible dashboards and reports.

Cons

Steep learning curve.
Less intuitive GA4 interface.
Additional setup for advanced reports.
Data sampling and platform constraints.
Data delays in standard reports.

Pricing

Google Analytics 4: It’s free. Includes standard web and app analytics, traffic reporting, conversion tracking, audience segmentation, and integrations with Google tools.
Google Analytics 360: It’s the paid enterprise version with custom pricing. It gives larger organizations higher limits for data collection, reporting, retention, and BigQuery export. It also includes more advanced support and service-level needs.

Reviews

“What I like best about Google Analytics is its ability to provide detailed insights into website and application performance. It helps track user behavior, traffic sources, conversions, and engagement metrics in one place. The reporting capabilities are comprehensive, and the integration with other Google products makes it easier to measure marketing effectiveness and make data-driven decisions.”
- Dharamveer P., Application Security Engineer

“Google Analytics has been the standard for free website analytics for the 2+ decades I've been in the industry. It has changed quite a bit over that time, but it has always provided powerful analytical tools at no cost to the user.”
- Lindsey M., Marketing Director

What to look for in a product analytics platform

The main goal is to understand how your users interact with your product. To do that, a platform should help you know precisely what users do, where and why they get stuck, and what they prefer.

Fullcapture vs. manual instrumentation

Manual instrumentation means that teams have to define in advance each event they want to measure. Instead, Fullcapture collects data automatically and comprehensively, creating more opportunities for future analysis. Some platforms market "autocapture" for instrumentation, but it's different than Fullcapture because they only log discrete, click-based interaction events, whereas Fullcapture records the complete visual experience and every behavioral nuance happening between those clicks.

Retroactive querying

A good tool allows you to measure events even if you had not tracked them before. You can run queries you had not thought of before and still get precise data.

Behavioral signals, not just event counts

Knowing how many times something happened is useful, but the real insight comes from context. You can know that 60 people abandoned their carts, but repeated clicks on an unseen error on a button show you where to fix it.

Privacy and compliance by design

Protecting your users’ privacy is fundamental, especially with tools that capture behavior. Prioritize a private-by-default architecture that ensures sensitive information is never captured at the source, rather than absorbing the massive compliance liabilities of platforms that ingest raw data first and then try to mask it after the fact. A platform designed from the beginning to protect sensitive data allows you to limit what is captured and have clear controls to comply with regulations such as GDPR, CCPA, or SOC 2.

Accessible to the whole team

A tool can be very powerful, but if you need analysts, data teams, or engineers to use it, you will probably waste its potential. An accessible platform allows more teams to analyze what happens and find the answers they need for their area. Ensure that your solution doesn’t create an engineering bottleneck anytime more data needs to be collected, analyzed or acted on.

Pricing that scales predictably

Most of these tools offer free plans or affordable pricing at first, but as usage grows, they become more expensive. That is why it is recommended to choose options that offer a clear and reasonable price as usage increases.

With Amplitude, for example, AI features may look free but rely on higher data volumes to work well, which can drive up costs faster than teams expect.

Native integrations with your stack

A product analytics tool becomes much more valuable when it connects with your CRM, data warehouse, customer data platform, business intelligence tools, and more. That way, data does not stay isolated, but integrates with the tools where your team already works.

How to choose the right Amplitude alternative

At this point, you know what Amplitude does, but you also know it isn’t enough for your team. To find the right tool, you can start by pinpointing what's missing, and from there, you'll know what to look for.

Engineering time

Every event you track with Amplitude software is defined in advance, and to stay ahead, you need to continually work on it. When your roadmap priorities shift, you risk dealing with blind spots you didn't get to tag.

Ask: How much engineering time can you realistically dedicate to instrumentation, and what happens to your data when priorities shift?

Retroactive analysis

The best questions surface after launch. If your analytics tool, like Amplitude, only lets you answer what you defined in advance, you'll need weeks to gather data and draw conclusions.

Ask: How often does a new product issue send your team into a cycle of instrumenting, waiting, realizing it was the wrong event, and starting over?

Privacy and compliance

With Amplitude, privacy depends on your team instrumenting which data to send and which to leave out. A mistagged event or a forgotten field can expose sensitive data, and you often find out after the fact.

Ask: How exposed are you to GDPR, CCPA, or SOC 2 risk, and does your current tool handle that at the source or after the fact?

Learning curve

Amplitude reviews mention a steep learning curve. Building custom funnels or cohorts means understanding the data model, so non-technical teams usually need an analyst to move forward.

Ask: Which teams need access to analytics, and how technical are they?

Behavioral depth

Event tracking tells you what users did, but it doesn't show you where they hesitated, or what they tried first, or why they left. Behavioral and digital experience data cover both sides.

Ask: Do you want to know what the entire behavioral context you user experienced?

Platform coverage

To understand how users actually behave, where they hesitate, or what they try before abandoning, you need another tool and the work of reconciling data across platforms.

Ask: What touchpoints and channels are most critical, and does the tool cover all of them natively?

Pricing model

With platforms that charge by event volume, the cost grows alongside product usage. Other pricing models decouple cost from engagement, making the bill more predictable as the product scales.

Ask: What's the real cost of this tool at your current and projected usage, not just the entry price?

Stop tracking events and start understanding behavior

You came here looking for an answer, and you’re leaving with a few more questions. What does your team need? What can it realistically maintain? What risks can it take on? And what is its main goal?

Your answers will pull you in different directions. GA4 works if all you’re after is free traffic reporting, and Glassbox makes sense if compliance is your only deciding factor.

Now, if your team needs behavioral depth to optimize conversion rates, private by default, and fast answers without the instrumentation overhead, Fullstory is the clear choice.

The 10 best Pendo alternatives for behavioral analytics

Fullstory — Thu, 30 Jul 2026 09:00:00 GMT

Pendo works great for teams that are just getting started with product analytics. But after you cross the initial threshold and need deeper behavioral insight? You'll likely hit a ceiling and start looking for Pendo alternatives.

While Pendo has its flaws, like shallow behavioral depth and a heavier setup lift, replacing it with a single tool isn't straightforward. Most alternatives aren't true replacements. Some only offer product or behavioral analytics, while others are built for developer workflows.

This guide will help you understand which alternatives cover which use cases, so you don't end up with a tool that leaves you worse off.

Why teams look for Pendo alternatives

Teams usually start looking for Pendo.io alternatives once these limitations start piling up:

Limited retroactive analysis. Events need to be tagged manually in advance. You only get feature usage data for the events you've instrumented upfront, while the rest of the data gets lost.
Limited behavioral context. Funnels and adoption metrics might show that users dropped off, but they don't explain what happened on screen to cause this. Teams can identify issues but not how to fix them.
Mobile analytics gaps. Coverage and privacy masking on mobile fall short of what regulated or complex-app teams need.
In-app guides prioritized over behavioral analysis. Pendo is built to guide users rather than diagnose friction. Teams that need the latter often maintain a separate stack.
Steep MAU-based pricing. Enterprise contracts scale quickly with MAU volume, often before teams have fully adopted the platform.

If this sounds familiar, it's time to see what else is out there.

Best Pendo alternatives overview

This overview helps you narrow down the best alternatives to Pendo based on your priorities: behavioral depth, product analytics, developer workflows, or free options.

Tool	Best For	Key Features
Fullstory	Enterprise teams searching for behavioral insights	Fullcapture, StoryAI, friction detection, funnel analysis, session replay, guides & surveys
Amplitude	Product teams prioritizing retention and funnel analytics	Event tracking, funnel analysis, cohort analysis, custom dashboards
Mixpanel	Self-serve teams running event-based product analytics	Event tracking, funnel analysis, cohort analysis, custom dashboards
Heap	Teams that want auto-capture without manual instrumentation	Auto-capture, retroactive analysis, funnel and path analysis
PostHog	Engineering teams that want open-source, self-hosted analytics	Event tracking, feature flags, A/B testing, self-hosted deployment
Glassbox	Regulated industries needing compliance-grade behavioral data	Session replay, struggle detection, compliance controls, funnel analysis
LogRocket	Frontend teams debugging errors and performance issues	Session replay, error tracking, performance monitoring, product analytics
Quantum Metric	Enterprise retail and FinServ teams quantifying revenue impact	Session replay, funnel analysis, anomaly detection, revenue quantification
Contentsquare	Enterprise teams needing journey analytics at scale	Journey analysis, zone-based heatmaps, friction detection, AI insights
Microsoft Clarity	Teams that need free behavioral basics with minimal setup	Heatmaps, session replay, click tracking, basic behavioral insights

1. Fullstory

Best for: Enterprise teams searching for behavioral insights

G2 rating: 4.5/5

Fullstory is a behavioral data platform that addresses one of Pendo’s biggest limitations: manual event tagging.

Unlike Pendo, which requires you to define and tag events you want to track upfront, Fullstory logs all user interactions automatically.

You can revisit them at any point and find insights you haven't thought to look for initially. StoryAI can also surface those insights for you based on plain-language questions.

Beyond that, Fullstory offers guides and surveys similar to Pendo, but it adds behavioral context on top to help teams understand friction.

The platform's Private by Default architecture ensures that behavioral depth doesn't compromise compliance. Fullstory masks data at the source, including on mobile, so sensitive information stays protected from the start.

Key features:

Fullcapture: Automatically captures behavioral data across web and mobile, with no manual tagging required.
StoryAI: Provides AI-powered summaries and answers that surface friction points and opportunities in plain language.
Friction detection: Identifies rage clicks, dead clicks, error clicks, and thrashed cursors.
Funnel analysis: Connects quantitative drop-off data to individual behavioral sessions for in-depth customer journey analytics.
Session replay: Provides high-fidelity playback without screenshot-based rendering.
Guides & Surveys: Enables in-product engagement tools for teams that need them.
Anomaly detection: Flags unusual behavioral patterns before they escalate.

Pros

Automatic behavioral data capture, no manual tagging, no gaps from uninstrumented events
StoryAI surfaces answers in plain language without SQL or engineering support
Session-level context connects funnel data to what actually happened on screen
Private by Default masks sensitive data at the source, including on mobile
Unified platform, one data model, one script, no acquisition-driven fragmentation
Guides and Surveys covers in-product guidance without a separate tool

Cons

Mobile setup takes more configuration effort than web
Large data volumes can feel like a lot to navigate in early rollout
Pricing fits better once you've scaled into it

Pricing

Free: 30K monthly sessions. 12 months of data retention, basic analytics, session replay, up to 10 users.
Business: Custom pricing. Deeper product analysis and quality insights.
Advanced: Custom pricing. Business + Journeys and Conversions.
Enterprise: Custom pricing. Advanced + SSO, role-based permissions, multi-org management.

Reviews

The ability to match a customer concern with a session has helped us better understand issues from the video recordings. The MCP/Claude integration also helps me explain what I am trying to discover with assistance and guidance.
- Bobbi M., UX Researcher

DESIGNED CTA

2. Amplitude

Best for: Product teams prioritizing retention and funnel analytics

G2 rating: 4.5/5

Amplitude is a robust product analytics platform that offers greater analytical maturity than Pendo. It provides features like retention curves, cohort analysis, and self-serve access to funnel data.

This makes Amplitude stronger on quantitative product analytics, but it's weaker on in-app guides, session-level behavioral context, and friction detection.

The platform's event-based model also provides data only for the events you instrument manually. There's no way to retroactively analyze historical data based on newly defined events.

Overall, if you need to understand the why behind funnel drop-off, you'll need to combine Amplitude with another tool.

Key features:

Funnel and retention analysis: Maps the user journey by identifying friction points, drop-offs, and user returns.
Cohort analysis and behavioral segmentation: Groups users by shared behaviors or properties to provide high-level analysis.
Self-serve dashboards: Facilitate the tracking of key metrics with custom views.
Data warehouse connectors: Connects Amplitude with the rest of the digital ecosystem.

Pros

Powerful funnel, retention, and cohort analysis out of the box
Self-serve model — no SQL required for most analyses
Strong ecosystem integrations and warehouse connectors
Good fit for cross-functional product and growth teams

Cons

Event-based model requires instrumentation planning upfront — no retroactive capture
No native session replay or friction detection
Teams often pair it with a behavioral tool, adding stack complexity
Pricing scales with event volume

Pricing

Free: 2M Events/month, 10K monthly session replays.
Plus/Growth/Enterprise: Custom pricing. Expanded or unlimited MTU or Events.

Reviews

What I like best is how Amplitude brings product analytics and Session Replay together in one place. We track complex quote and bind funnels across web and mobile, and being able to jump from a drop-off in a chart straight into a replay of what the user experienced makes debugging and prioritization much faster.
- Pablo M., FullStack Developer

3. Mixpanel

Best for: Self-serve teams running event-based product analytics

G2 rating: 4.5/5

Mixpanel is a clean, self-serve product analytics platform for teams that prioritize funnel and retention data.

It provides more comprehensive quantitative analytics than Pendo, along with self-serve setup. But it lacks in-app guides, offers less behavioral depth than Fullstory, and needs some manual configuration.

Like Pendo, Mixpanel only gives you the data you decide to track in advance. This requires careful instrumentation planning and doesn't solve Pendo's arguably biggest weakness.

The final verdict? Mixpanel is useful for understanding high-level product data. But if you need to diagnose page- or session-level friction, you should look for mixpanel alternatives.

Key features:

Event-based funnel and retention analysis: Shows how users perform events in series and assesses user engagement.
Cohort analysis and user segmentation: Groups users based on shared properties or events for a broad analysis.
Real-time dashboards: Monitors key product metrics in real time with customizable views.
Data warehouse connectors: Brings data from other systems into Mixpanel for increased accuracy and comprehensiveness.

Pros

Clean, intuitive event-based analytics
Self-serve setup with good documentation
Real-time dashboards and fast performance on large datasets
Transparent usage-based pricing at lower tiers

Cons

Requires careful instrumentation planning, no retroactive analysis
No heatmaps, session replay, or friction detection
Event naming and governance can get messy as teams scale
Pricing increases quickly at volume

Pricing

Free: 1M monthly events, up to 5 saved reports.
Growth: From $0.28/1K events. 20K monthly session replays.
Enterprise: Custom pricing. Unlimited monthly events.

Reviews

One thing I really like about Mixpanel is how intuitive and flexible its event tracking and funnel analysis are. The real-time insights, session replay capabilities, and easy-to-build dashboards make it simple for teams to spot issues quickly and better understand user behavior.
- Sourabh R., Lead Quality Assurance Engineer

4. Heap

Best for: Teams that want auto-capture without manual instrumentation

G2 rating: 4.4/5

Heap is a good Pendo alternative for teams that are frustrated with manual instrumentation. With this tool, you simply install the script once to capture all interactions without manual tagging.

This simplifies setup, but Heap still trails tools with more mature AI capabilities and session-based behavioral context, like Fullstory. It also falls short on in-app guides and mobile coverage.

Separately, its acquisition by Contentsquare might make the product direction uncertain.

Key features:

Automatic event capture and retroactive analysis: Captures a specific, limited set of interactions automatically.
Funnel and path analysis: Analyzes multi-step processes and user journeys.
Cohort analysis: Provides insights into users grouped based on behavior.
Basic session replay: Offers visual context on user sessions, but with fewer details than tools like Fullstory.

Pros

Retroactive event analysis without manual tagging
Funnel and path analysis without upfront instrumentation
Accessible onboarding for non-technical teams

Cons

Now part of Contentsquare, product roadmap independence is uncertain
AI layer less developed than Fullstory's StoryAI
Limited session-level behavioral context and friction detection
Privacy and compliance controls fall short for regulated industries

Pricing

Free: 10k monthly sessions, 6 months of data history, SSO.
Growth/Pro/Premier: Custom pricing. 12 months of data history, unlimited users & reports, AI assistant.

Reviews

Heap’s automatic event tracking is its strongest feature. It significantly reduces the need for manual instrumentation, which saves time for product and design teams. The interface is intuitive, and building funnels, retention reports, and user journey analyses is straightforward.
- Deep G., Web Designer

5. PostHog

Best for: Engineering teams that want open-source, self-hosted analytics

G2 rating: 4.5/5

PostHog is an open-source platform primarily built for product engineers. Unlike Pendo, it skews heavily toward developers and requires a meaningful engineering investment to maintain self-hosted deployments.

Although it lacks features like in-app guides and isn't as intuitive as Pendo, technical teams appreciate having better data control and many powerful features in one place.

PostHog is an excellent choice for engineering teams with strict data residency requirements or those that want full ownership of their analytics stack. Product and marketing teams might need to look for less complex tools, though.

Key features:

Open-source, self-hosted deployment option: Teams can host PostHog themselves for full data ownership.
Event tracking, feature flags, A/B testing: Teams can track custom events or set up autocapture, roll out and roll back new features to a small segment, and run experiments with minimal risk.
Session replay: Provides playbacks of user sessions in products, websites, or mobile apps.
Heatmaps: Show how users interact with different page elements.

Pros

Full data ownership with open-source, self-hosted option
All-in-one platform combining analytics, session replay, flags, and experiments
Generous free tier with transparent usage-based pricing
Developer-friendly with a clean SDK

Cons

Built primarily for developers, less accessible to non-technical teams
Self-hosting requires ongoing engineering investment to maintain and scale
Some features feel incomplete compared to mature enterprise platforms
Not a fit for teams that need in-app guides or behavioral depth without heavy setup

Pricing

Free: No credit card required. Community support, 1-year data retention.
Pay-as-you-go: Usage-based pricing.

Reviews

What I like most about PostHog is how it combines several tools – product analytics, session recordings, feature flags, and experiments – into one platform. It eliminates the need to juggle different tools and provides a much clearer picture of user behavior in one place.
- Verified User in Computer Software, Small-Business

6. Glassbox

Best for: Regulated industries needing compliance-grade behavioral data

G2 rating: 4.9/5

Glassbox is a strong option for financial services and healthcare teams where compliance and privacy controls are non-negotiable.

Although it's often positioned as a Pendo alternative because it serves regulated industries, Glassbox sits in an entirely different category: Pendo is a product adoption tool, while Glassbox is primarily a behavioral analytics platform.

One drawback is that Glassbox has no public pricing and no free plan. It's completely sales-led and doesn't offer a publicly available option to test out the features before committing. If you're looking for Pendo alternatives that are more affordable, Glassbox won't be it.

It's also narrower in scope than Fullstory, while services-heavy onboarding skews it toward enterprise.

Key features:

100% session capture across web and mobile: Captures all events and enables retroactive querying.
AI struggle detection: Identifies friction points and assigns Struggle Scores to facilitate prioritization.
Heatmaps and interaction maps: Show how users interact with pages and how different page elements impact the revenue.
Compliance controls and privacy masking: Omits sensitive information by default at the point of capture and keeps a record of every digital journey.
Customer journey analytics: Shows how users perform events in series and provides AI-driven insights.

Pros

Compliance-grade session capture with strong mobile privacy masking
AI-powered struggle detection surfaces friction automatically
Well-suited to financial services and regulated retail

Cons

No free plan; pricing not published
Limited data retention windows, often capped at 30–90 days
Onboarding and training resources can fall short
Less flexibility for non-regulated use cases

Pricing

Custom pricing across all plans. From 2M to 3M monthly sessions included.

Reviews

It offers comprehensive session replay capabilities that allow for detailed tracking of user behavior and interactions on websites. It also supports deep insights into customer journeys to more effectively identify customer issues.
- Anna Marie C., Data Reporting Analyst

7. LogRocket

Best for: Frontend teams debugging errors and performance issues

G2 rating: 4.6/5

LogRocket is a developer-focused tool that combines session replay with error tracking and performance monitoring, like tracking of web vitals and CPU usage.

This skews it toward a different audience than Pendo, which typically serves product managers and adoption-focused, non-technical teams.

LogRocket's main purpose is to bridge product analytics and frontend debugging. For example, it combines session replays with the underlying technical data, so teams can easily connect UX issues to technical root causes.

While it's obviously stronger than Pendo on error tracking and developer workflows, LogRocket isn't as suitable for product managers and marketers. It offers no in-app guides, while its interface defaults to technical context.

Key features:

Session replay synced with console logs and network requests: Visually captures user sessions alongside the underlying technical issues.
JavaScript error tracking and performance monitoring: Facilitates debugging, performance optimization, and UX quality tracking.
Galileo AI for issue detection and summaries: AI assistant that identifies user issues and consolidates user feedback.
Product analytics: Provides insights into user paths, conversion funnels, cohorts, and more.
Workflow integrations (Jira, GitHub, Datadog, Intercom): Sends session context to the tools teams already use to eliminate context switching and manual issue tracing.

Pros

Session replay synced with console logs and network requests
Galileo AI proactively surfaces issues without manual triage
Easy installation across web and mobile frameworks
Useful for engineering and support teams working from the same session data

Cons

Not built for product managers or non-technical teams
No in-app guides
Pricing scales quickly with session volume
Session recording can be unreliable at times

Pricing

Free: 1K sessions, 1 month data retention
Core/Pro/Enterprise: Pricing scales with the number of sessions captured. AI features, such as AI session summaries and issue detection, are only available on higher tiers.

Reviews

LogRocket was very simple to install and get up and running. I also like that our team can quickly pinpoint vague customer issues (like “checkout isn’t working”) by using the Session Replay feature, which makes it much easier to understand what the customer actually experienced.
- Telle M., Software Developer

8. Quantum Metric

Best for: Enterprise retail and FinServ teams quantifying revenue impact

G2 rating: 4.6/5

Quantum Metric is an enterprise behavioral analytics platform that focuses on quantifying the revenue impact of UX issues. It works best for retail and financial services teams who need to justify experience investments.

It's not a full replacement for Pendo, as it focuses more on behavioral analytics than product adoption data. Most enterprise digital teams would get the most value by combining the two, rather than using either tool on its own.

Another drawback is that Quantum Metric is completely sales-led and enterprise-priced. This makes it less suitable for teams looking for a simpler or more accessible starting point.

Key features:

Session replay with revenue impact quantification: Shows UX issues and measures revenue loss.
FelixAI autonomous insight engine: Automatically summarizes user sessions and draws insights.
Heatmaps and interaction analysis: Shows how users scroll, click, and navigate through the website, plus how these engagements impact conversions and frustration.
Anomaly detection: Identifies unusual patterns in user behavior and triggers alerts when needed.
Autocapture via one-time JS tag or SDK: Captures all user interactions automatically after a one-time JS tag or SDK deployment.

Pros

Strong revenue quantification connects UX issues to business outcomes
Anomaly detection flags issues before they show up in revenue
Captures behavioral data other tools miss
Responsive customer support

Cons

Steep learning curve to unlock full capabilities
Sales-led model with no public pricing or free plan
Session search can struggle with specific identifiers
Not a fit for teams outside enterprise retail or financial services

Pricing

Customer Operations/Business Operations/UX Operations/Developer Operations: Custom pricing.

Reviews

Quantum Metric makes it easier to see exactly how users experience a site or app. The session replay and journey insights are especially helpful for spotting issues quickly, understanding customer friction, and making faster improvements that have a real impact.
- Miguel D., Senior Product Manager

9. Contentsquare

Best for: Enterprise teams needing journey analytics at scale

G2 rating: 4.6/5

Contentsquare is another enterprise platform focused on behavioral analytics rather than product adoption or engagement. It's built for deep, on-page behavioral and journey analysis, with enterprise-level pricing and service requirements.

The platform evolved from Clicktale, a digital experience analytics tool, and has since expanded by acquiring other tools like Heap and Hotjar.

This added mature analytics capabilities. But it also introduced potential fragmentation issues you wouldn't see with tools built on a single data model, like Fullstory.

If you need a unified data model or consistent behavioral data across web and mobile, it may be worth exploring Contentsquare alternatives.

Key features:

Zone-based heatmaps and journey analysis: Identifies which page elements contribute to goals like conversions and what users do before buying or dropping off.
Session replay and friction detection: Shows what users do, where they struggle, and what they experience across digital platforms.
AI-powered insights: Sense Analyst generates summaries, sends scheduled analyses via email, and answers questions.
Ecommerce optimization tools: Show personalized products to customers, run shopper surveys, and more.
Heap and Hotjar now part of the platform: Specialized analytics and heatmap capabilities brought together under one platform.

Pros

Broad enterprise feature set covering journey analytics, heatmaps, and friction detection
Strong in ecommerce and retail use cases
Large ecosystem with dedicated enterprise support

Cons

Acquisition-built structure creates fragmentation across data models and workflows
Setup and maintenance overhead, site changes can break instrumentation
Getting full value often requires ongoing paid consultant support
Mobile privacy masking falls short of regulated industry requirements

Pricing

Free: 200k monthly sessions, 1 project.
Growth: Starts at $39/month. 13 month data access, 3 projects.
Pro/Enterprise: Custom pricing. Expanded or unlimited monthly sessions and projects.

Reviews

I like the ease of access to data and insights that Contentsquare provides, as it means more people in the business can make data-informed decisions. The speed to insight is fast, which is really helpful.
- Rob A., Senior CRO manager

10. Microsoft Clarity

Best for: Teams that need free behavioral basics with minimal setup

G2 rating: 4.5/5

Microsoft Clarity is the strongest free option for teams that want basic heatmaps and session data without a budget commitment. But, for most teams, the tool should be a starting point rather than a full replacement for Pendo.

It's built for an entirely different primary use case, with its strongest selling point being free page-level behavioral basics. Compared to Pendo, it lacks in-app guides, product analytics depth, and enterprise privacy controls.

For teams already in the Microsoft ecosystem, the tool's biggest draw will likely be its native integration with Microsoft Ads, which enables a deeper analysis of Microsoft Ads campaigns.

Key features:

Heatmaps and click tracking: Records how users interact with pages, including which elements were clicked.
Session replay with AI-powered summaries: Shows what users do and experience on the website, while Copilot generates key takeaways.
Basic behavioral insights: Record behaviors like rage clicks, dead clicks, and quickbacks.
Native Microsoft Ads integration: Provides recordings, heatmaps, and insights for Microsoft Ads campaigns.

Pros

Completely free with no session limits
Quick setup via GTM or code snippet
Native Google Analytics and Microsoft Advertising integration
Copilot AI generates session insights in plain language

Cons

No in-app guides, product analytics, or funnel analysis
Reporting and filtering options stay basic
Limited third-party integrations beyond the Microsoft ecosystem
Not suited to enterprise privacy or compliance requirements

Pricing

Free forever

Reviews

It's Free and does the job very well. Reliable and easy-to-use heatmap software can be used with Google Analytics and Tag Manager.
- Abhishek M., Digital Marketing Consultant

How to evaluate a Pendo alternative

Once you've narrowed down your options, it's time for a more in-depth evaluation. Sit down with your shortlisted vendors, get demos, and ask questions that feature pages don't answer. These tips will help you get this stage right:

Inquire about data capture. Ask whether the platform captures data automatically or requires manual instrumentation, and what happens to historical data if an event was never tagged.
Ask what happens when your product changes. You'll inevitably introduce new flows, update UI, and ship mobile releases over time. Find out whether the platform automatically adapts to these changes or if your team will constantly need to update tracking.
Get mobile-focused demos. Many analytics platforms do privacy masking well on desktop but fall short on mobile. Ask vendors to demonstrate mobile privacy controls rather than relying on general compliance claims.
Ensure team-wide accessibility. Ask whether non-technical team members, like product managers, analysts, and CX leads, can get to answers without routing requests through engineering.
Test the AI layer. The quality of your AI analysis directly depends on the data capture method a vendor uses. Can it surface answers about behavior it wasn't pre-configured to track, or is it limited to predefined metrics?
Get a full cost-of-ownership breakdown. Ask the vendor to estimate implementation time, ongoing maintenance, and whether you'll need professional services to get the full value from the platform.

Making the most of your behavioral analytics platform

Behavioral analytics platforms require a financial and time investment. Stakeholders will likely want to see a return on it fast to decide whether to continue investing or call off the pilot.

Here are some ways you can get to value faster:

Start with the highest-impact user flows. Behavioral data usually has the most direct product and revenue impact when applied to flows like user onboarding, activation, and key feature adoption.
Get product managers and analysts into the tool early, preferably before rollout is complete. The teams closest to the product roadmap should be first to access behavioral insights directly. This helps them build confidence in the platform and start acting on the findings early.
Set up anomaly detection alerts from day one. This will show your team how the tool can surface friction and help them fix it before it shows up in revenue or retention metrics.
Integrate the platform with your data warehouse and BI tools. Behavioral data becomes more visible and quantifiable when it sits alongside product and business reporting rather than in a separate workflow.
Build a regular review cadence into team routines. This will ensure that behavioral insights drive sprint planning and roadmap decisions rather than just one-time audits.

See user behavior clearly with Fullstory

Most Pendo alternatives show which issues exist on your digital properties, but don't explain why they happen or how to solve them.

Fullstory makes that connection clear. It captures all behavioral data automatically, so you can identify why users are dropping off or getting stuck from day one. With a single script, you get access to all user interactions, session-level context, AI-powered answers, and advanced privacy controls.

For enterprise teams that need deep behavioral insight to improve digital experiences, Fullstory is the strongest choice.

Customer journey analytics: Best practices & insights

The Fullstory Team — Tue, 17 Dec 2024 12:30:00 GMT

Traditional analytics might show you the endpoints. But that's not enough when you need to understand the entire customer path – and more importantly, where that path breaks down.

Why journey analytics matters now

45% of organizations are investing in customer journey analytics. According to McKinsey, companies using journey analytics see:

15-20% reduction in service costs
10-15% increase in revenue
Faster resolution of customer issues

The reality of fragmented customer experiences

Customer expectations are clear: 76% want consistent interactions across departments. The problem? Teams work in silos. Support doesn't see what sales knows. Marketing misses crucial product usage signals. 54% of customers report that teams don't share information.

This disconnect costs more than just customer satisfaction. It leads to repeated customer information requests, inconsistent service responses, and missed opportunities for personalization. Each touchpoint in isolation tells only part of the story.

So, what is customer journey analytics?

Customer journey analytics is a data-driven approach that tracks, analyzes, and visualizes how customers interact with your business across all touchpoints and channels over time. It transforms raw behavioral data into actionable insights about customer behavior and preferences.

Let's break that down. Your customers might:

Start with a Google search
Browse your pricing page
Check out your competitor
Come back two days later
Finally convert

Journey analytics reveals these paths, though in banking and lending, behavioral analytics for financial services first has to account for the authentication walls that cause journey data to go dark.

The building blocks of journey analytics

Every click. Every page view. Every support ticket. These are your touchpoints – the basic units of customer behavior. But they're just the start.

What really matters is how these touchpoints connect. It's about understanding:

What actions typically lead to conversion
Where customers get stuck
Which features keep them coming back
When and why they reach out for help

The tools have evolved too. Today's analytics stack uses machine learning to spot patterns humans might miss, helping you predict behavior instead of just reacting to it. Modern platforms can process millions of interactions in real-time, turning raw data into insights you can actually use.

No more guessing about what your customers want. No more wondering why they leave. Just clear data showing exactly how they move through their journey with your product.

What can customer journey analytics do?

Digital interactions have exploded. Customer paths aren't linear, and traditional metrics miss crucial context. Journey analytics fills these gaps.

Predict churn before it happens

Watch how customers engage with your product. Spot the early warning signs when valuable accounts start to drift away. See which features keep users coming back.

Turn confusion into conversion

Find the exact moment users get stuck. Learn why they hesitate at checkout. Understand what makes power users so successful - then help everyone else follow their path.

Make smarter product decisions

For SaaS teams, see which onboarding flows actually drive activation. Ecommerce companies can pinpoint what browsing patterns lead to bigger purchases. Fintech providers can spot where users need extra guidance during complex transactions.

This isn't about collecting more data. It's about understanding what your customers are really trying to tell you.

Journey maps vs analytics: Understanding the difference

Many teams have journey maps. Few have journey analytics. Here's why you need both:

Journey maps show the "what"

Visual flow of ideal customer paths
Hypothetical customer stages
Planned touchpoints and goals

Journey analytics reveal the truth

Actual paths customers take
Where reality differs from plans
Quantifiable impact of each touchpoint

For example: Your journey map might show a clean path from signup to activation. But analytics might reveal users taking unexpected detours through help docs or getting stuck on specific features.

Get the best of both

Combine them to:

Validate or disprove journey assumptions
Find paths you never mapped
Measure the impact of journey improvements

The most successful teams use maps to plan and then analytics to verify and adjust based on real user behavior. Journey analytics reveal the big picture, which you can then refine and test by building a specialized stack of the best CRO software to fix your most costly drop-off points.

How to build effective journey analytics

Most journey analytics projects fail, not because of bad tools or data but because teams overcomplicate the process. Here's a straightforward path to getting it right.

Step 1: Automate your data capture

Stop relying on manual tracking. Modern journey analytics needs automatic capture across all channels, giving you retroactive insights from day one. Tools with autocapture capabilities ensure you never miss valuable interaction data that could reveal why customers succeed—or struggle.

Fullstory's Fullcapture, for example, automatically collects every user interaction without manual instrumentation, ensuring complete data coverage from the start.

Step 2: Connect your data sources

Break down platform silos. Your customer data lives in multiple places:

CRM records
Support tickets
Product usage logs
Marketing analytics

When these connect, you get the full story of how customers move through your product.

Step 3: Map journeys that matter

Forget complex flowcharts. Focus on critical customer journeys that impact revenue:

User onboarding flows
Conversion sequences
Support interaction patterns
Upgrade decisions

Watch real sessions to understand why users take certain paths. This combination of quantitative and qualitative data reveals opportunities you'd otherwise miss.

Step 4: Create action workflows

Think about this - 67% of customers expect resolution within 3 hours. Set up:

Real-time alerts for friction points
Cross-team sharing of insights
Clear ownership of improvements
Regular testing cycles

Step 5: Test and optimize

Start with one critical journey. Make improvements based on data, not assumptions. Measure the impact on:

Customer satisfaction scores
Support ticket volume
Conversion rates
Revenue metrics

When you prove value in one area, expand your analysis to other important journeys.

Tools for journey analytics

Fullstory: Complete behavioral data

Automatic capture of every user interaction without instrumentation. Built-in customer journey mapping tools show exactly how users navigate your product.

Notable features of Fullstory include:

Retroactive data analysis
Session replay for context
AI-powered pattern detection
Real-time alerts on friction points

Google Analytics: Basic traffic insights

Standard web analytics focused on page views and basic user flows. Provides:

Traffic source tracking
Basic conversion reporting
Limited user behavior data
No session replay capability

HubSpot: Marketing-focused mapping

Marketing automation platform with basic journey visualization. Offers:

Lead tracking
Email journey mapping
Form analytics
Limited behavioral data

The difference? Fullstory captures every single interaction automatically, while other tools require manual setup and miss crucial behavioral data. This complete picture helps teams spot opportunities they'd otherwise miss.

Here's how one company put these principles into practice.

Real impact: How Wahi doubled conversions with journey analytics

Despite tracking events through Amplitude, Wahi couldn't pinpoint why users abandoned key journeys. Form fills, chat interactions – users were dropping off, but the raw data couldn't explain why.

Finding answers with journey analytics

By implementing Fullstory, Wahi uncovered the true story behind their user behavior:

Identified specific friction points in forms and chat
Watched real user sessions to understand abandonment
Made rapid improvements based on actual user behavior

The results

100% increase in conversion rate year-over-year
Under 48-hour median time to bug resolution
Clear visibility into user pain points

The lesson: Event tracking alone isn't enough. Customer journey tracking – including the "why" behind user actions – makes the difference between guessing and knowing what to fix.

Read Wahi's full story here ->

Common journey analytics challenges (and how to fix them)

Data accuracy problem

The problem: Teams rely on manually-tagged events, missing crucial interactions and creating blind spots in customer understanding.

Solution:

Implement automatic data capture
Use server-side tracking for reliability
Validate through session replay

Data silos

The problem: As we briefly mentioned earlier, customer data lives in disconnected tools or silos. Support can't see product usage, product teams miss support context, marketing lacks behavioral data.

Solution:

Start with one critical flow (like onboarding)
Connect tools affecting that flow
Build automated data pipelines

Team alignment

The problem: Insights stay trapped in analytics tools. Different teams work from different data sets, leading to conflicting priorities.

Solution:

Share session replays for clear context
Create unified customer views
Set clear ownership for improvements

While these challenges are common, they're not insurmountable.

Ready to see your customer journey clearly?

Your customers are trying to tell you something. Their clicks, their hesitations, their successes and struggles – it's all valuable data waiting to be uncovered. Fullstory's complete journey analytics platform helps you capture every interaction and make improvements that matter. See how your customers really experience your product – get started with a demo.

Customer journey maps: visualize and analyze your user engagement

Fullstory — Sat, 25 Jan 2025 07:00:00 GMT

The customer journey is a long and often unpredictable road. Understanding it can be even more complicated.

That’s why customer journey maps were invented: to understand a customer’s roadmap from the very first touchpoint throughout the lasting life of their relationship with your business. Customer journey maps help organizations see the process from the customer's perspective, ensuring a more empathetic and accurate understanding.

Customer journey maps (or user journey maps) can be invaluable resources for companies, from marketing to sales to UX. These maps are built on real customer behaviors, not assumptions, which ensures they reflect actual actions, attitudes, and emotions. If done correctly, they are known to help businesses increase their ROI by 13–22%. The benefits of customer journey mapping include improved satisfaction, increased loyalty, and better business outcomes.

We’ll cover journey maps from top to bottom, their importance and characteristics, and review examples, along with what you need to make your own.

Looking for a video? Watch how to create a journey map using Fullstory, as Lane provides a detailed breakdown of the process to uncover Popular Paths on a website.

>> Jump to see how to create a customer journey map

What is a customer journey map?

A customer journey map (sometimes called a user journey map) visualizes the touchpoints and experiences a customer has with a brand, and its digital product or application, helping teams understand the entirety of the customer's journey.

While customer journey maps visualize the path users take, customer journey analytics provide the data-driven insights needed to optimize every touchpoint across all channels.

Companies can use this shared understanding to identify opportunities for innovation and improvement.

These maps can be simple or complex, depending on the depth of the customer's experience you want to capture and what you’re looking to gain from them.

Why do you need a customer journey map?

For any company, a customer journey map helps to enhance the customer experience and increase customer loyalty.

A customer journey map can prove invaluable for optimizing across multiple departments—marketing, sales, product, and customer service—in many, many ways. Mapping your customer journey can help you:

Glean customer journey insights into your audience that can drive revenue
Improve sales funnels & conversion rates authentically
Test and optimize specific touchpoints through A/B testing to improve conversion rates
Amplify customer experience by understanding the customer’s perspective
Reduce customer support tickets by locating customer pain points
Decrease customer churn and increase customer lifetime value
Increase customer satisfaction by identifying and addressing pain points throughout the journey

Together, these advantages translate into higher sales for your business. By mapping the journey, you can create more satisfied customers, which leads to greater advocacy and loyalty. Additionally, journey mapping helps boost customer retention by improving the overall experience and addressing issues before they impact loyalty.

Characteristics of customer journey maps

A typical customer journey map includes:

Actors: The person experiencing the journey. This is usually a customer, but it could also be a user, employee, or partner. It’s important to consider different customer segments, as each segment may have unique experiences, behaviors, and needs that should be accurately represented in the map.
Scenario and expectations: The specific experience being mapped and what the actor expects to achieve.
Phases: The high-level stages of the journey, such as awareness, consideration, purchase, and post-purchase.
Actions, mindsets, and emotions: What the actor is doing, thinking, and feeling at each stage.
Opportunities: Insights and areas for improvement identified through mapping. Journey mapping provides a deeper understanding of customer needs and pain points, enabling teams to make more informed decisions and drive innovation.

Identifying key touchpoints—critical moments where customers interact with your business—is essential for an effective customer journey map. These touchpoints help reveal customer emotions, behaviors, and pain points, allowing for targeted improvements that enhance the overall experience.

Customer personas and buyer personas: What’s the difference?

A buyer persona is a profile that showcases your ideal customer based on existing customer data and market research. Buyer personas help humanize the ideal customer you are trying to attract, which helps you understand them better and pick the right marketing strategy to convert them.

A buyer persona is your ideal customer—they’re in research mode. You can have more than one buyer persona for your company, and understanding this buyer is the key to creating a successful customer experience. Different customer segments can have unique journeys and should be mapped separately to accurately reflect their distinct experiences, behaviors, and needs. This buyer will turn into your customer.

Here’s what makes up your buyer persona:

Demographics—including personal, professional, and specific (age, gender, location, education, income, marital status, skills, routines, etc.)
Goals—including personal and professional, priorities, and challenges
Values—including personal and professional, and what they find to be important in products and companies
Preferences—including the content they consume, their communication choices, communities, groups, or associations, and how they spend their day, on and offline

Customer journey stages

Journey phases are the different high-level stages in the customer roadmap. They provide organization for the rest of the information in the journey map (actions, thoughts, and emotions).

The stages will vary from scenario to scenario, and each organization will usually have data to help it determine what these phases are for a given scenario. Often, you will see the awareness stage as the initial phase, where customers recognize a need or problem. This is followed by the consideration stage, where customers compare options, evaluate brands, and seek information to determine the best solution.

Other common phases include research, evaluation, and decision-making in the buyer's journey, which is the overall process from awareness to decision.

Customer expectations

Journey maps are best for scenarios that involve a sequence of events, describe a process, or might involve multiple channels.

Pain points are a specific problem that customers or prospective customers of your business are experiencing in the industry. When identifying and addressing pain points, it is crucial to understand customer sentiment, as these emotions often drive their motivations and decisions throughout the journey.

Scenarios can be real (for existing products and services) or anticipated—for products that are yet in the design stage.

Actions, mindset, and sentiment

Every customer has a particular action that they take, because of a mindset that they have and will express it in their own sentiment

Actions: When a customer engages with your brand with a purpose. Mapping each customer's interaction with your brand at every touchpoint helps identify opportunities for improvement and reveals gaps or pain points in the experience.
Mindset: Correspond to users’ thoughts, questions, motivations, and information needs at different stages in the journey.
Emotions: How customers feel about your brand, whether positive, negative, or neutral. Plot these emotions in a single line across the journey phases, signaling the emotional highs and lows of the experience.

Opportunities

Opportunities of a customer journey map are desired outcomes. Maps should include key components, which can depend on the goal of the user journey mapping initiative.

Opportunities are also insights gained from mapping—they speak to how the user experience can be optimized. Journey mapping provides valuable insights that can drive innovation and further optimization by revealing pain points and areas for improvement.

To create a customer journey map, identify the personas, map the triggers that lead to desired outcomes, and discuss opportunities.

What are customer journey touchpoints?

Customer journey touchpoints are individual transactions through which the customer interacts with a business. While customer journey maps trace the ideal visual path, you need a stack of ecommerce CRO tools to analyze, test, and optimize how users actually navigate those touchpoints.

There are about 5–7 minimum touchpoints along a customer journey. Customer journey touchpoints for omnichannel brands are everywhere:

social media posts
product demos
live chats
advertisements
brick and mortar visits
website(s) clicks

At each of these touchpoints, customers interact with brands across multiple channels and moments, shaping their overall experience.

You’ll also have the added returning customer touchpoints to consider—like how engaged they are with your product, if they are returning to your website or if they are attending your events for the second or third time.

Examples of customer touchpoints

Identifying each touchpoint is crucial for creating a customer journey map that will drive a better customer experience. Once you’ve identified the touchpoints, list out possible customer actions for each.

Some actions that derive from customer touchpoints might be:

Downloading an eBook
Clicking on your FAQ
Requesting a demo or call
Subscribing to your blog
Clicking a paid ad
Engaging in customer service interactions, such as reaching out for support, providing feedback, or resolving issues after purchase

It’s important to know which touchpoints to invest time and resources into. Your map maps out the areas you can improve, retain and scale.

Types of customer journey maps

Each customer journey map has a different objective and business focus. Journey maps can cover the entire customer lifecycle, from initial awareness to post-purchase engagement, helping organizations understand and manage each phase of the customer relationship. There are six types to familiarize yourself with:

Current state—These illustrate what customers do, think, and feel as they interact with your business currently. A current state customer journey map helps identify gaps, pain points, and opportunities for improvement in the existing experience.
Future state—These illustrate what customers will do, think, and feel as they interact with your business in the future. This type of map is used to visualize the ideal customer journey, helping organizations optimize interactions and align efforts to meet customer needs.
Day in the life—These examine everything that customers or prospects do, think, and feel (within a specific area), whether that involves your product or not.
Service blueprint—This is a diagram that usually starts with a basic version of an existing or future state journey map.
Circular—These are used for subscription-based models to visualize the customer journey as a circle or loop. This helps reinforce the importance of customer retention and lifetime value.
Empathy—These are used to create a shared understanding around the wants, needs, thoughts, and actions of a customer.

Creating multiple customer journey maps for different scenarios or customer segments provides a more comprehensive view of customer needs and perceptions throughout their experience. To learn best practices and identify gaps, review customer journey mapping examples that illustrate how to effectively create and analyze these maps.

Compare types of journey maps

Journey maps are meant to be used as a strategic planning tool. By mapping out the customer experience, organizations can identify opportunities to design a more efficient customer journey, optimizing processes and reducing friction at every touchpoint. Use these definitions to guide you towards aspects of other methods that your team has not previously considered, with the goal of achieving an efficient customer journey through mapping and process optimization.

Journey map vs. experience map

A journey map is specific to a product or service, while an experience map is more general and can be used outside of a business's scope.

Since experience maps are more generic in nature, they can also be used to find pain points in a product or service for a future journey map.

Journey map vs. service blueprint

If journey maps are a product of experience maps, they will need a blueprint to direct them there.

Service blueprints are a continuation of journey maps in the service industry. They lead the roadmap for service-based customer journeys.

Journey map vs. user story map

User stories are used in Agile to plan features or functionalities, much like a future customer journey map.

In the user story map case, each feature is condensed down to a deliberately brief description from a user's point of view. The typical format of a user story is a single sentence:

“As a [type of user], I want to [goal], so that [benefit].”

How to create a customer journey map

Set your objectives for the map
Define your customer persona
Highlight your target customer personas
Determine customer journey stages
Identify customer touchpoints
Map the current state
Understand motivations, frustrations, and resources

Step 1: Set your objectives for the map

Before the whiteboard comes out, it’s important to set up clear objectives for the map. How do you do this?

Collect customer feedback from all company stakeholders, your team, and your customers. You can do this by forms, surveys, customer surveys, survey customers, interviews, spotlights, or good old-fashioned conversation. Using customer surveys is especially important, as they provide direct insights into your customers' experiences and help you better understand their buying journey.

Ask questions like:

How do you feel about this feature?
How easy was it to find us?
Were there any points of frustration during your interaction with us?

Set goals within the customer journey map like seeing your product through your customer’s eyes, ways to improve your product or service, and how it all impacts your future.

Step 2. Define your customer persona

Your customer is the core of your journey map, so the first step is defining your target customer persona.

A customer or buyer persona is an in-depth understanding of who your customer really is, what they are trying to solve, and how they interact with your business.

The first thing you need to decide is which type of journey you're going to map:

Persona—a profile of a specific customer type
Target—a profile of a potential customer
Market—a segment of customers

If you're creating your first map, it's best to pick your most common customer persona and consider the route they would typically take when engaging with your business for the first time.

Track each step from the point of interest to the product lifeline to get a true sense of your target persona.

Once you've created distinct personas, you can use them to dictate customer journey maps that describe each persona's experience at various points during their lifecycle with your company.

Step 3: Highlight your target customer personas

Take your journey map persona list and pinpoint your target customer. To do this, dig deep and understand what each customer wants to achieve as they go through the customer journey.

A great way to go about doing this is to first identify the paths that your customer may take on your site.

For instance, if your customer is a member, the first thing that they might do is to log in. These instances will help you determine engagement for each customer.

Here are some different ways to obtain and understand customers' goals:

Survey or interview different customer groups
Conduct user testing feedback
Study customer support correspondence

With this insight, you can then determine where your customer will go along their journey.

Step 4. Determine customer journey stages

Journey maps are organized by customer stages (sometimes referred to as phases).

Each stage represents a goal your customer is trying to achieve in their journey. You should build a customer journey map with stages that represent your customer's goal-oriented journey, not your internal process steps.

Based on the persona, define the stages that your customer experiences with you over time. To figure out your stages, answer this question:

What does it take for a customer to start from awareness to decision throughout the buying process?

The typical customer journey stages are:

Awareness—how customers found out about you
Research—how you can solve their problem
Evaluation—how you compare against others
Decision—how they chose you

The goal: to determine how, when, and where they discover you, choose you over competitors, purchase from you, and maintain a relationship with you.

Step 5. Identify customer touchpoints

Your buyer journey map will be built on customer touchpoints. Customer touchpoints are your brand's points of customer contact, from start to finish.

For example, here are a few ways customers may find you:

Search inquiry
Online ad
Customer review
Social media post

These are just a few touchpoints; there are many more. Identifying these customer touchpoints is an important step towards creating a journey map and ensuring your customers are satisfied every step of the way. It's also a pivotal part of how you will define your map.

Step 6. Map the current state

It's time to conduct customer research for your map. This will include information about your customer's intentions, motivations, digital footprint, and interpretation of your brand.

Most customers are happy to help if they believe you are genuinely interested in their experience and will use your feedback to improve things for others.

For each stage of the journey, try to identify:

What are my customer's goals?
What type of experience do they want?
What steps are needed to complete that process?
How do they feel during each touchpoint?
What other thoughts, feelings or frustrations do they have during certain stages?

Beyond this information, be sure to look at patterns of how they conduct themselves online, where they frequent, and what they share.

Step 7. Understand motivations, frustrations, and resources

This step involves looking at the totality of the customer experience (CX) with your company.

Every business will look through the lens of its customer personas uniquely. Walking through each journey map stage with your team will help you identify any friction points within the customer experience.

You know your customers best. Here are a few example questions to get you started:

Where could friction appear in this particular touchpoint?
Are people abandoning purchases because of this?
Are customers unaware of this solution that you’ve provided? If so, why not?

These questions can be answered with customer behavior data, using a behavioral data platform like Fullstory, which delivers a complete, retroactive view of how people interact with your site or app. By analyzing how customers engage with your brand across different touchpoints, you can identify opportunities to improve the customer relationship and enhance their overall experience. By finding out how visitors engage with your product and service, you can understand what resources you will need for growth.

Examine customer emotions and motivations.

Every action your customer takes is motivated by emotion. The emotional driver of each of your customer's actions is usually caused by a pain point or a problem.

So, get to know what roadblocks are stopping customers from making desired actions (again with digital intelligence) and get in front of motivation.

Overcome obstacles like cost, product friction, and onboarding frustration with customer experience intelligence.

Take the customer journey yourself.

Follow the customer journey yourself. Walking in the customer's shoes helps you identify pain points and better understand where customer needs aren’t being met by seeing it through your customer’s point of view.

Pro tip: Document the customer journey for each of your personas and make note of the differences. Putting yourself in customers shoes will help you better understand their experience and will help for future user journey maps.

Determine the resources you have and the ones you'll need.

It's important to take inventory of the resources you have and the ones you'll need to improve the customer's journey.

Using your map, you can advise leadership to invest in the right tools that will help your team manage customer demand. Do you have what it takes to solve the customer's problem?

Step 8: Evaluate, adjust and scale

As with any process, you’ll need to test it over time. Data analysis is used to identify customers’ behavior and pain points that need changing along the way. It’s important to regularly update your journey maps to reflect evolving customer behaviors and needs, ensuring your insights remain accurate and relevant.

Use digital experience and customer intelligence to keep you informed on the user journey. This means relying on a solution to let your customers show you what’s actually happening. With tools like session replay, you can understand exactly what they are going through and proactively fix it.

Always include your team and keep stakeholders involved to keep the roadmap clear.

Your customer journey map checklist

This guide on creating a customer journey map should help you truly understand the impact of your product or service. By stepping into your customer's shoes, you will gain the insight needed to improve the entire experience.

Now, you're ready to create your map. Here are the questions you will ask yourself while creating a customer journey map:

☑ Set clear objectives for your map ☑ Define your customer persona ☑ Highlight your target personas ☑ Determine customer stages ☑ Identify customer touchpoints ☑ Map the current state ☑ Understand motivations, frustrations, and what tools you’ll need ☑ Evaluate, adjust, and get ready to grow

Use the data and let your customers do the rest.

6 customer journey map templates

Having a template is a great way to get started. There are a few different templates to choose from:

Current state

The current state journey map visualizes the current experience with your product or service. It involves defining the scope of the customer experience with customer touchpoints.

This type of customer journey map is designed with the considerations, thoughts, feelings, and actions of your customers in mind. Current state mapping is a practical approach to identify existing pain points and create a shared awareness of the end-to-end customer experience.

Day-in-the-life

A day-in-the-life journey map is another simple grid map based on time, created especially for the daily grind of the customer. Instead of different journey stages, it represents times in the day related to actions based on decisions in the path of purchasing.

This template helps you visualize your customer’s daily routine even if these actions are outside your company. It typically is organized chronologically to systematically show the course of the habits of the day.

Day-in-the-life's are great for giving you insights into all the thoughts, needs, and pain points users experiences throughout their day. You can use this type of map to evaluate when your product or service will be most valuable in your customer’s day.

Future-state

With a future-state journey map template, your goal is to learn how your customers feel about a new product launch or about how they will require your service in the future.

Future-state journey mapping is a useful approach to explore possible customer expectations and to create new experiences. Mapping out a future customer journey helps to align your team around a common goal—the betterment of the customer experience.

Service blueprint

A service blueprint helps you design a roadmap of your service process—much like building a house. The goal is to be able to make projected changes to the service where needed and to be able to visualize each step in the eyes of the customer.

Service blueprint maps reflect the perspective of the organization and its employees and visualize the things that need to happen behind the scenes in order for the customer journey to take place.

Service blueprints are created when making procedural changes, or when trying to pinpoint solutions to roadblocks in the customer journey on a website.

Circular

A circular customer journey map is just that—circular instead of linear or graph-like to showcase a different type of business model. For instance, a SaaS company may find it more useful to visualize the customer journey as a loop or wheel.

This subscription-based journey map does a nice job of portraying both the customer interactions and sentiments, as well as their journey from awareness to purchase.

Empathy

The empathy journey map is a bit different because it aligns with the customer's feelings and emotions. Empathy is a big factor in the customer journey and this template is designed to help teams align their customer journey mapping exercise with these types of needs.

With empathy, you can get into your customer’s shoes and truly feel what they feel as it pertains to your product or service.

Customer journey map tools

As with anything, you’ll need customer journey mapping tools to help you. The key is to find the right tool that works with your team and workflow.

Here are a few tools to consider:

With the right map and the right tools, you can overcome roadblocks and open a path to scalability and success.

Enhance your journey mapping process with customer intelligence. Look at behavioral data points like heatmaps, scroll maps, and other insights you can glean from session replay. Combining these quantitative and qualitative insights will help you in your journey-mapping process.

Using journey maps to drive organizational change

It may not be easy to get buy-in to support the changes in strategic planning that result from customer journey mapping.

You can use what insights you’ve gleaned from the current state journey map in these beneficial ways:

Align your organization around the customer viewpoint. Engage with each department and set up a commitment to put the customer experience moments top of mind with an initiative for growth.
Enlist team members and partners to generate empathy for customers. Use your journey map to bring together relevant teams to train on customer experience best practices.

Supplement a new strategy with internal communications that encourage better customer service. As new initiatives roll out, use internal channels to communicate how you’re improving the experience of the customer and how team members can help.

Checkout friction: The hidden leaks in your conversion flow

Kailey Boucher — Wed, 11 Feb 2026 12:00:00 GMT

You’ve done everything right. The campaign was a hit, traffic is surging, and carts are being filled. But the numbers don’t lie: your conversion rate is flat.

Shoppers don’t abandon checkout because they lost interest. They leave because something breaks their momentum, like an error they can’t recover from, a page that stalls, a total that suddenly changes, or a button that doesn’t respond.

Across more than 9 billion user sessions analyzed in 2025, the signals are clear for retail products:

Rage clicks increased 56% year over year
Exit rates after any error rose 40%
Bounce rates climbed 64%

These aren’t small usability issues. They’re measurable revenue risk.

→ If you want to see how your experience compares, explore the interactive report for digital product benchmarks.

In most product analytics, checkout appears as a clean funnel with a drop-off. What’s missing is the why. Without behavioral visibility into the exact moments people get stuck, teams end up fixing the wrong problems, or nothing at all, while revenue quietly leaks from the highest-intent part of the journey.

What is checkout friction?

Checkout friction is anything that slows or stops a ready-to-buy customer from completing their purchase, confusing form fields, unexpected fees, payment declines, or pages that load just a little too slowly.

The numbers are stark. Global cart abandonment hovers around 70%, and in some verticals, like luxury retail and travel, it's even higher. Research from the Baymard Institute suggests that large ecommerce sites may improve conversion rates by as much as 35% through checkout design improvements alone.

For a business generating $10 million in annual revenue, even a modest 3–4% lift from targeted friction fixes could translate to hundreds of thousands in recovered revenue, often from changes that take weeks, not months, to implement.

Behavioral data makes this visible. Fullstory shows where friction occurs in real user sessions, not just in aggregate charts that smooth over detail. When you can watch a shopper rage-click a broken coupon field or abandon after a payment timeout, guessing turns into fixing. The full checkout journey becomes clear, from “Add to cart” through confirmation.

Checkout friction isn’t just a UX problem. It’s a revenue problem. And the faster you can see it, the faster you can stop it from costing you paying customers. Once you identify exactly where users abandon their carts, you can deploy the right CRO tools to test, iterate, and implement high-converting checkout flows.

The real reason users drop off at checkout

Think of your checkout as a product you offer. A customer “hires” it to do one simple job: help them finish a purchase, quickly and without any surprises. When your checkout fails at that job, your customer fires it by simply closing the tab.

The reasons they leave can fall into two categories: the seen and the unseen.

The seen: The obvious culprits

A page that takes too long to load
A form field that won’t accept a valid phone number
A surprise shipping cost that appears at the last second.

These are the annoyances users know are frustrating them.

The unseen: The silent killer of conversion

The subtle hesitation you can't see
The moment of confusion over a vague button
The flicker of doubt when a page layout feels unprofessional or untrustworthy

These micro-experiences don’t always cause a rage click, but they erode trust and momentum until the user just… gives up. This is the mystery that traditional analytics can’t solve.

Where checkout friction hides

Not all friction carries the same weight. Some issues affect only a small slice of shoppers, while others quietly drain conversion across your entire customer base. The challenge is that many of the most damaging problems look minor in isolation...until you see how often they occur.

Below are ten friction points that consistently surface across ecommerce experiences, especially in high-traffic or mobile-heavy journeys.

1. Forced account creation

Requiring an account before checkout still ranks among the most reliable conversion killers. Many shoppers prefer to move quickly with guest checkout and will abandon rather than create another password. Let the purchase happen first. You can invite account creation after trust—and revenue—are already secured.

2. Unclear or hidden total costs

A large share of abandonment stems from unexpected costs appearing late in the flow. Shipping, taxes, or added fees introduced at the final step break momentum at the worst possible moment. Clear, early pricing keeps expectations aligned and reduces last-second exits.

3. Slow mobile performance

Speed shapes confidence. Even small delays on mobile can meaningfully reduce conversion, and pages that take several seconds to load lose a significant portion of shoppers before checkout fully appears. Performance is not just technical hygiene—it directly protects revenue.

4. Limited payment options

Shoppers have preferred payment methods. When wallets, local payment methods, or buy-now-pay-later options are missing, friction appears immediately. Offering familiar, low-effort payment paths has become a baseline expectation rather than a differentiator.

5. Weak or missing trust signals

Checkout is the moment of highest perceived risk. Without visible reassurance—security indicators, recognizable payment logos, clear return policies—hesitation grows. Even subtle uncertainty can be enough to stop a completed purchase.

6. Form errors without clear recovery

A meaningful portion of payment failures comes from simple user mistakes. When error messages lack clarity or real-time validation is missing, shoppers cannot easily recover. Clear guidance and immediate feedback reduce frustration and keep progress moving.

7. Promo code failures

Few experiences generate frustration faster than a coupon that refuses to apply. Expired promotions, case-sensitive validation, or backend timeouts create outsized emotional friction—especially during peak events. Promo flows deserve close monitoring because small failures scale quickly.

8. Address and form field friction

Forms that resist autofill, validate incorrectly, or truncate on smaller screens create unnecessary effort. Small usability improvements—smart defaults, accurate validation, seamless copying between shipping and billing—remove steps shoppers never wanted in the first place.

9. Overly aggressive security controls

Fraud prevention is essential, but misconfigured rules can block legitimate customers alongside bad actors. False declines rarely appear obvious in aggregate reporting, yet they represent direct, preventable revenue loss. Ongoing review of decline patterns is critical.

10. Weak confirmation and post-purchase clarity

Friction does not end at payment. Missing order details, unclear delivery timing, or uncertain confirmation experiences introduce anxiety after the transaction. A strong confirmation moment reinforces trust and reduces support burden.

Each of these trouble spots shows up as behavioral signals: rage clicking the “Apply coupon” button, repeated back-and-forth between the cart and shipping pages, or multiple form validation errors in a single session. When you’re watching real sessions in a tool like Fullstory, these patterns become obvious and fixable.

How to find checkout friction with behavioral analytics

Checkout friction isn’t solved with opinions or best-practice lists. It’s solved with a repeatable diagnostic process of product analytics that surfaces the problems actually costing revenue.

Here’s a practical way to do that, from start to finish.

1. Establish a single source of truth for your checkout experience

Begin with a focused checkout health dashboard inside your analytics platform. The goal is to track the signals that map directly to revenue risk.

Prioritize metrics that answer five questions to create a baseline. Without it, every optimization conversation becomes subjective.

Are shoppers entering checkout? → checkout start rate from cart views
Where does checkout progress stall? → step-to-step funnel across shipping, payment, and confirmation
What breaks along the way? → validation errors, payment failures, form issues, timeouts, and JavaScript issues
Where is frustration found most? → rage clicks, dead clicks, and rapid backtracking
How long does purchasing actually take? → total checkout duration and time spent per step

2. Locate friction inside the checkout funnel

Next, move from totals to transitions. Instead of viewing checkout as one funnel, break it into discrete funnels:

From cart → to shipping
From shipping → to payment
From payment → To order confirmation

When one funnel shows an abnormal drop-off, you’ve found your investigation zone. That single step often explains the majority of lost revenue.

3. Watch the failure, not just the metric

Metrics tell you where to look. Session evidence tells you what actually happened. Review sessions that exited at the problematic step and look for repeat patterns:

Payment buttons that appear clickable but fail
Promo codes that reject valid input
Authentication modals that hang or loop
Address validation that blocks legitimate entries

These issues rarely surface in aggregate reporting. They become obvious the moment you watch real user behavior.

4. Segment users until the pattern sharpens

Aggregate averages hide the most damaging friction. Break checkout behavior down by:

Device type (mobile vs. desktop friction often differs completely)
Browser and OS (payment failures frequently cluster here)
Geography (currency, shipping cost, and payment availability vary)
Traffic source (email, paid, and organic visitors carry different intent)
Customer state (new vs. returning, logged-in vs. guest)

The goal is simple: Find the specific audience experiencing disproportionate failure. That’s where fixes produce the fastest revenue impact.

5. Let behavioral data surface what you don't see

Even strong hypotheses miss things. AI-powered analytics agents like StoryAI, can scan session patterns to uncover friction clusters teams didn’t think to investigate:

Abandonment tied to a specific browser version
Higher failure after viewing multiple products
Hidden error states triggered only under peak load

This shortens discovery time from weeks of manual analysis to minutes of surfaced insight.

6. Turn diagnosis into an operating rhythm

One investigation isn’t enough. Checkout friction evolves with releases, integrations, traffic spikes, and changing customer behavior. High-performing teams treat this as a continuous practice:

Weekly checkout friction review during normal periods
Daily monitoring during peak events or campaigns
Post-release validation after any checkout-related deployment

The return compounds quickly. Most teams recover meaningful revenue within the first diagnostic cycle—often from fixes measured in days, not quarters.

How to reduce ecommerce checkout friction and boost conversions

Design choices account for a significant portion of checkout friction. The good news: proven patterns exist that consistently boost conversion rates across ecommerce verticals. Here’s what works.

Streamline your forms ruthlessly

Every required field introduces risk: confusion, validation failure, or hesitation. Focus on:

Limiting required inputs to only what fulfillment truly needs
Using clear, specific labels that match how customers think
Grouping related information logically across shipping and payment
Enabling inline validation with clear recovery guidance
Supporting browser autofill and accurate address lookup

Make pricing transparent from the start

Surprise pricing breaks trust faster than almost any design flaw. Strong checkout flows:

Display taxes, shipping, and duties early—ideally before checkout begins
Avoid last-step cost changes that trigger reconsideration
Provide at least one transparent, lower-cost delivery option

Build trust throughout the checkout flow

Checkout is the highest-risk moment in the journey from the customer’s perspective, and confidence keeps momentum moving. Reduce hesitation by:

Placing visible security and payment indicators near sensitive fields
Explaining returns, refunds, and data handling in concise language
Reinforcing legitimacy through recognizable proof points where appropriate

Nail the micro-interactions

Seemingly small interaction details strongly influence whether customers finish. High-performing experiences:

Show clear progress through checkout steps
Keep an editable order summary visible throughout
Use explicit, action-oriented calls to action
Deliver an unambiguous confirmation with next steps and delivery expectations

When these moments feel smooth, completion rates rise without redesigning the entire flow.

Behavioral data validates the impact—fewer error events, less backtracking, faster completion, and cleaner funnel progression.

How Fullstory helps fix checkout friction

Here’s the reality: checkout friction never goes away completely. New features, new integrations, new traffic patterns, and new customer expectations constantly create new opportunities for friction to creep in. The brands that win at ecommerce aren’t friction-free—they’re friction-aware. They have systems that surface friction quickly and processes that fix it before it costs them serious revenue.

Fullstory’s role in this practice is specific.

Autocapture means every interaction is recorded without manual tagging—so when friction appears, you don’t discover you forgot to instrument the relevant events.
Session replay lets you watch exactly what happened, not just that something happened.
Heatmaps show where customers engage and where they struggle.
Funnels quantify drop-offs between checkout steps.
StoryAI turns messy behavioral data into clear, prioritized insights about where to focus next.

The future of ecommerce checkout is personalized, context-aware, and driven by AI-ready behavioral data. Customers expect shopping online to be faster, smoother, and more intuitive every year. The checkout experiences that meet those expectations—the ones that reduce friction continuously and adapt to customer behavior in real time—will capture the revenue that frictionless alternatives leave on the table.

A clearer checkout starts with clearer visibility.

Checkout rarely fails in obvious ways. It fails in small, compounding moments—an error that blocks progress, a delay that creates doubt, a step that quietly pushes someone to leave.

Fullstory brings those moments into view. By capturing the entire checkout journey, behavioral data shows where shoppers struggle, where they stop, and what caused it. With that level of visibility, prioritization becomes clearer, decisions move faster, and improvements start protecting revenue instead of chasing it.

If you want a practical place to start, download our 1-pager on fixing cart and checkout friction.

Or explore FullstoryFree to see some of these signals firsthand, no credit card needed. It includes:

Up to 30,000 captured sessions each month
12 months of session replay retention
12 months of behavioral analytics history

The top technical considerations when assessing session replay solutions

Joel Webber — Thu, 08 Jun 2023 04:00:00 GMT

In the world of session replay, there are plenty of tools out there that can show you approximations of what your users saw and did on your site or app. But too often, they’re just that: approximations.

When done right, session replay can be far more than the sum of its parts. While a shortcut session replay provider might save you a few dollars up front, the long-term costs and risks of approximated session replay will quickly outpace the benefits. In a previous article, we talked about how the best version of session replay is operational: fitting into and empowering your teams’ existing workflows rather than acting as an add-on or afterthought.

Most session replay tools record session data and event data separately, then piecemeal these datasets together. This tells an incomplete story because it limits the interaction data that’s captured.

On the other hand, operational session replay uses autocapture to extract and analyze all events on the server, making session data the source for all ensuing digital experience insights. This means a client receives complete digital experience data that can be retroactively analyzed without introducing the security and performance risks inherent in other methods of capture.

Ultimately operational session replay helps you grow revenue smarter, save time and money, and innovate with confidence on web and mobile—and you shouldn’t settle for anything less in your tech stack.

Fullstory’s session replay has been built from the ground up to provide complete, high-performance session replay that’s low-lift for your team. Here, I’m going to walk you through the technical foundation that sets operational session replay apart from lesser versions.

Accuracy, fidelity, and completeness

Fullstory’s Digital Experience Intelligence (DXI) platform is built on top of a patented data engine that captures and interprets complete, reliable session data to power your insights. Here’s how Fullstory ensures you have accurate, complete, high-fidelity digital experience data at all times.

Autocapture

Fullstory’s proprietary approach to session replay is to autocapture sessions completely and accurately, then extract insights and analytics from that session data. Other session replay tools use a more old-school approach that requires users to instrument or tag the events they want to analyze–instead of enabling tagging as an option–and then add on session replay as a sidecar. Because the analytics data doesn’t come directly from the sessions in this method, the product analytics and session replay insights won’t be a perfect match, which can leave detrimental blind spots in your analyses.

Broad feature support for mobile

Teams need a DXI platform that understands the complexities of ever-evolving app frameworks. Fullstory for Mobile Apps was built to be compatible with as many apps as possible, supporting platforms and frameworks that are:

Used most often for building mobile apps (including Swift, SwiftUI, and Java)
The most sensible and relevant for developing mobile apps
At the cutting edge of app development trends

We stay on the pulse of app framework development. When a new framework comes on the market, we go through an auditing process to determine if Fullstory should support it.

One of the most important requirements we look for is whether the framework meets our privacy and performance standards. If it passes the audit, we then scope the work we need to complete to support it. That means you get to stay at the forefront of app development without sacrificing the DXI you need to make your app stay great. Your engineers are constantly improving your app—make sure you have a DXI platform that can keep up.

Tabbed browsing

The average person has nine browser tabs open at any given time, including multiple pages from the same website to make it easier to browse. Fullstory is the only digital experience solution that can provide a complete view of how your customers navigate between multiple browser tabs on your website. With tabbed browsing in session replay, you can understand how users move around your site more clearly than ever.

Though tabbed browsing has become an increasingly popular way to navigate the internet, it’s historically been very difficult for companies to understand what prompts users to open new tabs, what they’re doing in each tab, and how they move between them. Now, with Fullstory session replay, this DX data is readily available.

Debugging tools

The depth of Fullstory’s session data provides invaluable context for reproducing and fixing bugs. With access to network and console data, engineers using Fullstory can debug with lightning efficiency.

Console: The Fullstory Console view allows you to capture messages reported in a customer's browser's console. When Fullstory captures those Console messages, such as console.log(), console.error(), console.info(), and console.warn(), you can see errors that occur "in the wild," so you can zoom in on exactly what caused errors to occur, giving you more time to go back to doing other things.
Network Capture: Network Capture provides the ability to identify and analyze Network errors via DevTools in Session Playback for mobile sessions. This gives you the ability to create segments and funnels based on common network errors for Mobile Apps to understand their impact and prioritize fixes accordingly.

Complete, reliable data

When it comes to capturing digital experience data on your site or app, relying on sampling is necessary in many instances. However, your digital experience solution should never force you to rely on random sampling as the basis of your analyses. After all, how can you find, quantify, and fix DX issues if you don’t have an accurate representation of your digital experience?

The breadth of DX events Fullstory can produce and analyze is greater than other solutions because the events are extracted from session data. Because the event extraction is not running on the client’s side, Fullstory can handle most queries retroactively (rather than needing to tag first and then capture them), so you can answer questions as they crop up.

Here are three examples of powerful Fullstory insights that would be impractical or impossible to extract on the client’s side:

Dead Clicks, a key frustration heuristic, relies on the ability to correlate user actions with DOM changes across time. This would be difficult to do on the client’s side.
Pinch-to-zoom, another frustration signal captured in Fullstory, requires a complex analysis of tap gestures.
Detections, an important privacy feature, requires deep scanning of all session data.

Retroactive queries require an indexing system that allows interactive search across the extraordinary number of events that autocapture generates. Fullstory is the only tool on the market that has the ability to index DX events at this scale.

Autocapture is only as useful as its safety features, though. Fullstory’s Private by Default approach ensures that no page elements are captured unless specifically allow-listed. This can include images, prices, inputs, product names, URL parameters, network data, and much more. Fullstory is able to accurately capture a mixture of masked and unmasked content on the web without losing fidelity or breaking the layout. Many DX tools claim to be “private by default,” but can actually only deliver this capability on form fields (which aren’t as fragile as other page elements). To be able to fully trust your solution’s privacy capabilities, you need to be absolutely certain that it’s masking everything potentially sensitive right out of the box—even things you don’t see like network data information and URL parameters.

Privacy and security

Fullstory’s session replay was built to deliver maximum digital experience insights while maintaining end-user privacy every step of the way. With industry-leading privacy controls, Fullstory ensures session replay remains flexible and private-by-default across all data types. Here are a few ways we accomplish this:

Authorized Fullstory users are able to mix and nest masked and unmasked privacy rules in the app structure
Through network and URL capture, without the need to explicitly mask unsafe values
Create and mix privacy rules are both in code and in configuration
Detections and Redactions are available to deal with any accidental leaks

What’s more, because Fullstory extracts events on the server, there’s no need to ever deploy poorly vetted, workaround scripts to a customer’s site that could open it up to risks. (We’ll cover this more in a later section.)

Performance

Your quest to understand bugs and digital experience issues should never create more issues for the end-user. That’s why Fullstory is built to have the lightest possible touch on the client, ensuring it never has a noticeable impact on site or app performance.

How? We use:

Less thread blocking than other DX platforms
Optimized wire format that doesn’t rely on background compression to hide chatty protocol
No video or screenshots on mobile capture, which dramatically reduces CPU and network load (more on that here)

Internal work and maintenance

Implementation

Many session replay platforms require some level of customization to get them up and running on your site or mobile app. Relying on your vendor to write bespoke code isn’t ideal for multiple reasons:

It adds substantial time to value for your organization
It introduces the security risk of having a vendor write code to your website on your organization’s behalf
It doesn’t result in retroactive data

Implementing Fullstory is as simple as adding the data capture snippet into the <HEAD> element either via your Content Management System (CMS) or directly into your application’s code. Because there’s no tagging required to begin capturing sessions, your digital experience data is retroactive and searchable by design.

Maintenance

Session replay tools that require heavy tagging or instrumentation require a significant amount of labor to continue to capture the data you care about. And when the data you care about changes, your tags will have to change too. Whether your internal team or your vendor is doing that work, your organization is paying for it. With operational session replay, the data you care about (and the data you didn’t even know you would care about) is at your fingertips with very little upkeep.

You’re probably already leveraging session replay to understand your digital experience. But are you settling for a bolted-on version of session replay that isn’t operational? At best, you’re likely missing out on insights and data. At worst, tools like this could actually do more harm to your digital experience than good.

Learn more about how Fullstory’s patented DX data engine helps you pinpoint bugs and prioritize fixes here.

6 things to look for in a session replay platform

The Fullstory Team — Mon, 15 May 2023 04:00:00 GMT

In digital experience analytics, “session replay” is a phrase that’s thrown around a lot. And whether or not you realize it, all session replay is not created equal. It may not seem like a big deal at first blush, but having top-tier session replay is critical to digital experience optimization (and explain what sets Fullstory’s apart).

In fact, most session replay tools are lacking or incomplete in some way. The majority of tools record session data and event data separately, then piecemeal these datasets together. This tells an incomplete story because it limits the interaction data captured—not to mention this method is often harmful to performance and can open your site to data security risks.

Fullstory, on the other hand, provides what we call operational session replay.

It gives you crisp, complete X-ray vision into the ongoing user experience and elevates your existing digital experience strategy. Operational session replay uses autocapture to extract and analyze all events on the server, making session data the source for all ensuing digital experience insights. This means a client receives complete digital experience data that can be retroactively analyzed, in a performant and privacy-first way.

Why having best-in-class session replay matters to you

At the end of the day, operational session replay helps organizations grow revenue smarter, save time and money, and innovate confidently. In the current economic climate (and, really, in any economic climate), teams should be squeezing the most utility and value out of each of their platforms. Fullstory’s approach to session replay ensures it’s impactful and used often–and by many members of many teams.

Here are six important lenses through which it’s critical to view and evaluate session replay platforms.

1. Accuracy, fidelity, and completeness

Fullstory’s Digital Experience Intelligence (DXI) platform is built on top of a patented data engine that captures and interprets complete, reliable session data. By autocapturing session data, we ensure every DX datapoint is at your fingertips. With this autocapture approach, signals, insights, and session replay are able to work together to provide a truly complete picture of the digital experience.

Other solutions might say they’re using autocapture–but they aren’t. With other solutions, these fundamental elements are often siloed or Frankensteined together—leaving gaps, questions, and missed insights.

Here are some signs that–despite what they might say–a session replay tool doesn’t use autocapture (which means they’re leaving key insights on the table and creating risk for your business).

Tagging

Session replay tools that rely on manual tagging rather than autocapture can only pinpoint the friction you anticipate. Let’s say you notice a behavior in a session that you want to learn more about. When you’re limited by tagging, you can’t learn about behaviors you didn’t tag—you’d need to tag that event and wait for days or weeks while data is collected to be able to analyze it.

Fullstory is the only session replay solution that automatically gives you all the data to dive into specific instances of struggle without needing to define what you want to monitor.

Random sampling

Often, session replay tools use heavy sampling to gather session data. While sampling isn’t always a bad thing, random sampling can create dangerous biases in your data. Plus, tools that take a random sampling approach to session replay likely can’t capture all sessions without harming a site’s performance.

While Fullstory does employ sampling at times, it’s anything but random, and therefore tells a much more accurate data story than other tools.

Mismatched quantitative and qualitative data

Many session replay tools attempt to offer cohesive quantitative and qualitative information—but fall short. This is because these tools have essentially “bolted on” session replay to their product, while continuing to derive the quantitative counterpart from an entirely different data system. So you may be getting both types of data—the sessions and the analytics—but they aren’t originating from the same source of truth, which degrades their reliability.

With the digital experience data engine at its core, Fullstory takes a different approach. Quantitative DX insights are derived directly from session-capture data, ensuring cohesive, apples-to-apples analysis across quantitative and qualitative data for every user interaction.

2. Performance

Operational session replay shows you exactly what a user saw and did on your site or app without slowing it down or negatively impacting the user experience. If session replay is getting in a user’s way, it’s not operational. Unfortunately for mobile apps, the industry standard is to screenshot or video record screens—but this can have negative and costly effects on mobile app performance and end-user privacy.

Instead of a clunky, outdated approach that slows sites and apps down, Fullstory captures the structure of your site at the DOM level (and the delta between the view hierarchy of your mobile app) and uses that information to recreate sessions with perfect fidelity and no impact on performance.

And that's critical: 72% of global consumers say that slow page load times are their most frustrating digital issue.

3. Omnichannel journey

These days, people have multiple devices—and preferences about what they do on which device. Operational session replay should follow a customer or user’s full journey across tabs, devices, and sessions. If your session replay tool can’t, it’s impossible for you to understand how a user progressed through the digital customer experience.

Mobile apps

Capturing mobile app sessions is a requirement for understanding the complete digital journey. The average person spends upwards of three hours on their smartphone every day. If you’re not capturing those sessions, you’re missing a huge piece of the puzzle.

Fullstory’s session replay for mobile apps is considerably ahead of others on the market in its scale and accuracy of data capture. We take the same approach that we do for desktop session replay, capturing the structure and data needed to reproduce a session with perfect fidelity. And, just like web, the insights are derived from session data, rather than capturing the two separately and duct taping them together later.

Tabbed browsing

On average, an internet user has nine tabs open at any given time. Are you still able to understand what a user is doing or trying to accomplish when they open new tabs on your site? If the answer is no, you’re missing key digital experience data.

Fullstory offers the only session replay with detailed visibility and interactivity surrounding browsing behaviors across one or multiple tabs. You can now accurately visualize the creation, closure, and navigation between tabs.

4. Privacy and security

It’s a given that you need session replay analytics to keep your end-users’ data safe and where it should be—on their devices. Fullstory’s advanced approach to privacy gives you the power and confidence to experiment with changes to sensitive flows (like your checkout flow, for instance) without danger of losing visibility into what is or isn’t working.

If you capture sensitive information—even unintentionally—you risk eroding trust, and it can open your organization up to large fines. Safe, reliable privacy controls free you to iterate, experiment, and learn without fear of losing data or accidentally capturing unwanted information.

Some platforms only block specific data out-of-the-box that they deem to be Personally Identifiable Information (PII), leaving room for error. We think differently. We prioritize partnership with you when it comes to privacy. With features like Detections, Fullstory automatically monitors your site for select categories of sensitive data, such as credit card numbers and passwords, and proactively identifies and categorizes PII, so your teams can take action quickly. This is a win for you, your users, and your security team.

5. Implementation and maintenance

Operational session replay doesn’t require a huge lift to implement or constant, costly maintenance from your team or your vendor’s. Ultimately, session replay should help you save time and money—not use a bunch of it. Let’s look at this point from two angles.

Implementation

Most session replay vendors’ tags require customization. In this case, your business has to rely on the vendor to write bespoke code to capture all of the data you care about during the initial implementation—which is time-consuming for both parties. Plus, the “data you care about” will likely change over time, requiring updates to that code at every turn.

This adds substantial time to insights and introduces the security risk of having a vendor write code to your website on your organization’s behalf–not to mention doesn’t even result in retroactive DX data.

Implementing Fullstory is as simple as adding the data capture snippet into the "<HEAD>" element either via your Content Management System (CMS) or directly into your application’s code. Captured sessions will appear in Fullstory within a few minutes, as long as users are visiting your site and navigating.

Maintenance

Operational session replay shouldn’t require ongoing manual labor—by you or your vendor—to keep running smoothly. With many session replay tools, either you or the vendor must continuously work behind the scenes to keep things running as you push various updates to your site or app. Your organization pays for this one way or another—either with your own time or by paying for it in vendor services.

6. Collaboration

Operational session replay should bring teams together around a single source of truth. Tools that pull quantitative and qualitative information from different data systems, as discussed earlier, open up room for doubt, disagreement, and distrust across teams.

Fullstory’s session replay enables teams to move seamlessly between verifiable quantitative and qualitative information. This means people in different roles around the organization can collaborate more effectively, work from the same trusted data, and improve cross-functional alignment.

Chances are you’re already using or considering session replay in some form—but is it truly operational? If it’s not accurate, performant, secure, flexible, low-lift, and collaborative, then you’re not only missing out on critical insights, you’re sacrificing time and money.

For a deeper look at the only operational session replay solution on the market, get a Fullstory demo.

6 of the best product analytics tools to perfect your digital experience

The Fullstory Team — Sun, 04 Aug 2024 11:00:00 GMT

Data-driven organizations are profitable organizations. They’re 23 times more likely to acquire customers, six times more likely to retain customers, and 19 times more likely to be profitable as a result.

To be data-driven, you must be selective about the product analytics tools you choose and use. There are several product analysis platforms on the market, and it can be easy to get lost in the sea of features and functionalities.

At Fullstory, we’re a bit data-obsessed, so we researched and compiled a list of the best product analytics tools available today.

Now, let's get into a quick view of 6 of the best product analytics platforms.

6 of the best product analytics tools

Fullstory - ideal for deep behavioral intelligence and product analytics
Mixpanel - notable for predictive analytics
Amplitude - notable for data visualization
Pendo - notable for product feedback
Heap - notable for ease-of-setup
Google Analytics - notable for website traffic analysis

See the full list

What is product analytics?

Product analytics is a set of tools that allows teams to assess the performance of the digital experiences they build. Simply put, product analysis lets you track user actions on a website.

Product analytics is the most objective way to receive feedback about your products by providing interpretive information to:

Optimize performance
Detect problems
Determine feature upgrades
Connect customer engagement with value

Analytics tools for product managers can be used to determine which features they should invest more in, where the customer experiences friction, and ways to diagnose problems. This will, in turn, reduce churn and personalize engagement for customers and users.

What is a product analytics tool?

Product analytics software analyzes and summarizes user sessions and interactions in software applications for both web and mobile devices.

The goal of any product analytics platform is to capture and examine events or trends in the customer journey and report real-time insights to begin implementing improvements.

This data is a constant source of truth, and these metrics will provide a level of insight that cannot be found without being a user.

What should I look for in a product analytics tool?

As far as what it can do for you, product analytics tools should have at least three main categories that should be reviewed:

Product or website health: focusing on security, performance, and dependability
Product engagement: focusing on element participation, time spent on pages, and customer behavior and involvement.
Product behavior: focusing on navigation, utilization, and experience

Each of the categories above will give you an arsenal of areas to consider, each showcasing its own essential part of the customer experience.

Your product analytics tool should answer these questions.

How can user engagement be increased for new and existing features?
How healthy are our products?
How quickly can we visualize all our data to monitor goals and KPIs?

Many tools have features that will cross over the three main categories and work together.

Why are product analytics tools important?

Product data analytics is one of the most efficient ways to proactively solve problems with your product.

Without any quantitative or qualitative data to support decisions, your teams would be in the dark and could make the wrong assumptions.

On the contrary, having access to reliable qualitative and quantitative data provides a foundation for the following:

Make your case on which features to build next
Find problems that need to be addressed
Avoid gaps in the customer experience

How do product analytics platforms work?

A data analytics platform ultimately works by supporting data-intensive applications and simultaneously supporting hardware in real-time.

Product analytics integration with third-party tools is critical for a full understanding of a user's behavior.

Here are a few concepts to be aware of when it comes to learning how product analysis works:

Data warehousing is constructed by integrating data from multiple sources that support analytical reporting, queries, and decision-making. Data warehousing involves data cleaning, data integration, and data consolidation and is a key component for keeping data secure and organized.
Data governance encapsulates the policies and practices implemented to securely manage the data assets within an organization to keep data safe, clean, and organized.

The bottom line is you want your data to be secure, organized, and safe.

Common products analytics platform features

As with any tool, your product analytics platform features should be tailored to what your business needs.

It’s important to understand the most popular features you should evaluate when making your decision—because you’ll need them.

These are typical in most product analytics tools:

Automatic data capturing
Detailed session and user segmentation
Ability to add and analyze events with one click
Custom dashboard, segments, and reports
Key KPI dashboards
Date-based data filtering and data funnels
Session replay
User property attachment
User journeys diagrams and drill-down
Conversion rate dashboards
User interactions dashboard
And even more insights to help you make data-driven decisions

Top product analytics platforms:

Fullstory

Fullstory is a behavioral data platform designed to help companies answer questions about customer experience. While alternatives may excel in isolated metrics or workflows, Fullstory connects customer interactions to the metrics that matter, offering a more cohesive view of the user journey.

Built on a powerful analytics engine, the behavioral data platform delivers a retroactive view of how people interact with your site or app.

Who is it for?

Any team that builds, sells, or supports digital experiences can use Fullstory’s detailed user data to align around customers and break down data silos. Customer support solves product issues and helps users with digital intelligence. Technical and non-technical teams alike rely on Fullstory for data, reducing costly back-and-forth and increasing throughput.

Pros

This behavioral data solution is dynamically built to integrate with your tech stack, with out-of-the-box integrations and an open API. Specifically, Tray.io, Workato, and Zapier—to streamline automation, help you scale, and keep processes running smoothly.
Fullstory has tagless autocapture, making data-capture and tracking simple and fast for any team. Every click, scroll, and interaction is recorded automatically from day one.
It’s searchable and extensible and allows you to see firsthand which investments are making an impact.
Leverage a limited version of intelligent digital experience analytics with three seats, 1,000 sessions per month, and up to one month of data storage.

Cons

Pricing is oriented toward mid-market and enterprise — not practical for early-stage startups or solo teams with limited budgets.

Mixpanel

Mixpanel is a product analytics tool that helps teams use user data and improve their user experience.

Mixpanel offers solutions to help measure and predict customer usage across unique segmentation needs.

Who is it for?

Mixpanel is used by product and marketing teams, as well as product managers who want self-service and a technical path to gathering product analytics. Mixpanel is for Product and Customer Experience teams in the B2B and B2C spaces.

Pros

The Group Analytics add-on lets you calculate metrics at the account or company level, like product adoption, active usage, and retention.
Customizable dashboards offer a series of options to manage your metrics.
Mixpanel enables interactive event analysis from Snowflake, BigQuery, or Redshift. They make it easy to explore product and user behavior data without SQL.

Cons

Mixpanel’s technical implementation difficulty level is on par with its closest competitor, Amplitude.
The properties can be overwhelming with how many there are, it makes it hard to know if the correct attributes are running the report.
There are discrepancies between its data and the data in a certain data warehouse.
Mixpanel does not feature tagless autocapture, so goal/event instrumentation can be a lot of work

Amplitude

Amplitude is a product analytics tool that helps product teams understand user behavior. Amplitude has a variety of visualizations and data tracking options.

Who is it for?

Amplitude suits teams in the B2B and B2C space that focus on product and customer experience. It is also used by technical product or data science teams.

Pros

Amplitude is enterprise-grade
Out-of-the-box reports enable you to answer questions and understand how you can create customer and business value.

Cons

Amplitude requires a lot of technical expertise
Their free plan does not include many of their features such as Compass (a retention or conversion prediction feature) or their SQL integration—these are only available with a subscription.
Users have stated that the database function tends to be a bit slow, it could be more real-time to work faster.
Does not feature tagless autocapture

Pendo

Pendo captures quantitative and qualitative insights, customer feedback, and general product metrics across both web and mobile applications.

Who is it for?

Pendo was made for Product and Customer Experience teams in the B2B and B2C spaces.

Pros

Pendo may be a good fit for customer-focused product teams at any company.
They offer fast setup and easy guided tutorials for onboarding.

Cons

No session replay, no frustration signal detection, and no tagless autocapture — behavioral gaps remain even with strong product metrics.
Pendo offers great features for product management workflows and planning, but the user experience can feel clunky.
It has been known to lack functionality to track different types of user activity, and more customization on what to track.
Does not feature tagless autocapture

Heap

Heap is a product analytics tool to understand individual user behavior. By tracking user events and customer analytics, product managers can segment and review customer experiences throughout the product easily.

Who is it for?

Heap is best used with Product and Customer Experience teams in the B2B and B2C world.

Pros

Heap tracks user events without any technical configuration on your end.

Cons

Heap is web and mobile-only, with limited customizability for reports and dashboards.
Reporting does not allow for users to see typical paths between both a starting point and an ending point and only shows 5 steps.
Unlike Fullstory, Heap lacks session replay and automatic frustration signal detection — you can see that users dropped off, but not the behavioral context of why.

Google Analytics

Google Analytics is a well-known player in the analytics space, helping monitor user traffic and general product usage analytics. The main power behind Google Analytics is in its integrations with other tools to easily gather data to be used throughout your tech stack.

Who is it for?

Google Analytics is good for the individual website owner and product and marketing teams in the B2C space.

Pros

Google is a trusted source. The analytics are built to work with Google’s standards so you can trust your analytics insights for the right customers.
Google is free and always will be.
There are benefits to the other Google tools it can connect to, and it integrates with any business intelligence or data platform that you use.

Cons

Google Analytics is a free product analytics tool but is not able to provide full detail on a user's behavior, nor provide session replay.
Its dashboard is a bit robust of an interface and that can be challenging for new users.
The free version does not have accurate cookie reporting—only sampling. Cookies can display up to 10% variance compared to other tags.

What’s the best product analytics tool for you?

The most important thing any product analytics tool can provide is a combination of quantitative and qualitative data.

Sometimes you might only need to watch some session replays to find the insights you’re looking for, and sometimes you just want to zoom in on conversion funnel rates—but, often, the best way to sift through data is to combine the two data types. That way, you understand the what and the why behind how users are engaging with your product.

Aside from data capture, the best product analytics tool is going to be the one that fits your business, your team, and your customers.

Effectively implementing a product analytics tool can give you a clear understanding of your customer’s intentions. You now know choosing the right tool to track users requires comprehensive research and clarity of the objectives your team wants to achieve. The requirements and benefits of each one of these tools will help you in your further research.

How Fullstory revolutionizes behavioral analytics

The Fullstory Team — Mon, 29 Jan 2024 06:00:00 GMT

Wading through the sea of available digital analytics solutions is essential for enterprise brands that want to build and maintain customer loyalty—but finding the one that best meets a team’s needs can create major slowdowns. To help cut through the noise, we've crafted a head-to-head comparison between Fullstory and other notable names in user behavior analytics tools. Fullstory, with its unique features such as tagless autocapture and low-code implementation, offers a level of insight and user understanding that stands out in the crowded market and helps brands grow conversions.

We aim to demonstrate how Fullstory's innovative approach to user experience analytics translates into substantial strategic business gains. And how we help brands like Gap avoid timely implementation and spread powerful digital analytics throughout their organization.

Fullstory vs. Amplitude: Complete autocapture over manual tagging

First up is Amplitude. While a well-known analytics platform, Amplitude grapples with limitations due to its heavy reliance on manual tagging. This not only demands substantial engineering resources during implementation and beyond, but also introduces delays in deriving value. Compounding these challenges is Amplitude's session replay tool, a recently tacked-on feature powered by limited data, which ultimately delivers incomplete insights.

This contrasts sharply with Fullstory’s well-established, reliable autocapture capabilities that eliminate the need for continuous manual tagging updates and ensure comprehensive data collection. Fullstory’s approach significantly reduces the time and resources required to start gaining insights, offering clean and dependable data from day one. Plus, Fullstory's ease of implementation and minimal technical burden make it a more efficient and effective solution for businesses seeking to understand and enhance their digital user experience (and improve customer loyalty) in a rapidly evolving landscape.

Fullstory vs. LogRocket: Enhanced usability and privacy

LogRocket faces limitations due to its engineering-centric design, which can restrict broader adoption across an organization. This focus results in a platform that is less accessible to non-technical users, potentially creating gaps in cross-functional collaboration. Additionally, LogRocket's approach to data collection is not entirely retroactive, and its privacy practices, which require code-level configurations, place a heavy burden on engineering resources that could be better used elsewhere.

Unlike LogRocket, Fullstory offers a more inclusive platform designed for cross-functional use, making it accessible to a broader range of team members. Fullstory's superior privacy practices provide an added layer of security and control, allowing businesses to manage privacy settings—and grow customer trust—without over-relying on technical teams. This combination of wider usability, scalability, and privacy control positions Fullstory as a more suitable analytics option for enterprises seeking an efficient, agile, and secure digital analytics tool that results in higher conversion rates.

Fullstory vs. Heap: Superior data capture and intuitive design

Heap's methodology, requiring manual event naming, poses significant limitations for enterprises by failing to structure and index undefined events. This rigid process contrasts sharply with Fullstory's dynamic autocapture feature, which effortlessly tracks many user interactions without prior configuration.

Additionally, Heap’s session replay capabilities, the outcome of an acquisition rather than organic development, lack the flexibility and comprehensive coverage offered by native solutions, particularly in mobile app analytics. Fullstory excels here with its intuitive, user-friendly interface and robust privacy protocols, offering a more effective and efficient approach. This makes Fullstory a comprehensive and adaptable solution for enterprises aiming to enhance their digital analytics capabilities and turn frustrated users into happy customers.

Fullstory vs. Pendo: Enhanced analytical efficiency for diverse teams

Pendo's platform, while offering certain benefits, encounters limitations with its protracted setup time and a focus that predominantly serves Product teams. This specialization reduces utility for broader team functions, particularly Data and Engineering teams. Additionally, Pendo's emphasis on basic analytics, such as in-app guidance and feedback collection, falls short of providing in-depth data analysis and a comprehensive understanding of user behavior.

Fullstory distinguishes itself with its wide-ranging applicability and efficient implementation. Its capabilities extend beyond Product Management, offering valuable insights across various departments, including Engineering and Data teams. Fullstory's streamlined setup and maintenance processes significantly reduce the workload on technical teams, allowing them to devote more resources to development and other critical tasks. By adeptly combining quantitative data with qualitative insights, Fullstory accelerates obtaining a holistic view of user interactions and improving retention.

Overall, Fullstory presents a more versatile and effective solution for organizations seeking to gain deeper, actionable insights from their digital analytics, encompassing the needs of the entire enterprise.

Learn more about how Fullstory and Pendo compare →

Conclusion: Fullstory is the optimal enterprise solution for behavior analytics excellence

Fullstory stands out as the top choice for enterprises. Its superior edge is evident compared to Amplitude, LogRocket, Heap, and Pendo. Fullstory's advanced autocapture technology, user-friendly interface, and stringent privacy measures set a new standard in the industry. It excels in its effortless setup, comprehensive data capture, and digestible analytics.

What’s more, Fullstory goes above and beyond routine insights with innovative features like Spaces, which allow users and teams to simplify their access to KPIs, dashboards, and data. Such capabilities are essential for businesses striving to refine their digital experiences, boost loyalty and retention, and increase conversions.

Additionally, with Fullstory, enterprises can avoid the complexity and inefficiency of juggling multiple platforms. It eradicates the need for, and additional costs associated with, multiple tools by offering a comprehensive suite of analytics features in one integrated platform. This consolidation streamlines workflows and saves valuable time that would otherwise be spent toggling between different systems.

In the end, Fullstory emerges not just as a tool but as a strategic partner for enterprises seeking to merge efficiency with in-depth digital insights, making it the unparalleled choice for forward-thinking businesses in the digital landscape.

Ready to enhance your digital analytics strategy? Request a demo to discover the capabilities of Fullstory for your entire digital presence.

What is product analytics? A guide for modern digital teams

Fullstory — Tue, 10 Sep 2024 07:00:00 GMT

Key takeaways in this article:

Product analytics captures real user behavior inside web and mobile products through event-based tracking, enabling data-driven decisions across acquisition, activation, retention, and revenue.
Modern product analytics platforms like Fullstory combine deep behavioral data, session replay, and AI to reveal not just what users do, but why they do it, without relying solely on guesswork or surveys.
Product analytics benefits teams across the entire organization: product managers prioritize roadmaps, UX designers reduce friction, engineers reproduce bugs, marketers optimize campaigns, and leadership proves ROI.
High-impact use cases include cohort analysis, churn and retention analysis, funnel analysis, milestone tracking, and personalization based on actual user interactions.

Products make the world go ’round. But the teams building them usually don’t struggle with ideas—they struggle with uncertainty.

Did users abandon onboarding because the value wasn’t clear, or because the page broke on one browser version? Is checkout conversion down because pricing changed, or because one shipping option is confusing? Product analytics exists to answer questions like these with behavioral data, not vibes.

This guide breaks down what product analytics is, why it matters, which teams use it, and how to build a workflow that actually gets used (not one that dies as a dashboard nobody opens).

What is product analytics?

Product analytics is the practice of collecting, analyzing, and interpreting behavioral data from digital products, websites, mobile apps, and other interfaces, to understand how people actually use what you’ve built. Unlike user surveys or customer interviews that capture opinions, product analytics captures facts: every click, tap, scroll, and error that occurs inside your product.

That sounds simple. The nuance is what makes it useful:

Surveys and interviews tell you what people say they experienced.
Product analytics shows what actually happened—step by step.

Most product analytics starts with events. A user clicks a button, submits a form, completes onboarding, hits an error—each action becomes an event with a timestamp and properties (device, plan, location, page/screen, and more).

Example: an event might be “Started checkout” on 2025-01-10 at 14:32, with properties like device type (iOS), plan (Premium), and region (North America). When you connect events into sequences, you can see the journeys that lead to success—and the ones that quietly fall apart.

Why is product analytics important?

Product analytics matters because teams ship changes all the time, but many teams can’t answer a basic question afterward:

Did that change help users—or just move numbers around?

Good product analytics reduces wasted work in three common ways:

It turns “we think” into “we know.” You can point to the exact step where users hesitate or abandon.
It makes bugs solvable. Technical issues stop being rumors (“it’s broken for me”) and become reproducible evidence with scope and impact.
It helps teams prioritize. You can focus on the friction that affects meaningful cohorts (high-value plans, key regions, specific devices, and important flows).

Fullstory’s Private by Default approach evaluates and redacts sensitive information directly on-device, enabling teams to gain deep insight without exposing user data.

How do modern product analytics platforms work?

Modern product analytics platforms are designed to get teams from setup to insight quickly, without heavy instrumentation or long ramp-up periods. While implementations vary, most follow the same core flow.

Deploy lightweight capture: Teams install a lightweight SDK or code snippet across web and mobile apps. Data collection begins almost immediately, often within hours or days, not weeks of planning and manual event setup.
Automatically capture behavioral context: User interactions are recorded along with the context that explains them: clicks, taps, navigation paths, errors, device details, and performance conditions. Strong platforms reduce privacy risk by evaluating and redacting sensitive data on-device before it ever leaves the user’s environment.
Index data for flexible, retroactive analysis: Behavioral data streams to secure infrastructure, where it’s indexed and made available across dashboards, funnels, cohorts, and session replay. Teams can ask new questions of historical data without waiting for fresh tracking to be added or new events to accumulate.
Use AI to reduce manual digging: AI layers help teams focus faster. Instead of scanning charts and filters, tools like Fullstory’s StoryAI can answer plain-language questions, summarize complex sessions, and highlight likely drivers behind conversion drops, error spikes, or friction trends.
Protect trust with “Private by Default” practices: Privacy and security are built into the workflow. Field-level masking, role-based access controls, governance tools, and on-device evaluation ensure teams get actionable behavioral insight without exposing sensitive user data or compromising compliance.

Together, these pieces create a system where teams can observe real user behavior, investigate issues in context, and move from insight to action—without slowing down development or putting user trust at risk.

What teams use product analytics?

Product analytics isn’t only for product managers anymore. The best teams treat it like shared infrastructure: one behavioral dataset, many ways to use it:

Product managers use product analytics data to evaluate feature launches, identify underused capabilities, and prioritize the roadmap by actual impact. For example, after rolling out a new subscription page, a PM can track how many users reach it, complete the flow, and convert, then compare that cohort’s retention against previous signup methods.
UX and design teams watch replays and review funnels to refine navigation, forms, and user flows based on real friction signals. Rather than guessing that a dropdown menu is confusing, designers can see users clicking the wrong options repeatedly, then redesign with confidence.
Engineering and QA teams reproduce bugs via session replay, correlate errors with impacted revenue, and validate that fixes actually reduce friction. When a customer reports “the button didn’t work,” engineers can pull up the exact session, see the error in context, and identify whether it affected hundreds of other users.
Marketing and growth teams build cohorts of high value customers, analyze how different acquisition channels translate into in-product behavior, and tune activation journeys. Seeing that users from a particular campaign churn faster, or slower, than average guides marketing spend decisions.
Customer support and CX teams investigate specific tickets by reviewing associated sessions, spot repeated frustration patterns, and proactively flag issues for product teams. Instead of asking customers to describe what happened, agents see exactly what the user experienced.
Data and analytics teams monitor high-level key metrics like conversion, user retention, and NPS drivers. They align teams around shared data, ensuring everyone works from the same understanding of what’s happening inside the product.

What data does product analytics track?

Most product analytics programs start with events—but strong programs don’t stop there.

An event is a recorded moment in time: a user clicks a button, submits a form, encounters an error, or abandons a flow. Each action is captured with a timestamp and contextual properties. On their own, events are just points on a timeline. When enriched with the right signals, they become a clear picture of how real people experience your product.

Modern product analytics platforms capture multiple layers of data around every interaction, so teams can move from “something happened” to “here’s what broke, for whom, and why.”

User interactions: Clicks, taps, swipes, scrolls, text inputs, page views, and screen transitions form the backbone of behavioral analysis. These interactions show how users progress through key flows, where they hesitate, and where they loop or drop entirely.
Navigation and journey paths: Beyond individual actions, platforms track how users move between screens, features, and sections over time. This reveals unexpected paths, backtracking behavior, and points where users get lost or take longer-than-expected routes.
Form and input behavior: Field focus, time-to-complete, validation errors, field abandonment, and re-typing patterns expose friction that conversion metrics alone can’t explain. A form may “convert,” but analytics can show whether users struggled the entire way through.
Technical and performance context: Device type, OS version, browser, app version, screen size, network conditions, load times, and JavaScript or native errors help teams pinpoint issues that only affect certain environments. This is often where “we can’t reproduce it” bugs finally become solvable.
Business and account context: Properties like plan tier, account size, region, role, subscription status, and lifecycle stage (trial, active, churned) connect behavior to outcomes. This layer makes segmentation meaningful and allows teams to prioritize fixes that impact high-value customers first.
Experience and frustration signals: Repeated clicks, dead clicks, rage clicks, long idle periods, failed searches, and error encounters act as early warning signs. These moments often never generate support tickets, but they’re where users quietly decide whether to continue or give up.
Timing and sequencing data: How long users pause between steps, how quickly they complete key actions, and the order in which behaviors occur all matter. Timing reveals hesitation, confusion, and cognitive load that raw conversion rates can’t capture.
Cross-device and cross-session behavior: Modern users don’t experience products in a single sitting or on a single device. Product analytics can connect behavior across sessions and platforms, showing how research on mobile leads to completion on desktop, or where friction on one device impacts outcomes later.
Custom, domain-specific events: Teams define events that reflect real business value, such as “Generated quarterly compliance report,” “Submitted insurance claim,” or “Published first listing.” These events align analytics with outcomes that actually matter, not just clicks.

Fullstory’s Fullcapture approach automatically logs rich interaction data and reconstructs sessions without storing actual video. This enhances both searchability (teams can query across all the events for user retention metrics) and privacy (no raw screenshots to expose sensitive information).

Core examples and use cases of product analytics

This section covers the analytical approaches that product teams rely on daily: cohort analysis, churn and retention analysis, funnel analysis, milestone tracking, customer journey analysis, and personalization. Each method answers different questions, but all draw from the same foundation of event-based behavioral data.

Cohort analysis

Cohorts are groups of users who share a characteristic or behavior. You might define a cohort like “Users who completed onboarding in October” or “Customers on Enterprise plan in North America” or “Users acquired through paid search in Q1.” Comparing cohorts over time reveals patterns that aggregate metrics hide.

Cohort analysis lets you track retention curves, feature adoption, expansion revenue, and support ticket volume for different groups. For example, comparing users who signed up before and after a product change shows whether the change actually improved outcomes. Comparing acquisition channels reveals which sources bring users who stick around versus those who churn quickly.

Example: A B2B SaaS team discovers that users who view at least three product tutorials within their first 7 days have 40% higher 90-day retention. Armed with this insight, they use product analytics to identify users who haven’t hit that milestone, then trigger in app guidance nudging them toward tutorials. The result is more new users reaching the high-retention cohort.

Churn and retention analysis

Churn refers to users or accounts that stop using or stop paying for your product. Retention is the inverse, those who keep coming back. Understanding both requires tracking typical windows: Day 1, Day 7, Day 30, and for subscription products, 6- or 12-month retention.

The power of product analytics for churn analysis comes from combining quantitative data (who left and when) with qualitative context from session replay and frustration signals. You can see that 200 accounts churned in March, then drill into sessions from those users to identify common friction points: slow-loading reports, confusing navigation, or repeated errors.

Example: A B2B SaaS app that finds customers churning within 60 days often encounter the same slow-loading analytics report. Using a product analytics tool, the team quantifies how many users hit this issue, correlates it with churn risk, reproduces the problem in session replay, and validates after fixes ship that the friction, and the churn, decreased.

Funnel and conversion analysis

Funnels represent ordered steps users take toward a goal. A typical funnel might be:

“Visited pricing → Started trial → Completed profile → Used core feature 3 times → Upgraded to paid.”

Conversion analysis examines how many users progress between each step and where drop-offs occur.

Product analytics platforms allow teams to build funnels retroactively over automatically captured events. You don’t have to define the funnel before gathering data, you can ask the question later and still get answers from historical data. When you spot a drop-off, you can jump directly into session replay to see what happened at that step.

Example: An ecommerce team preparing for the Q4 holiday season might analyze their checkout funnel and discover a 45% drop-off between “added to cart” and “completed payment.” Reviewing sessions reveals that users are confused by shipping options. Simplifying that screen, based on what analytics data showed, increases completion rates and recovers revenue.

For a mobile subscription app, funnel analysis might show that users stall at the payment screen. Session replay reveals that the keyboard covers the “Subscribe” button on certain Android devices. A small UI fix, informed by usage data, lifts trial-to-paid conversion.

Milestone and “aha moment” analysis

Milestone analysis focuses on key actions that correlate strongly with long-term value. These are the behaviors that separate users who become loyal customers from those who fade away. Common milestones include “Created first project,” “Invited 3 teammates,” “Completed first purchase in under 5 minutes,” or “Generated first report.”

The goal isn’t to guess which milestones matter, it’s to discover them through data. By analyzing users who were still active 180 days after signup and comparing their first-14-day behavior to users who churned, teams identify the actions that predict retention. This turns vague intuition into a specific, measurable activation target.

Example: A marketplace platform might discover that sellers who publish five listings in their first week have 2x higher annual revenue than those who publish fewer. With this insight, the team can refine onboarding: they add progress indicators, in app guidance, and email nudges that push new sellers toward that milestone. The result is more sellers reaching the high-success behavior pattern.

Customer journey and experience analysis

Customer journey analysis examines the entire path from first touch, whether that’s an ad, organic search, or referral, through onboarding, everyday product usage, support interactions, and renewal. It connects marketing and product data to show the complete picture of how customers engage over time.

Modern users hop between desktop, mobile web, and native apps. A customer might research on their phone during a commute, save items to a wishlist, then complete a purchase on desktop that evening. Product analytics should unify these digital interactions so teams see a single, coherent story per user or account rather than fragmented snapshots.

Example: A travel brand analyzing their customer lifecycle might discover that users search flights on mobile, save itineraries, but complete bookings on desktop. Using Fullstory’s Analytics and Mobile App Analytics together, the team sees the cross-device journey and identifies where mobile users drop off, perhaps because the mobile booking flow is too cumbersome. Improving that flow captures bookings that would otherwise be lost.

StoryAI can summarize long, complex sessions and journeys into short narratives that pinpoint friction and emotional moments. Instead of watching dozens of sessions manually, product managers and CX leaders get AI-generated summaries highlighting where things went wrong.

Personalization and activation based on behavior

Behavioral data feeds personalization that actually works. Instead of generic messages based on demographics or email lists, teams can deliver in-product guides, targeted campaigns, and tailored offers based on what users have actually done.

Use cases include nudging users who stalled at onboarding step 2, resurfacing features they tried once but never adopted, or offering discounts to users who repeatedly reach pricing pages but don’t convert. The key is acting on real user actions rather than assumptions about what segments might want.

Example: A B2C subscription service might detect that a user just encountered a payment error and immediately route them to a chat agent with context. A B2B product might see that a trial user just completed a key action and trigger a personalized email celebrating the milestone.

Fullstory Anywhere: Activation uses real-time behavioral signals, like cart abandonment, error encounters, or milestone completion, to trigger relevant experiences in marketing tools, support systems, or in-app messaging.

The goal is making every interaction feel relevant and timely, driven by what users actually do inside your product.

How product analytics impacts the AARRR metrics

The AARRR framework, Acquisition, Activation, Retention, Referral, Revenue, remains a widely used structure for product-led companies. Product analytics touches every stage, providing the data driven decisions that improve performance across the customer lifecycle.

Acquisition: Find the right users

Product analytics goes beyond click-through rates to reveal which acquisition channels bring users who actually activate, retain, and generate revenue. A campaign might drive thousands of signups, but if those users churn within a week, the channel isn’t valuable.

By comparing cohorts acquired via paid search versus organic search, a team might discover that organic users have 30% higher 90-day retention. Marketing then shifts spend toward channels that deliver users who stick around, not just users who sign up.

Connecting behavioral data into a data warehouse via Fullstory Anywhere: Warehouse lets growth and analytics teams join product data with ad and CRM data. This enables full CAC/LTV analysis by channel, showing the true return on marketing spend, as well as a deeper understanding of user retention rate.

Activation: Reach the first “aha” moment

Activation is the moment users clearly experience product value, sending their first invoice, publishing their first post, or booking a trip successfully. Measuring time-to-activation and activation rate by cohort reveals whether users are reaching value quickly enough.

Teams use funnels and replays to streamline onboarding flows that slow activation. A fintech app in might find that identity verification is taking users 3 days to complete. By analyzing where users struggle and simplifying the flow based on behavioral insights, they cut median “first successful transfer” time to 1 day.

Activation metrics directly predict retention. Users who never reach the “aha moment” rarely become long-term customers.

Retention: Keep users coming back

Retention curves, usage frequency, and engagement metrics show whether customers are forming habits around your product. The goal is identifying what separates users who stay from those who leave, then systematically moving more users into the high-retention pattern.

A B2B collaboration tool might discover that teams who set up templates within the first month are significantly more likely to stay for a year. Armed with this insight, they promote templates more heavily during onboarding, increasing the percentage of users who adopt the habit.

Retention work also includes tracking negative signals, errors, failed searches, rage clicks, and systematically removing them. Every point of friction is an opportunity for users to give up.

Referral: Turn customers into advocates

Product analytics can track referral flows, invite links, in-app referrals, sharing features, and correlate them with satisfaction and usage depth. This reveals who your true advocates are and what behaviors predict advocacy.

A subscription service might identify that highly engaged users who use the product daily active users metric consistently are 3x more likely to use referral codes. Rather than blasting referral requests to everyone, they design targeted campaigns around power users who actually convert referrals.

Syncing behavioral cohorts from Fullstory into marketing tools enables smarter referral and advocacy programs that target users most likely to participate and succeed.

Revenue: Optimize monetization and expansion

Product analytics ties directly to revenue by tracking paywalls, pricing pages, upgrade clicks, add-on feature usage, and renewal flows. This reveals what drives users toward paid plans, and what blocks them.

Example: A SaaS company in might discover that users who frequently hit usage limits convert better when shown in-app usage meters and contextual upgrade prompts rather than generic emails. The behavioral signal (hitting limits) triggers the right message at the right time.

Feeding behavioral data into finance and business intelligence tools helps teams forecast revenue more accurately. When you know which features drive upgrades and which friction points block renewals, you can prioritize monetization experiments with confidence.

Product analytics vs. other analytics disciplines

Product analytics, web analytics, marketing analytics, and business intelligence overlap but serve different primary audiences and questions. Understanding the distinctions helps teams choose the right tools and avoid gaps.

Product analytics provides deep behavioral insight inside the product. It’s used mainly by product, UX, engineering, and CX teams to understand user behavior, feature usage, and friction points. The focus is on what happens after users arrive, not how they got there.
Web analytics and marketing analytics focus on pre-signup activity: campaign performance, traffic sources, pageviews, and sessions. Google analytics is a common tool in this category, helping marketers understand which channels drive traffic and conversions. The primary users are growth and marketing teams optimizing acquisition.
Business intelligence covers cross-functional reporting and strategic KPI monitoring. BI tools aggregate data from multiple systems, sales, finance, product, support, to give leadership a holistic view. They’re powerful for executive dashboards but often lack the granular behavioral detail product teams need.
Data management platforms and warehouses form the underlying storage and processing layer. They’re infrastructure rather than end-user analytics tools, enabling teams to join data from multiple sources for advanced analysis.

When should my company invest in product analytics?

Product analytics can benefit your organization any time you wish to improve the user experience. Here are a few reasons you may need to implement product analytics.

When you’re curious about the experiences users have on your site, and concerned with making them better.
When you want to build a product, people like—or better yet, love.
When you want to scale properly, develop your data value chain, increase user retention and conversion rates, and lower customer churn.
When you want to perfect your data storytelling and ensure that you continue to evolve with the market.

How do I choose the right product analytics tool?

The ideal product analytics tool depends on company size, industry, regulatory environment, and internal data maturity. But consistent evaluation criteria apply across contexts:

Data coverage matters first. Can the platform capture web, native mobile apps, and other interfaces like kiosks or embedded experiences? A product analytics system that only covers web leaves blind spots in your digital experience.
Time-to-value separates tools that languish in implementation from those that deliver insights quickly. Look for minimal engineering effort, automatic data capture via a software development kit, and intuitive self-serve analysis for non-technical users.
Privacy and security can’t be afterthoughts. A product analytics platform should offer Private by Default architecture, fine-grained redaction, compliance certifications, and robust governance tools. This protects both users and your organization.
AI capabilities now differentiate platforms. The best product analytics software doesn’t just alert you to problems, it provides contextual summaries, prioritizes opportunities, and enables natural language querying so anyone can ask questions of the data.
Integrations connect product analytics to your broader stack. Warehouse connectors, CRM and CDP sync, support system integrations, and activation hooks into marketing tools ensure behavioral data flows where it’s needed.

What are the best product analytics tools?

Whether you're looking for a general analytics solution or specialized retail and ecommerce analytics, here are some of the top tools to consider:

Fullstory

Fullstory is a behavioral analytics platform that turns behavioral data into instant, actionable insights. The combination of deep product analytics, session replay, and AI, with privacy built in, helps teams prioritize the highest-impact improvements without drowning in data.

Built on a powerful analytics engine, the behavioral data platform delivers a retroactive view of how people interact with your site or app and connects customer interactions to the metrics that matter.

Google Analytics

Google Analytics is a major player in the analytics space. It offers a low-cost solution to monitor user traffic and general product usage. GA’s main strength is its integration with Google’s offerings and other tools that gather data from your tech stack.

Mixpanel

Mixpanel is a product analytics software, that enables teams to learn from their user data and innovate to create winning products.

Mixpanel offers a variety of solutions to help measure and predict customer usage across unique segmentation needs.

Amplitude

Amplitude is a product analytics tool that helps product teams understand customers while continuously providing insight to ensure proactive retention. Amplitude offers a variety of visuals and data-tracking options that can track high-level KPIs down to granular metrics.

Pendo

Pendo is a growing product analysis and adoption platform that captures quantitative insights, direct customer feedback, and general product metrics across web and mobile applications.

Heap

Heap is a product analytics tool that helps tell the story of user behavior. By tracking user events, product managers can segment and review customer experiences throughout the product with Heap.

Implement a practical product analytics workflow

Rolling out product analytics doesn’t require a massive transformation. Successful teams follow a repeatable workflow that connects data to business goals from day one.

1. Connect analytics to business goals

Analytics projects should start with an objective, not just a dashboard. Specific, measurable goals prevent the common trap of gathering data without a purpose.

Define the win: Instead of "looking at data," aim for "Increase trial-to-paid conversion by 20% in H2."
Map the journey: Connect goals to key user flows like signup, onboarding, and renewal.
Identify success metrics: An e-commerce team might focus on checkout completion, while B2B SaaS teams prioritize time-to-first-value.

2. Design a tracking and governance plan

Even with platforms that offer automatic capture, you need a blueprint. This ensures consistency and prevents "garbage-in, garbage-out" data.

Standardize naming: Decide if it’s Checkout Started or started_checkout and stick to it across the board.
Define identifiers: Ensure user and account IDs are consistent so you can connect sessions across different devices.
Prioritize privacy: Work with legal and security stakeholders to decide which fields to redact and how long to retain data.

3. Deploy, validate, and iterate

Don't launch everything at once. Roll out in stages to ensure your data is actually reliable before you make big decisions.

Test in staging: Perform known test actions in a staging environment to confirm they appear correctly in your platform.
Start with simple funnels: Build early-win dashboards that answer core questions, like "from homepage to completed signup."
Layer in sophistication: Once the basics are validated, add advanced features like AI summaries and anomaly detection.

4. Move from insight to action

Data without action is just noise. Every insight should lead to a hypothesis, a change, and a follow-up measurement.

Example: If you discover a 50% drop-off at a payment step, don't just note the problem. Watch the sessions to understand why, redesign the page based on those learnings, and then measure the new funnel to confirm the fix worked.

Fullstory helps here by allowing teams to share links to specific sessions or dashboards directly into tools like Jira or Slack. This keeps collaboration fast and keeps the "why" attached to the "how."

5. Establish review cadence and ownership

High-performing teams don't look at data randomly—they make it a habit.

Weekly (Tactical): Review bugs, friction spikes, and urgent UX issues.
Monthly (Strategic): Examine feature adoption, experiment results, and progress toward goals.
Quarterly (Planning): Use analytics to inform the product roadmap and investment decisions.

Assign ownership: Data thrives when it has a "parent." A product lead might own checkout conversion, while an onboarding PM owns the activation rate. When someone is accountable, the data actually gets used.

Mobile product analytics and omnichannel experiences

Mobile apps have become central for industries like retail, banking, travel, and gaming. Their interactions, taps, gestures, offline states, are more complex than web-only products, requiring specialized analytics.

Mobile-specific analytics needs include tracking gestures (swipes, long-press, pinch-zoom) and navigation patterns that don’t exist on web. Frequent app releases and OS changes on iOS and Android mean teams need to identify trends quickly when new versions cause problems. Intermittent connectivity and background states add complexity that web analytics doesn’t address.

Example: A ride-sharing or food delivery app might use mobile analytics to discover that users abandon the payment flow when their connection drops during address confirmation. By identifying this pattern across thousands of sessions, the team can implement better offline handling and recover lost bookings.

Fullstory for Mobile (Mobile App Analytics) captures high-fidelity data in native and cross-platform apps with a privacy-first SDK. Session replay and friction detection are tailored to mobile behaviors, showing exactly where users struggle on small screens.

Turn analytics into a competitive advantage

Organizations that consistently use product analytics develop faster feedback loops and outpace competitors still relying on intuition or sparse metrics. Every feature launch, every bug fix, every optimization is informed by real user behavior, not assumptions.

AI-driven behavioral analytics platforms like Fullstory reduce the burden of manual analysis by automatically highlighting the most impactful issues and opportunities. Instead of sifting through dashboards looking for problems, teams get proactive alerts and prioritized recommendations.

Product analytics isn’t a one-time project you complete and forget. It’s a continuous discipline that underpins product strategy, UX design, and customer experience. The teams that build this muscle, gathering data, identifying patterns, shipping improvements, measuring results, compound their advantage over time.

If your current analytics setup leaves you guessing about what users actually do, or if critical flows go unmonitored, it’s worth auditing your capabilities. The best product analytics platforms combine rich behavioral data, session replay, and AI, all with privacy built in, so teams can move from insight to action faster than ever.

If you've made it this far, check out FullstoryFree, our free plan packed with value.

30,000 sessions monthly
12 months session replay retention
12 months analytics retention

3 early MCP insights and learnings from our customers

Gardner Rordam — Mon, 31 Aug 2026 12:00:00 GMT

I spend my days partnering with companies that use Fullstory to its fullest extent, across teams and use cases., Over the last few months, I've seen the same mindset shift across my customers: MCP & AI first.

It starts when a team connects Fullstory to an LLM through MCP. Behavioral data stops being something you go and check, and becomes something you can ask questions about, and increasingly, something your systems act on without waiting on a human.

If you're looking to get set up with MCP, start with Fullstory's MCP docs.

How customers use Fullstory MCP

Here's what three of our early MCP customers are actually doing in their own words.

1. Talking to their analytics

At one of the nation’s largest automotive retailers, MCP changed how teams interact with behavioral data. A senior UX design manager put it plainly:

"[MCP] lets us not just be a recipient of data anymore, but interrogate it. … I can look at Fullstory, I can look at everything, and contextualize that into one answer."

Instead of opening a dashboard and hunting, he pulls behavioral data, survey results, and financial data into a single conversation and gets one answer back. He runs it a few times a week to monitor funnel performance and quickly identify any issues to his executive team.

The second-order effect surprised them more: who could suddenly do this. As a lead product designer on the team said:

"It's democratized data and access. I'm able to get a ton of information that is not normally at the designer's hands … and can get solutions out very easily."

Yet another Fullstory customer brings Fullstory data together with other analytics and back-end monitoring tools via MCP to set daily priorities. For this engineering team, their focus is on quantifying issues to help prioritize their focus. Through this process, the team gets a regular report on errors, outages, and negative behavioral swings, driven by the severity of impact. Thanks to this approach, they have a data-driven north star that guides their team’s work, far beyond what one human could find in a dashboard.

That's the quiet story of MCP. It doesn't just make analysis faster, it widens who gets to act on what the data says.

2. Catching problems before their customers do

Equals Group, a London-based financial technology payments company, is using MCP for something more autonomous. Their VP of technology, Richard Coombes, described spotting trouble without waiting for a complaint:

"Now if there's an issue, we can usually detect it without the customer contacting us. There are signals, a rage click on the front end, or a payment taking a long time in our data lake."

Once a signal fires, MCP pulls the rest of the picture together, logs and past tickets and recent releases, and turns it into a decision:

"We use the MCP to pull in all the logs, then decide: this looks like a customer having an issue. We can solve it straight away, or reach out and say we know you're having a challenge. For us, that's a huge differentiator."

This is the proactive loop in practice. Behavioral signals feed a judgment, and the judgment drives action while the customer is still in the moment, before they've quietly given up.

3. Closing the support-to-engineering loop automatically

The engineering team at The Last Unicorn wired MCP directly into how they build. Their founder and CEO, Zac Sheffer, walked me through it:

"We set up an automation so that every time a new ticket gets created, it will automatically review the ticket and try to find if there's a customer record associated with it."

He tried to break it and couldn't:

"I tried to trick it. This email actually isn't the right email, but it knew enough information that it was able to narrow down to the right email, find the Fullstory session, and then tag that onto the ticket automatically."

From there it gets genuinely surprising. Their agents pull the session through MCP, generate comparable synthetic data, and build a browser test that replays exactly what the user did. The result is a reproducible test attached to every customer ticket:

"Literally within a couple of minutes I can go from picking up a ticket to confirming that it's all fixed, before me as an engineer even has to look at it. It basically brings the amount of work that I have to do down so, so low."

That's the reactive loop, fully closed. A real user's struggle becomes a reproduced bug, a proposed fix, and a verified result, mostly before an engineer has to touch it.

But none of these can work without ground truth

Here's the thread running through all three use cases.

Fullstory MCP is the connection, but what travels through it must be complete and trustworthy, or the outputs fall apart. The same UX design manager, talking about the LLM his team works through, had the sharpest way of putting it:

"It's like your smartest intern. It's really, really good at its job, but it doesn't know anything about what you want."

The context you feed it is the whole game. That's why the teams getting the most out of MCP are the ones who were already capturing complete behavioral data, not a tagged fraction of it. When the record is complete, you can finally answer the question that actually matters. The same team's Fullstory administrator put it this way:

"Fullstory is going to help us tell the why behind any numbers we're seeing … and keep a pulse on whether the transformations we're doing are working."

Where this goes

Three companies, three very different problems, one direction.

Analytics is moving from something a few analysts check to something anyone can interrogate, and something our systems can act on, proactively when they can and reactively when they must. MCP is how the behavioral data gets there. The complete, real-time record of what your users actually do is what makes any of it trustworthy.

This is early, and the best use cases are still being invented by customers like these. If you're building on Fullstory MCP, I'd genuinely like to hear what you're doing with it.

We've created an MCP prompt library to help you envision what's possible with Fullstory MCP, and if you want to submit an idea, success story, or prompt you've found valuable, you can do so there.

The best Mixpanel alternatives for behavioral analytics

Fullstory — Fri, 28 Aug 2026 07:00:00 GMT

Mixpanel is one of the strongest and most well-built event analytics platforms, but rising costs and up-front tagging send teams looking at alternatives. The bill scales with tracked users and event volume, so growth turns into a bigger invoice. The platform only measures what you tag, and the questions you didn't plan for go unanswered. For example, conversion rate optimization usually means bringing in a second tool.

The Mixpanel competitors aren't all the same, and not every one of these is a true replacement. This guide helps product, engineering, and data teams sort through these Mixpanel alternatives and find the one that fits their goals, team, and stack.

Why teams look for Mixpanel alternatives

Even teams that like Mixpanel hit the same limits as they scale. Its pricing ties to monthly tracked users or event volume. That means your costs climb as growth takes off, and a launch or traffic spike can catch billing by surprise.

But that’s only the surface. The deeper issue is the event model itself. You have to define and instrument every event before it fires, so anything you didn't tag is gone for good, even with session replay added on. And when you finally do want to connect a trend to the behavior behind it, you end up pulling data across tools and reconciling separate datasets to see the full picture.

On top of all that, the upkeep compounds. As more teams instrument more events, your naming conventions and taxonomy become harder to manage, and event governance becomes a job in its own right.

Best Mixpanel alternatives overview

The best Mixpanel alternatives depend on what you need most, whether that's behavioral depth, mature product analytics, self-hosted control, or a free starting point.

Tool	G2 rating	Best for	Key features
Fullstory	4.5/5	Enterprise teams that need behavioral depth alongside product analytics	Fullcapture, StoryAI, friction detection, funnel analysis, session replay
Amplitude	4.5/5	Product teams prioritizing retention, funnel analytics, and experimentation at scale	Event tracking, funnel analysis, cohort analysis, experimentation
Contentsquare	4.6/5	Enterprise teams needing journey analytics at scale	Journey analysis, zone-based heatmaps, friction detection, AI insights
Heap	4.4/5	Teams that want auto-capture without manual instrumentation	Auto-capture, retroactive analysis, funnel, and path analysis
PostHog	4.5/5	Engineering teams that want open-source, self-hosted analytics	Event tracking, feature flags, A/B testing, session replay
Hotjar	4.3/5	Teams that want lightweight heatmaps and session replay alongside analytics	Heatmaps, session replay, surveys, feedback widgets
Pendo	4.4/5	Teams focused on product adoption and in-app guidance	In-app guides, NPS surveys, feature usage tracking, roadmapping
Matomo	4.2/5	Teams that want privacy-first, GDPR-compliant web and product analytics	Event tracking, heatmaps, session recordings, self-hosted option
LogRocket	4.6/5	Frontend teams debugging errors and performance issues	Session replay, error tracking, performance monitoring, product analytics
Google Analytics 4	4.5/5	Teams that need free, marketing-oriented event tracking	Event tracking, audience reporting, Google Ads integration

1. Fullstory

Best for: Enterprise teams that need behavioral depth alongside product analytics.

G2 rating: 4.5/5

Fullstory is a behavioral data platform that automatically captures every user interaction across web and mobile. Its main difference from Mixpanel is Fullcapture. Instead of instrumenting each event up front, Fullstory indexes everything as it happens, so teams can answer questions retroactively, even about behavior they never tagged.

StoryAI then turns that data into plain-language answers with no query to write, and Private by Default masks sensitive data on the user's device before it reaches Fullstory's servers, including on mobile. That makes Fullstory a strong Mixpanel alternative for teams that want product analytics and session-level context in one platform.

Key features:

Fullcapture: automatic behavioral data capture across web and mobile, no manual tagging required
StoryAI: AI-powered summaries and answers that surface friction points and opportunities in plain language
Friction detection: identifies rage clicks, dead clicks, error clicks, and thrashed cursors
Funnel analysis: connects quantitative drop-off data to individual behavioral sessions
Session replay: high-fidelity playback without screenshot-based rendering
Anomaly detection: flags unusual behavioral patterns before they escalate

Pros

Automatic behavioral data capture, no manual tagging, no gaps from uninstrumented events
StoryAI surfaces answers in plain language without SQL or engineering support
Session-level context connects funnel data to what actually happened on screen.
Private by Default masks sensitive data at the source, including on mobile
Unified platform, one data model, no reconciling a second tool's dataset

Cons

Mobile setup takes more configuration effort than web
Large data volumes can feel like a lot to navigate in early rollout
Pricing fits better once a team has scaled into it

Pricing

Free: 30K monthly sessions, 12 months of data retention, basic analytics, session replay, up to 10 users.
Business: Custom pricing. Deeper product analysis and quality insights.
Advanced: Custom pricing. Business + Journeys and Conversions.
Enterprise: Custom pricing. Advanced + SSO, role-based permissions, multi-org management.

Reviews

“FullStory is an invaluable tool for our eCommerce and Digital Experience team. We use it daily for analytics, CX analysis, evaluating customer perception and acceptance of features, and analyzing our ongoing experimentation.
This is an extremely rare SAS solution which allows you to both work simply with big picture ideas, and also subtle nuances only visible under microscope.
Our FullStory representatives are outstanding. Our success manager and technical resource could not be better — they are responsive, thoughtful and fully engaged with us.”
— Edward F., Digital Analytics Manager, Enterprise

2. Amplitude

Best for: Product teams prioritizing retention, funnel analytics, and experimentation at scale.

G2 rating: 4.5/5

Amplitude is Mixpanel's closest analytical peer and one of the most established alternatives to Mixpanel. It runs on the same event-based model, with strong funnels, retention curves, and cohort analysis, plus native experimentation and session replay of its own.

Its limitation is also the same as Mixpanel's. Analysis stays tied to the events you instrument up front, so there's no retroactive capture of behavior you didn't plan to track.

Key features:

Funnel and retention analysis
Cohort analysis and behavioral segmentation
Native experimentation and A/B testing
Data warehouse connectors

Pros

Powerful funnel/retention/cohort analysis
Native experimentation removes the need for a separate A/B tool
Strong ecosystem integrations
Good fit for cross-functional teams

Cons

Event-based, no retroactive capture
Data retention is capped at one year on the lower tiers

Pricing

Free: 2 million events per month, 10K monthly session replays, basic product analytics, cohort syncs.
Plus: Starts at $0. Scales to 70 million events, with heatmaps, 20 behavioral cohorts, and 2 years of data retention.
Growth: Custom pricing. Unlimited MTUs or events, advanced behavioral exploration, 20K monthly session replays, SSO.
Enterprise: Custom pricing. Growth + unlimited projects, 50K monthly session replays, advanced user management with RBAC.

Reviews

“I'm a software engineer, not a PM - but Amplitude helped me think like one on a recent project. Structured events turned “is anyone using this?” into something I could measure. I stopped guessing what to fix or improve next and started deciding based on usage and failure patterns. That's what I like best: it bridges engineering and product without a separate analytics stack.”
- Stratulat R., Software Engineer

3. Contentsquare

Best for: Enterprise teams needing journey analytics at scale.

G2 rating: 4.6/5

Contentsquare is a large enterprise Mixpanel alternative in a different category. It's a digital experience platform that focuses on journey analytics at scale rather than event-based product analytics.

The platform grew largely through acquisition, folding in Heap, Hotjar, and Clicktale. It brings heatmaps, session replay, and journey analytics into one suite, but that same structure is the main trade-off, since it can mean reconciling data across tools that started as separate products.

Key features:

Zone-based heatmaps and journey analysis
Session replay and friction detection
AI-powered insights
Ecommerce optimization tools

Pros

Broad enterprise feature set
Strong in ecommerce and retail
Large ecosystem with dedicated support

Cons

Acquisition-built fragmentation across data models
Setup and maintenance overhead
Often needs paid consultant support

Pricing

Free: 200K monthly sessions, 1 project, replays, heatmaps, funnels, error and performance monitoring.
Growth: Starts at $39 per month. From 7K monthly sessions, with 13 months of data access, journey analysis, and zone-based heatmaps.
Pro: Custom pricing. From 1M monthly sessions, with retroactive precision filtering and revenue goal tracking.
Enterprise: Custom pricing. Pro + custom session volumes, unlimited projects, dedicated support and SLAs.

Reviews

“I like how Contentsquare provides detailed behavior analysis of our targeted users on both the website and the app. The visual tools - such as heatmaps, session replays, and journey analysis - give us clear, in-depth insights into what users are doing. I also enjoy creating dashboards, and I appreciate how easy it is to build them while still keeping the level of detail we need.”
- Manoj B., Assistant Manager

4. Heap

Best for: Teams that want auto-capture without manual instrumentation.

G2 rating: 4.4/5

Heap is the most direct Mixpanel alternative for teams stuck on upfront instrumentation. You install it once, and it captures interactions automatically and retroactively, so you can analyze behavior you never defined as an event.

Contentsquare acquired Heap in 2023, which adds some roadmap uncertainty to factor in the long term. It closes the retroactive data gap, but doesn't go as deep on session-level context or AI analysis as a platform built around behavioral data from the start.

Key features:

Automatic event capture and retroactive analysis
Funnel and path analysis
Cohort analysis
Basic session replay

Pros

Retroactive analysis without manual tagging
Accessible onboarding for non-technical teams

Cons

Roadmap independence is uncertain post-acquisition
AI capabilities are less developed than StoryAI
Session-level context is limited

Pricing

Free: Up to 10K monthly sessions, 6 months of data history, core analytics charts, SSO.
Growth: Usage-based pricing, estimated after you install the tracking snippet. Adds unlimited users and reports, CSV exports, 12 months of data history.
Pro: Custom session pricing. Account analytics, engagement matrix, report alerts, session replay as an add-on.
Premier: Custom session pricing. Pro + data warehouse integration, behavioral targeting, unlimited projects, region-specific storage.

Reviews

“Heap's automatic event tracking is its strongest feature. It significantly reduces the need for manual instrumentation, which saves time for product and design teams. The interface is intuitive, and building funnels, retention reports, and user journey analyses is straightforward even without heavy engineering support.
Heap is particularly useful for understanding real user behavior across web applications.”
- Deep G., Web Designer

5. PostHog

Best for: Engineering teams that want open-source, self-hosted analytics.

G2 rating: 4.5/5

PostHog is an open-source Mixpanel alternative for engineering teams. You can run it on your own infrastructure and keep your data in-house. That makes it a fit for teams with strict data residency needs.

It also packs a broader toolset than Mixpanel, adding error tracking, surveys, a data warehouse, and a CDP to the core analytics stack. The breadth pays off, but self-hosting takes real engineering investment.

Key features:

Open-source, self-hosted deployment option
Event tracking, feature flags, A/B testing
Session replay
Heatmaps

Pros

Full data ownership with an open-source, self-hosted option
All-in-one platform combining analytics, session replay, flags, and experiments
Generous free tier with transparent usage-based pricing
Developer-friendly with a clean SDK

Cons

Built primarily for developers, less accessible to non-technical teams
Self-hosting requires ongoing engineering investment to maintain and scale
Some features feel incomplete compared to mature enterprise platforms
Not a fit for teams that need in-app guides or behavioral depth without heavy setup

Pricing

Free: No credit card required, 1 project, 1 year of data retention, unlimited team members, community support.
Pay-as-you-go: Usage-based pricing above the free tier, with 6 projects, 7 years of data retention, and email support.

Reviews

“What I like most about PostHog is the level of visibility it gives us into real user behavior, while still staying very developer-friendly.
Session recordings, funnels, feature flags, and the event exploration tools are especially valuable.
They make it straightforward to debug issues, validate experiments, and see how users interact with new features, without needing a massive analytics setup.” -Natanael G., Sr Software Engineer

6. Hotjar

Best for: Teams that want lightweight heatmaps and session replay alongside analytics.

G2 rating: 4.3/5

Hotjar, now part of Contentsquare, is a lightweight Mixpanel alternative for teams that want quick visual feedback without a heavy setup. It installs fast and stays accessible to non-technical teams, with heatmaps, session replay, and on-page surveys. It's thin on funnels, cohorts, and retention, so teams usually pair it with a product analytics tool rather than replace one outright.

Key features:

Heatmaps
Session replay
On-page surveys and feedback widgets
Basic funnel tracking

Pros

Fast setup
Affordable entry point
Useful qualitative layer for UX and marketing teams

Cons

Limited product analytics depth
Not built for enterprise scale or complex funnels
Often used alongside another analytics tool rather than instead of one

Pricing

Free: 35 sessions per day, heatmaps, session replay, 20 survey responses per month, 2 integrations.
Growth: Starts at $39 per month. From 7K monthly sessions, with 13 months of data access, journey analysis, and zone-based heatmaps.
Pro: Custom pricing. From 1M monthly sessions, with retroactive precision filtering and revenue goal tracking.
Enterprise: Custom pricing. Pro + custom session volumes, unlimited projects, dedicated support and SLAs.

Reviews

“Hotjar is an excellent tool for startups and is very user-friendly. It offers comprehensive user experience management, making it an ideal solution for companies looking to explore advanced UX measurement and testing.
In the end, the ease of installation and use, along with a fully customizable and advanced interface, make it a straightforward add for most organizations. Implementation is straightforward, and once in place, it can be a powerful tool in your arsenal.”
- Eric M., Senior Solutions Consultant

7. Pendo

Best for: Teams focused on product adoption and in-app guidance.

G2 rating: 4.4/5

Most teams weigh Pendo alongside Mixpanel rather than instead of it. Pendo is a product adoption platform for guiding users through an interface, with onboarding guides, tooltips, and NPS surveys.

On the analytics side, it's lighter, better suited to teams that care more about adoption than deep funnel and cohort analysis.

Key features:

In-app guides and walkthroughs
NPS and in-app surveys; feature usage tracking
Roadmapping tools

Pros

Strong in-app guide/survey tooling
Feature usage ties directly to adoption workflows
Good for product-led growth teams

Cons

Manual event tagging, similar gap as Mixpanel
Less analytical depth on funnels/cohorts
Enterprise pricing scales quickly with MAU

Pricing

Base: Custom pricing based on MAU volume. Product analytics and in-app guides.
Core: Custom pricing. Base + session replay.
Ultimate: Custom pricing. Core + NPS, PMF and CSAT surveys, Orchestrate, Listen, and Data Sync.

Reviews

“I use Pendo to check customer satisfaction scores and draw insights from them, which I find quite useful. It's great for seeing the way users engage with our platform easily, and helps me analyze how users interact with the product, including the percentage of users accessing certain parts.
I also use Pendo as an intercept tool for surveys, which is handy. Additionally, Pendo has really helped with onboarding by directing us to specific bottlenecks and reducing friction.”
- Gonzalo M. G., UX Researcher

8. Matomo

Best for: Teams that want privacy-first, GDPR-compliant web and product analytics.

G2 rating: 4.2/5

Matomo is an open-source, privacy-first analytics platform, and its self-hosted option gives teams full control over where their data lives. That makes it a natural pick when compliance or data residency is the deciding factor rather than analytical firepower.

It covers the core analytics well but goes less deeply than Mixpanel on cohorts and advanced funnels, so it suits teams that prioritize ownership over depth.

Key features:

Event tracking
Heatmaps
Session recordings
Self-hosted or cloud deployment
No data sampling

Pros

Full data ownership with self-hosted option
GDPR-friendly by design
No data sampling, even at high volume

Cons

Less advanced cohort/funnel analysis than Mixpanel
Self-hosted setup takes engineering effort
UI less polished than newer competitors

Pricing

Free: Matomo Community is free forever and self-hosted, with unlimited users and hits.
Cloud Business: From ~$29 per month for 50K monthly hits, 30 sites, 30 team members, 24 months of raw data retention.
Cloud Enterprise: Custom pricing.

Reviews

“Being open source is surely a good advantage over picking another proprietary on-premises solution. No worries on being GDPR compliant since you will manage every aspect of analytics data gathering.”
- Giovanni S., Project Manager

9. LogRocket

Best for: Frontend teams debugging errors and performance issues.

G2 rating: 4.6/5

LogRocket is a developer-focused tool that pairs session replay with error tracking and performance monitoring. That mix pulls in a different buyer than Mixpanel's usual PM and analyst crowd, since it's aimed at engineers tracing a bug back to what the user actually did.

The trade-off is product analytics depth, so teams lean on it for technical debugging rather than funnel and cohort analysis.

Key features:

Session replay synced with console logs/network requests
JS error tracking and performance monitoring
Galileo AI issue detection; product analytics

Pros

Session replay synced with logs
Galileo AI surfaces issues proactively
Useful shared context for engineering and support

Cons

Its developer focus makes it less approachable for PMs and non-technical teams
It runs shallower on funnels and cohorts than Mixpanel
Pricing scales quickly with session volume

Pricing

Free: 1K sessions per month, with unlimited analytics, error, and log events.
Core: Pricing scales with sessions captured, starting at $176 per month for 25K monthly sessions. Adds heatmaps, path analysis, conversion funnels, and SSO.
Pro: Core + Galileo AI, with session summaries, issue detection, funnel and heatmap insights, and RBAC. Included at no additional cost above 100K monthly sessions.
Enterprise: Custom pricing. Pro + unlimited seats, streaming data export, self-hosted deployment.

Reviews

“I use the session replay and the network usage constantly. I'm a software developer, and so this really helps me debug my software. It's really useful to see the user details and scrub through the times and see how they interacted with the app.
Usually a customer reports an issue, and then I have their email. I immediately go to [LogRocket] and type in their email and then pull up their relevant session.”
- Alexander F., Software Engineer

10. Google Analytics 4

Best for: Teams that need free, marketing-oriented event tracking.

G2 rating: 4.5/5

Google Analytics 4 is the free option most teams already run. It's tightly integrated with Google Ads and strong on traffic metrics like bounce rate, which makes it a reasonable place to start.

But it wasn't made for product-specific analysis, so it falls short on cohort and behavioral segmentation and offers no native session replay or heatmaps. For Mixpanel power users, GA4 works better as a complement than a true replacement.

Key features:

Event-based tracking
Audience and funnel reporting
Native Google Ads integration
Free, generous usage limits

Pros

Free for most businesses
Native Google Ads integration
Widely adopted, easy to hire for

Cons

Built for marketing analytics, not deep product analytics
It offers no session replay, heatmaps, or friction detection
Weaker group/account-level (B2B) analytics than Mixpanel

Pricing

Free: Standard properties cost nothing to run, with data retention capped at 14 months, explorations sampled above 10 million events per query, and BigQuery export limited to 1 million events per day.
Analytics 360: Custom pricing. Higher collection and reporting limits, up to 50 months of retention, unsampled explorations, and billions of daily BigQuery events.

Reviews

“Google Analytics has been the standard for free website analytics for the 2+ decades I've been in the industry. It has changed quite a bit over that time, but it has always provided powerful analytical tools at no cost to the user.”
- Lindsey M., Marketing Director

How to evaluate a Mixpanel alternative

Now that you know what some good alternatives to Mixpanel are, the choice comes down to a few questions:

Does the platform capture data automatically, or does it need manual tagging? If an event was never tagged, automatic capture still lets you analyze that behavior retroactively. Event-based tools only show what you set up in advance.
How does pricing scale with volume? Cost usually ties to events, sessions, or tracked users, so model it at twice your current volume. That's where event- and session-based pricing tends to surprise teams.
Does it connect quantitative data to real sessions? A funnel shows you where users drop off, but not why. Look for a live demonstration that ties a specific drop-off to a real session.
Can non-technical teams get answers on their own? PMs, analysts, and CX leads should reach insights without routing every request through engineering. If answers depend on someone writing a query, that becomes a bottleneck.
What can the AI do? Some AI only reports on predefined metrics. What matters is whether it can surface answers about behavior it wasn't pre-configured to track.
What's the full cost of ownership? Look past the subscription to implementation time, ongoing maintenance, and whether reaching full value requires paid professional services.

How to put behavioral data to work

Start by mapping the flows where behavioral data has the clearest revenue impact, like onboarding, activation, key feature adoption, and conversion. Get PMs and analysts direct access while the rollout is still underway. The teams closest to the roadmap are the ones who turn insights into decisions.

Once those flows are mapped, anomaly alerts can surface friction like form abandonment or rage clicks before it affects retention or revenue. Connect behavioral data to your warehouse and BI tools so those signals show up in the reporting. Then review the findings on a schedule. That way you can turn them into input for sprint planning instead of one-time audits.

See the why behind every metric with Fullstory

An events-only stack tells you what happened in aggregate, but not which step to fix first or why users hesitated.

Fullstory works differently; it captures behavioral data automatically and retroactively, then connects it to the session-level context behind each metric. That's the foundation of intelligent digital experiences, where the product adapts to real behavior instead of guesswork.

StoryAI answers questions in plain language, so teams trace a drop-off to the behavior that caused it without having to plan every event. And because Fullstory is Private by Default, sensitive data stays masked from the start.

If your team wants that depth without the manual setup, Fullstory is where to look next.

The context chasm: Is your AI giving you answers or best guesses?

The Fullstory Team — Mon, 01 Jun 2026 05:00:00 GMT

On June 16, we hosted our summer release event, where we talked through what informed AI can do—learn what we released here.

Every time a user clicks, hesitates, rage-clicks their way through a broken flow, or quietly closes the tab and moves on, they leave behind a record. Enterprise digital teams have been collecting those records for years. In most organizations, the data isn’t the problem. What to do with it is.

Your team probably knows where users are dropping off. They can tell you which funnel step is losing people, which page has the highest exit rate, which campaign drove traffic that didn’t convert. What they usually can’t tell you, at least not quickly and not without a painful amount of manual correlation work, is why. The specific interaction sequence that preceded the drop. The error state the user hit twice before giving up. The exact moment friction appeared, and what it looked like from where they were sitting.

That gap has a name. We’ve been calling it the context chasm. And when your AI systems are operating without that context, they’re not giving you answers. They’re giving you best guesses dressed up in confident language.

The data exists. The problem is that it’s scattered.

Most digital teams aren’t working from one coherent data source. They’re working from several that were never designed to talk to each other. Product analytics in one system. Session data in another. Customer support history somewhere else. Every one of those systems captures a slice of what happened. None of them, on their own, can tell you the full story.

The result is that answering a straightforward question, why did checkout conversion fall Tuesday, requires someone to go pull data from multiple places, manually assemble it, build a theory, and hope the theory is right. Meanwhile the friction sits in the product, costing conversions and eroding trust, for days or weeks before anything changes.

The same problem shows up in a different form for operations and workforce teams. AI-assisted workflows are getting deployed at speed across support, operations, and back-office functions. Leadership wants to know: is it working? Handle times changed. Ticket volume moved. But connecting those outcomes to specific workflow changes, proving that the AI did it and understanding where it helped versus where it introduced new friction, requires exactly the kind of behavioral visibility that most teams don't have.

The investment is real. The evidence isn't. Teams end up making expensive decisions in a fog, and according to our 2026 Intelligent Digital Experiences Survey, 80% of digital experience professionals say AI has increased their review workload as a result.

When context is fragmented, AI makes confident mistakes

What makes this particularly acute right now is that AI raises the stakes. Agentic systems don’t just surface insights, they take action. An AI that has incomplete context doesn’t produce an incomplete answer. It produces a confident, fluent, completely wrong one. And in a world where those systems are operating workflows, personalizing experiences, and making decisions at the speed of software, wrong is expensive.

The promise of AI in digital experience is real: close the gap between something going wrong and something being fixed, from weeks to minutes. Detect friction before users complain about it. Resolve issues in the session, not in the postmortem. But none of that works if the agentic system is working from fragmented data. AI is fast. Without behavioral context, without the full record of what users actually did, in sequence, including every hesitation and error, it’s fast at the wrong thing.

This is the problem Fullstory MCP was built to address. External AI systems, whether that’s your customer support AI, your coding agents, or any model you’ve wired into your stack, can’t access the behavioral record on their own. MCP gives them a way in. Instead of working from vague inputs and producing plausible-sounding responses, your AI gets the session-level context it needs to trace what actually happened and act on it.

Digital sight is what changes the equation. When an AI system has access to the actual behavioral record, not just aggregated metrics but the interaction-level sequence across a session, it can do something qualitatively different. It can trace a conversion problem back to a specific friction pattern. It can surface an issue for a specific combination of device, flow, and user state, not because someone instrumented for that scenario, but because the data was captured. It can take action on what it sees, rather than inferring from what it was told.

The same gap exists inside your workforce, and it’s just as costly

The behavioral data that tells you what’s happening to your users is also, if it’s structured the right way, the data that tells you what’s happening to your workforce.

Operations teams face a version of the context chasm that’s less talked about but just as costly. They know what comes out of a workflow: resolution rates, handle times, CSAT scores. What they don’t have is clear visibility into how work actually moves through the system, where time is being spent, where the AI tools they’ve deployed are helping versus creating new friction, where the highest-value improvement opportunities are. You can’t optimize what you can’t see. And in most organizations today, the work itself is invisible.

The investment in AI assistance, the copilots, the routing tools, the summarization features, is significant enough that teams need to be able to account for it. Not just “we think it’s helping” but “here’s where it moved the metric and here’s what it changed.” Without process-level visibility, that kind of accountability isn’t possible.

What closing the gap looks like

What closing the context chasm looks like is behavioral data that functions as shared infrastructure, not a separate analytics silo, but a layer that flows into the systems that need it. Session-level context streaming into your customer support tools so your agents and your AI are working from the same picture. Behavioral data moving into your warehouse so it can be combined with transactional records and LTV models without someone spending two days on the export.

Fullstory MCP is already making this possible for teams in beta. Builders are connecting their external AI systems directly to Fullstory’s behavioral data, so the models they’ve deployed can pull the context they need to produce answers that are traceable to what actually happened, not just statistically likely. The difference in output quality is significant, and the difference in trust is more so.

When that’s in place, the question shifts. Teams stop spending time building context and start spending it acting on what the context tells them. Problems get surfaced in the session, not in the review meeting three weeks later. Agentic workflows earn the trust to operate with more autonomy because the work is verifiable. You can see what the AI saw, trace what it did, and confirm the outcome.

On June 16, we hosted our summer release event, where we talked through what informed AI can do—learn what we released here.

5 questions to ask your product analytics vendor

Addison Price — Tue, 25 Aug 2026 13:00:00 GMT

Run a bake-off for product analytics, and something uncomfortable happens around the third demo. Everyone says yes to everything.

Yes, we capture everything. Yes, we have AI. Yes, mobile is covered. Yes, implementation is straightforward. The comparison grid fills in solid green across every column, and you are no closer to a decision than when you started.

The products do differ. Feature questions have just stopped exposing how, because almost any vendor can answer one honestly. The differences live one layer down, in what the architecture requires of you after the yes. A tracking spec somebody has to write. A release somebody has to wait for. A sample rate nobody volunteered.

So the questions below are architecture questions, phrased the way a buyer would actually ask them in a room.

We put them with two of the people who sit on the vendor side of those rooms. Nestor and Jennifer are solutions engineers here. They spend their weeks in evaluations, migrations, and the specific silence that follows a prospect discovering what their current tool was never capturing. They work for Fullstory, so read their answers with that in mind. The questions hold up no matter who you point them at, and you should point them at us.

1. If I need data to answer a question I didn't plan for, what do I have to do first?

Start here, because this question quietly decides how the next year goes. Nearly every meaningful decision you'll make starts with a question nobody thought to ask in advance. Conversion drops on a Tuesday. A competitor ships something. A board member wants to know why a cohort behaves differently. What happens next is the whole evaluation in miniature.

"When a buyer asks this, they're really testing whether the tool is proactive or reactive. In legacy platforms, answering an unplanned question almost always triggers a multi-week engineering ticket. You draft a tracking spec, convince developers to instrument custom event tags in the codebase, wait for the next app release, and then wait weeks for data to trickle in. You end up making critical product decisions on old, incomplete, or delayed information."
- Nestor, Solutions Engineer

Jennifer calls the pattern tag and wait, and puts a finer point on why it never fully resolves: "You can only analyze questions you've had the foresight to tag in advance. If I don't know today exactly what I'll want to look at next month, I'm not going to be able to look at it next month."

What a good answer sounds like

The vendor should be able to say you define the interaction now and look backward.

Checkout conversion slips this morning, you describe that step in the interface this afternoon, and you're reading the last six months of behavior against it. No code, no release, no waiting for new data to accumulate. That's the difference between capturing everything and then organizing it, versus deciding in advance what's worth capturing.

What to listen for

Nestor's tell, verbatim: "If the answer is 'it's just another quick track call,' that's code for: you won't have this answer for at least a month." Quick describes the developer's effort. It does not describe your wait, which runs from ticket to backlog to release to enough data to trust.

2. Is the behavioral data complete everywhere I operate, including mobile, or am I getting a sampled view?

Every vendor claims complete capture, which makes the claim worth almost nothing on its own. The compromises surface later, in the places that are hardest to instrument, which are also, inconveniently, the places your product increasingly lives.

"Modern apps use dynamic web frameworks and native mobile components, and partial data creates false conclusions about where users drop off. A lot of teams on legacy tools have no idea it's happening. They believe they're seeing every session, when they're seeing a sample."

"At Fullstory we capture when a mobile device goes offline. Shadow DOM. Single-page applications. Canvas and web components. The mobile frameworks that have historically been very hard for tools in this category, like Flutter and Jetpack Compose."
- Jennifer Hold, Lead Customer Solutions Engineer

Nestor frames what's actually at stake when the answer is a shrug: "If you're making million-dollar roadmap decisions on a sampled view or an incomplete mobile picture, you're making educated guesses and calling them evidence."

What a good answer sounds like

Specific and unprompted.

A vendor that has genuinely solved this will name the hard cases before you finish asking, because those are the ones their engineers are proud of. You want the capture to be automatic and deterministic across every surface, without draining battery, degrading app performance, or asking your team to hand-instrument anything. Privacy belongs in this answer too, not as a separate topic: capturing everything and controlling what's stored, masked, and exposed are the same conversation, and a vendor who can only do one of them has told you something.

What to listen for

"We support mobile," with no framework named. Ask which ones, and ask what happens to a session on a flaky connection. Then ask for the sample rate in writing. If it's a setting rather than a number, ask what it defaults to and who last changed it.

3. Is your AI reasoning over all of it, or only the events we remembered to tag?

This is the fastest-moving claim in the category and the easiest one to stage. Every vendor has shipped an assistant. Far fewer have changed what sits underneath it, and the assistant is only ever as good as what it can see.

"AI is only as smart as the data set you feed it. If your platform tracks the 20 or 30 events your team instrumented last quarter, your assistant is blind to most of what people actually do in your product. The real test is whether it can surface unknown unknowns: an untagged layout shift, a broken CSS selector, a dead click nobody ever mapped, without a human telling it to go look. Putting a language model on top of a sparse, manually tagged event table just gives you a nicer way to query the same gaps."
- Nestor, Solutions Engineer

Jennifer states the constraint plainly: "Model output is bounded by model input. Partial data in, partial answers out." She points to agentic session review as the version of this that goes past querying: an agent watching sessions and reporting what it found, rather than waiting to be asked about an event somebody defined.

What a good answer sounds like

The AI reasons over complete behavioral data, including everything nobody thought to tag, and it volunteers things nobody thought to ask about. That's the only version that helps, because the failures that cost you money are by definition the ones you weren't watching for.

What to listen for

Run this test live in the demo: ask their AI to tell you something about a behavior nobody on their team tagged in advance. If every answer traces back to a defined event, you haven't bought intelligence. You've bought a chat interface on a spreadsheet.

4. Who else in my business can use this, and can the data leave your platform?

Analytics tends to get evaluated by the team that will own the license and used by roughly that same team. It's worth deciding early whether that's the outcome you want, because the behavioral data that answers a product question is the same data that would have closed a support ticket faster or told a data scientist which accounts are drifting.

"Analytics shouldn't be a spectator sport locked inside a product manager's dashboard. Ask how the behavioral data serves a support agent trying to reproduce a rare bug, an engineer chasing network errors, or a data science team building churn models in Snowflake. Traditional tools force a compromise: aggregate charts with no individual context, or individual sessions that don't scale across millions of events. You want to move from a funnel drop-off to the specific session behind it in one click, and you want structured behavioral data streaming out of the platform into the rest of your stack rather than sitting in a walled garden."
- Nestor, Solutions Engineer

Jennifer has watched what happens when that constraint lifts: behavioral data into Snowflake and BigQuery, into the support tool, into Salesforce as customer context. "The tech stack cracks right open once you can send the data somewhere else."

What a good answer sounds like

One platform that connects the aggregate number to the individual experience behind it, plus a real pipeline out: a stream your warehouse, your CRM, and your support desk can consume on their own schedule.

What to listen for

Two things. First, whether the pricing model makes it economically irrational for anyone outside the core team to ever log in, which is a distribution decision disguised as a line item. Second, what "export" means concretely. Ask for the destination list and the format, and ask whether it's the raw feed or a summary of it.

5. How much engineering time stands between a new question and its answer?

The total cost of ownership question, and the one most likely to be answered with a number that has nothing to do with the invoice.

"Engineers should be building and fixing. They shouldn't be spending their time tagging data. And it isn't just the cost of that work. Every single time somebody has a question, a ticket gets filed, it has to climb out of the backlog, it has to be prioritized against everything product and engineering already committed to, and then it has to be built and shipped. Your team has to find engineering bandwidth before a question can be answered at all."
- Jennifer Hold, Lead Customer Solutions Engineer

Nestor puts it in procurement terms: "If every feature launch, redesign, or business question needs a developer to write a tracking spec and change application code, you're paying twice. Once for the license, and then continuously in engineering tax." The architectural fix, he says, is decoupling collection from organization. "Engineering implements the SDK once. Product, design, and growth self-serve from there, without pulling developers off the work that actually ships revenue."

What a good answer sounds like

Instrument once at install.

After that, defining what you want to measure is configuration rather than code, and the people asking the questions are the people who can answer them.

What to listen for

Ask a question about the past instead of the future: what did taxonomy maintenance look like for their last three customers a year after go-live? Implementation effort is always quoted at week one. The engineering tax is an ongoing subscription, and it isn't on the order form.

Five questions, one underlying test

Read them together, and they're the same question asked five ways. Does the platform capture what actually happened, or only what somebody thought to ask for in advance?

That distinction used to be about convenience. It decided whether you waited two weeks for an answer or got it that afternoon, which mattered, but you could live with it. It's now the thing your AI runs on. A model doesn't get smarter than the data underneath it, and a system that only ever looks where it was pointed will keep missing whatever nobody pointed at. That gap stops being a data problem and becomes every decision made on top of it.

So ask the five questions. Make the vendor answer the second half of each one, the part after the yes.

Ask them about us, too. We would rather answer these than be graded on a feature grid.

What product analytics can't tell you about work

Brent Kinman — Tue, 18 Aug 2026 12:30:00 GMT

The product analytics market has spent the better part of a decade building tools that answer narrow questions well. Whether the software is creating friction, whether employees are adopting new tools, and where bottlenecks show up in a documented process. These are real questions, but there’s a more important one sitting behind all of them that the market still hasn’t built a serious answer for: Why does work take as long as it takes, and what would it look like if it didn’t?

The category gets you partway there

Product analytics tools, digital adoption platforms, task miners, process miners—each of these was built with consumer-facing logic at its core. Funnels, journeys, conversions, rage clicks. These metaphors were developed to understand how external users move through digital products, and they’ve been repurposed, with varying degrees of success, to analyze how employees move through internal tools.

The repurposing only goes so far.

Consider a proof of concept at a large contact center. By every standard product analytics metric, the environment was clean. Zero friction, no rage clicks, no thrash cursors, no long screen load times. The tools looked fine. But human agents were copying and pasting an average of 50 times per case, and half their working time on every single case was manual data transfer between systems that had never been integrated. The tools weren’t the problem. The work was the problem. And the entire category of tools designed to evaluate that environment had no way to see it.

That’s not a failure of any single product. It’s a structural limitation of what the current category was built to do.

The questions customers are actually asking

When enterprises get serious about workforce productivity, the questions they bring aren’t primarily about software performance. They want to know:

Why does a process that should take 20 minutes take two hours?
Why does a quarter of all tickets require rework?
Why do certain workflows require senior resources when they shouldn’t?

These are throughput questions, adherence questions, process quality questions, and at their core they’re financial questions because every hour of unnecessary process time carries a cost, whether organizations can see it or not.

Product analytics addresses one dimension of this. It can tell you whether a tool is contributing to the slowdown, and that’s worth something. But it’s one dollar for every five that enterprises will spend to understand the nature of the work itself. The process, the orchestration, the hand-offs, the breakdowns that happen with no digital friction anywhere in sight.

The contact center agents had clean tools and broken work. What they needed wasn’t better instrumentation of their software. They needed visibility into why each case required a human being to act as the integration layer between a dozen disconnected systems. That’s a different question entirely, and the current market doesn’t have a good answer for it.

Product analytics is aspirin. It addresses real symptoms and it helps. But the nature of work itself, the process beneath the tooling, the invisible labor that no click map or session replay will ever surface—that pain is still largely untreated.

Why assembling a stack doesn’t close the gap

The natural enterprise response to this problem has been to assemble. A product analytics platform for tool instrumentation, a digital adoption platform for guidance and training, a task miner for low-level activity capture, a process miner for higher-level workflow mapping. Four tools, four data models, four partial answers to one complete question.

The data quality issues compound the problem. Process miners run on log data, which has gaps everywhere, while task miners work from something closer to analog-quality input. AI is interpreting visual recordings of work rather than the underlying digital activity itself. Each tool sees what its collection mechanism allows it to see, and none of them were designed to see the whole picture.

What enterprises end up with is four partial views that someone has to reconcile, and that reconciliation falls to a person. Which means the stack designed to surface invisible, unnecessary labor has quietly created its own version of it. If you’re going into the woods and you can only bring one knife, you want the one that handles more than one problem, not because it’s the best at any single job but because you’re not going to face only one kind of problem out there. Enterprises never do.

The gap that workforce technology hasn’t closed

The workforce tech category has made real progress, and enterprises genuinely have better visibility into their tools than they did five years ago. Some bottlenecks that were previously invisible are now findable, and that matters.

But the contact center agent is still out there, copying and pasting, acting as the human integration point between systems that should’ve been connected long ago. Invisible to every tool in the stack because no tool in the stack was built to see that kind of work. The question worth asking isn’t which point solution answers its narrow question best. It’s what kind of foundation makes the full productivity question answerable at all.

That’s a different evaluation. And it’s one the market is only beginning to have.

How Pizza Hut turns behavioral data into AI-powered experimentation

The Fullstory Team — Tue, 25 Aug 2026 04:00:00 GMT

Pizza Hut has already reshaped how it understands customers, using Fullstory's behavioral data to move beyond commercial metrics and see the “why” behind customer behavior across 110+ markets. But behavioral data doesn't just explain what happened—it fuels what comes next. For Fabian Alvares, Global Head of CRO and Experimentation, and his team, Fullstory has become the engine behind a fast-evolving experimentation program: one that's moving from manual A/B testing to AI-built product affinities, and toward a self-improving, agentic future.

For the full picture of how Pizza Hut transformed its global customer experience with Fullstory, read the customer story: Pizza Hut transforms global customer experience with Fullstory →

Great experiments start with data, not opinions

Where do good A/B tests come from? Too many organizations still rely on the opinion of the highest-paid person in the room. Pizza Hut takes a different view: the best experiments are rooted in high-fidelity customer data. With 15 years of experimentation experience and thousands of tests behind him, Fabian treats behavioral data as the raw material for every hypothesis. Comprehensive behavioral capture lets the team do more than track what users, they can intuit what users are thinking and feeling, understand why an experiment succeeds or fails, and surface the friction that dashboards hide.

From one side item to a multivariate matrix

Pizza Hut's experimentation program spans standard A/B testing, multivariate testing, and web personalization. A recent basket-page test shows how the team scales an idea. They started simple: a pop-up modal on the basket page asking customers whether they'd like to add a single side of fries before checkout. It worked, resulting in a 1.5% increase in total transactions.

But Pizza Hut offers hundreds of side items, and testing each one in isolation wasn't feasible. So the team expanded the experiment into a multivariate test spanning a range of sides like mac and cheese, chicken, wedges, Pepsi Max, and more, capturing how customers actually responded to each option with Fullstory.

Turning behavioral data into AI-built product affinities

This is where the program takes its biggest leap. Rather than manually combing through the multivariate results, the team pushed the raw Fullstory interaction data into an AI model. Within 15 minutes, a workflow that would have taken one to two weeks by hand, the AI produced a targeted product-affinity matrix, mapping which side to surface for which customer, and when:

The output isn't just a smarter upsell. Pizza Hut is treating these product relationships as company-wide intelligence—sharing them beyond CRO with food development, product innovation, and marketing teams to shape omnichannel strategy. Behavioral data captured in a single digital experiment now informs decisions well outside the online storefront.

“Where this would have taken us maybe one to two weeks, with Fullstory and AI I was able to do it in about 15 minutes. There's a hidden value in running an experiment and using AI to claw through all that insight.”
— Fabian Alvares, Global Head of CRO & Experimentation, Pizza Hut

A blueprint for agentic, self-improving experimentation

The next frontier for Pizza Hut is a “human plus AI” agentic ecosystem. Fabian envisions a scaled optimization framework built on three principles:

The goal is a system that moves from enrichment to automated execution to continuous self-improvement—always with human-guided guardrails to evaluate and validate what the AI proposes.

Why behavioral data changes the game for experimentation

By treating behavioral data as the fuel for experimentation, Pizza Hut is changing how a global brand tests, learns, and personalizes. The team is now preparing to launch its newly mapped AI product affinities, establishing a scalable, omnichannel blueprint for digital retail.

Behavioral data is the best source of hypotheses. The strongest tests come from what customers actually do, not opinions or guesswork.
AI turns behavioral data into scale. Analysis that once took one to two weeks now takes fifteen minutes, making it feasible to personalize across hundreds of variables.
Insight shouldn't stay in a silo. Behavioral findings from a single experiment can inform product, food development, and marketing across every channel.

Your warehouse doesn't need raw events. It needs queryable tables.

Patrick Brandt — Fri, 14 Aug 2026 12:00:00 GMT

Executive summary

Raw event exports move the transformation work onto your team instead of removing it.
Analytics-ready means modeled fact and dimension tables landing in your warehouse, ready to query.
Use-case dependent: Raw to cloud storage is the right call for a few specific architectures. Modeled tables are the right call for most analysis.
AI analysts write correct SQL against a dimensional model they recognize. Against a raw event blob, they struggle.

Every data team I talk to is running more feeds than they were a year ago. Not slightly more. Meaningfully more, and each one arrives with its own schema, its own quirks, and its own claim on somebody's Tuesday.

So the question I get has changed. It used to be "can you send us the raw events?" Now it's closer to "what do we have to do to this before we can use it?"

Raw data is cheap to send and expensive to use

A raw event export is the simplest possible arrangement: one pipe, one destination; everything preserved, nothing thrown away.

The bill comes later, at query time. Raw behavioral data can't be easily queried as it lands. Somebody has to pre-process it on the fly, inside the query: transform the nested payloads, stitch events into sessions, pivot event types into something a join can reach. That work is expensive in compute and tedious in practice.

And it happens every single time. Not once, in a pipeline, but on every analyst's first attempt at every question. Two people ask the same question, write two different transformations, and get two different numbers. Then somebody has to reconcile them, which is its own meeting.

Meanwhile the data team that was supposed to be building something is instead maintaining the connective tissue between what a vendor sent and what an analyst can actually use. That work is a real job. Companies hire people to do it, and there's an entire category of vendors selling services that do it for you. The question worth asking is whether the job needs to exist at all.

What "analytics-ready" actually means

Plain terms: fact and dimension tables.

Facts hold the measures, the events themselves. Dimensions hold the things you slice by: user, element, page, and so on. It's the same shape data warehouses have used for decades.

That's the point. It isn't a novel format. It's the format every BI tool expects, every analyst was trained on, and every AI coding model has seen more of than any other.

Here's how we do it at Fullstory. We pre-process raw behavioral event data at scale into fact and dimension tables, then export that ready-to-analyze data directly into BigQuery, Snowflake, Redshift, and Databricks.

The work that disappears is the reshaping. Your team goes from raw events to answers without building the layer in between.

One more piece of it, and this is the part data engineers tend to react to: when we add new event types or new data features over time, they show up in your tables automatically. No pipeline change on your side. No migration. The model grows and your queries keep working.

Where raw still fits, and why we're not replacing your warehouse

None of this makes raw data wrong! We ship both, and the raw-to-cloud-storage option exists because some teams genuinely need it.

Three cases where I'd point you at raw without hesitating:

Security or compliance requires it. The data has to land in your own object storage before anything else gets near it.
Your architecture already stages everything. If every source in your environment lands in object storage on the way to the warehouse, you probably don't want one vendor to be the exception.
You have a modeling standard of your own. Some teams conform every source to a house model. If you're going to remodel it anyway, take the raw feed.

What I'd push back on is choosing raw by default. Or choosing it because keeping everything and deciding later feels safer. That's how you end up paying for the transformation twice: once in compute, once in calendar time.

I want to be precise here, because "we model the data for you" can sound like "we'd like to be your warehouse."

We wouldn't and we aren't. The data lands in your warehouse. Your governance, your access controls, your cost center, joined to your CRM and your transactions and everything else that makes behavioral data worth having. Snowflake stays Snowflake. BigQuery stays BigQuery.

What we take off the table is the transformation tier between our export and your first useful query.

Hourly syncs, and where streaming takes over

It’s worth being precise about timing, because "analytics-ready" can get misread as "instant."

Warehouse data syncs hourly. That's the right cadence for what the warehouse is actually for: cohort analysis, attribution, modeling, anything where you're reasoning over history and joining behavioral data to the rest of the business.

It's the wrong tool for acting on somebody who is struggling right now. That's a streaming problem and it needs a streaming answer, which is what Activation streams are for. Different job, different pipe, and many teams end up running both.

So analytics-ready isn't a claim about latency. It's a claim about readiness. It removes the work that sits in front of the query, which saves your data team time and money and shortens the trip from question to answer.

Why the shape of the data matters more now that agents are querying it

This is the part that changed fastest.

Analysts are no longer only people. A meaningful share of the queries hitting behavioral data now come from an AI agent working on someone's behalf, and agents are far more sensitive to structure than we are.

Point an agent at raw events and ask something ordinary, like how many people completed registration yesterday. It has to infer what the events mean, guess at how they relate, and construct the reshaping logic before it can even attempt the count. Sometimes it lands. Often it loops, and every attempt costs you tokens and time.

Point the same agent at fact and dimension tables and it recognizes the shape on sight. Decades of SQL have been written against dimensional models, so that's the pattern these agents handle best. Natural language goes to correct SQL on the first pass.

The second-order effect is the interesting one. When the translation from question to query gets reliable, the set of people who can ask a question stops being the set of people who can write SQL. That's a much larger group.

Which is also why "we'll model it later" is a worse answer than it used to be. The shape of the data now determines who gets to use it.

What to ask, whoever you're buying from

The clearest signal I've seen recently didn't come from a product conversation. It came from a budget one.

We were talking with a large education provider about how the data would land. When they understood that we transform it and export it directly to their warehouse, the thing they reacted to wasn't the modeling. It was the line item they no longer needed for third-party ingestion and transformation tooling. The quote I remember was "I wish everybody were like Fullstory."

I'll take it, though I'd note what they were actually praising. Not a feature. The absence of a project, and the people and budget it would have consumed.

The other pattern worth naming: customers exporting warehouse data across multiple accounts so they can run cross-account analysis in one place. That only works if the data arrives in a consistent, modeled shape. Raw events from five accounts are five reconciliation problems.

Applies to us as much as anyone. If you're evaluating how behavioral data gets to your warehouse, these are the five questions that surface the real cost:

What shape does it land in?	Raw events, or modeled tables an analyst can query without a rebuild?
Who owns the transformation, and who owns it when the schema changes?	The second half of that question is where the maintenance cost hides.
What happens when new event types appear?	Automatic, or a ticket for your team?
How fresh is it, precisely?	Ask for a number and an SLA, not an adjective. "Real-time" is not a number.
What's the total cost of getting it query-ready?	Include the ingestion tooling, the transformation tooling, the compute burned on repeated on-the-fly reshaping, and the engineer-weeks. The pipe is rarely the expensive part.

The bottom line

The warehouse stopped being a place data goes to sit. It's where analysis happens, increasingly with an agent doing the asking, and that raises the bar on what "delivered" means.

If the first thing your team has to do after receiving data is rebuild it, the delivery isn't finished. Somebody just moved the work to your side of the wall.

Visit our Anywhere: Warehouse page to learn more.

What is A/B Testing? A Complete Guide

Fullstory — Sat, 10 Feb 2024 07:00:00 GMT

What is A/B testing?

A/B testing, also called split or bucket testing, involves presenting two versions (A as the control and B as the variant) to comparable visitors or viewers simultaneously to see which one improves your chosen metric.

How does A/B testing work?

A/B testing works by comparing two versions of content to determine which one performs better. One version (A) is the control, while the other (B) includes a change you want to test. Users are randomly assigned to either version to reduce the risk of bias and ensure that differences in performance are due to the change being tested—not external factors.

The test runs for a set period to gather enough data for statistically significant results. Once complete, teams analyze metrics such as conversion rates, clicks, or purchases to determine which version performed better. These insights help inform decisions on which changes to implement for optimization

6 key benefits of A/B testing

When there are problems in your conversion funnel, you can use A/B testing to pinpoint the cause. Some of the most common conversion funnel leaks include:

Confusing call-to-action buttons
Poorly qualified leads
Complicated page layouts
Too much friction leads to form abandonment on high-value pages
Bugs or frustration points during the checkout process

With A/B testing, you can target these areas by experimenting with different landing pages, CTAs, and layouts to pinpoint exactly where users are dropping off.

1. Solve visitor pain points

When visitors come to your website or application, they usually have a goal in mind. They might want to:

Learn more about a deal or special offer
Explore products or services
Make a purchase
Read or watch content about a particular subject

Even casual browsing counts as a goal—users may simply want to explore and familiarize themselves with your offerings. Unfortunately, these goals can be hindered by roadblocks like confusing navigation, unclear copy, or broken CTAs that disrupt their experience.

Several tools can help you understand this visitor behavior and optimize user experiences. Fullstory, for example, offers heatmaps, funnel analysis, session replay, and other tools that reveal how users interact with your site. By analyzing this data, you can identify the source of user pain points and start fixing them.

2. Get better ROI from existing traffic

If your website or app attracts significant traffic, A/B testing can help you maximize ROI by improving conversion rates.

Instead of spending more money acquiring new visitors, A/B testing allows you to optimize your conversion funnel and identify which changes—such as improving CTAs, simplifying forms, or enhancing page layouts—will have the most significant impact on user experience and conversions.

This targeted approach often yields greater returns at a lower cost than continuously investing in new traffic acquisition.

3. Reduce bounce rate

Bounce rate measures how often visitors land on your site or app, view just one page, and then leave. This metric is calculated by dividing the number of single-page sessions by the number of user sessions on your website. While there are other ways to define bounce rate, they all point to the same thing: disengaged users.

A high bounce rate means visitors often encounter frustrating or confusing experiences on your website or mobile application that cause them to leave quickly. This is a great opportunity for A/B testing. By identifying high-bounce pages, you can test and tweak problem areas. A/B testing helps track the performance of different versions until you see an improvement.

Over time, these tests help uncover specific pain points and improve the overall user experience.

4. Make low-risk modifications

Making significant changes to your website or mobile application always comes with risk.

You could spend thousands of dollars on a full redesign for an underperforming campaign, but if those changes don't deliver results, you’ll lose time and money.

A better approach is to use A/B testing to make small, incremental changes instead of overhauling everything at once. This way, if a test fails, you’ve minimized the risk and cost while still gathering valuable insights to guide further improvements.

5. Achieve statistically significant results

A/B testing is only reliable when the results are statistically significant. This means the observed differences between variations are unlikely to be due to chance. Statistical significance confirms that the results reflect real user behavior, making them actionable for decision-making.

A 95% significance level is ideal for most tests. Some teams may target 90% to reduce the sample size and speed up results, but this increases the risk of inaccurate findings.

Several factors can prevent you from reaching statistical significance, including:

Not enough time to run tests
Pages with very low traffic
Changes that are too small to produce meaningful results

To achieve statistical significance quickly, you can test on high-traffic pages or implement larger, more impactful changes.

6. Redesign websites to increase future business gains

A/B testing isn’t only for minor tweaks to your website or application. Even during a full redesign, this kind of testing can still be valuable. For example, you could create two versions of your site and measure the results after receiving a statistically significant number of visitors.

Even after the new site or application goes live, you should continue A/B testing. In fact, following a big launch is the perfect time to start refining and optimizing individual elements.

Common types of A/B testing

There’s no single right way to conduct A/B testing. Different methods are suited to different goals and use cases, and you should tailor your approach based on your desired outcomes. That being said, there are 3 common types of testing:

1. Standard A/B Testing

Standard A/B testing compares two versions of the same page or element, with users randomly assigned to variation A (control) or variation B. The goal is to measure which version drives better performance through statistically significant data.

For example, a retailer might test two versions of a product page—one with a more prominent "Add to Cart" button and another with a promotional badge—to see which version increases purchases.

2. Split URL Testing

Split URL testing is a variation of A/B testing where users are directed to different URLs instead of making changes to a single page. This kind of testing is helpful for major changes to design, structure, or backend functionality, such as page load optimizations or new workflows.

For example, a bank might use split URL testing to test a new version of its credit card application flow by directing some users to a completely redesigned application page hosted at a different URL.

3. Multipage Testing

Multipage testing tests a single change across multiple pages within a workflow or funnel. This can be useful when optimizing entire user journeys rather than individual pages.

For example, a restaurant chain could add customer testimonials to the menu, reservation, and contact pages of its website to see if it increases overall reservation completions.

How do you choose which type of test to run?

There are several factors to consider when deciding which tests to run for conversion rate optimization (CRO) testing. You should think of:

The number of changes you’ll be making
How many pages are involved
The amount of traffic required to get a statistically significant result
The size and impact of the problem you're trying to solve

Simple changes can often be effectively tested with standard A/B testing. More extensive changes involving multiple pages or total design overhauls may call for approaches like split URL or multipage testing.

How do you perform an A/B test?

Following a structured process helps ensure your A/B test delivers reliable, actionable insights.

Step 1: Research

Before conducting tests or making changes, it's important to set a performance baseline. Collect both quantitative and qualitative data to learn how your website or application is performing in its current state.

The following elements represent quantitative data:

Bounce rate
Video views
Traffic
Subscriptions
Average items added to the cart
Purchases
Downloads

Much of this information can be collected through a behavioral data platform like Fullstory.

Qualitative data, on the other hand, focuses on the user experience and is typically collected through polls, surveys, or interviews. When these two types of data are used together, they provide a more complete picture of user behavior.

Step 2: Observe and formulate a hypothesis

At this stage, you analyze your data and write down your observations. This helps you develop a hypothesis that will eventually lead to more conversions. In essence, A/B testing is hypothesis testing.

Step 3: Create variations

A variation is a new version of a page that includes the changes you want to test. A good rule of thumb is to optimize areas where users tend to drop off, such as sign-up forms or checkout pages.

Try to keep your test focused—changing too many elements at once can make it difficult to determine which change drove the result.

Step 4: Client-side vs. server-side A/B testing

Before running your test, you'll need to decide whether to deliver the variations to users through client-side or server-side testing.

With client-side testing, JavaScript runs in the user’s browser to adjust what they see based on the variation assigned. This method works best for front-end changes like fonts, color schemes, layouts, and copy. For example, if you’re testing the wording or color of a CTA button, client-side testing is ideal.

Server-side testing happens before the page is delivered to the visitor. It's more robust and suited for backend or structural changes, such as improving page load time or testing different workflows. This method allows you to test elements that affect performance and engagement, like speeding up checkout steps or altering database queries to reduce response time.

Step 5: Run the test

With variations and a testing method selected, it’s time to run your test. The length of the test depends on how much traffic you receive and the level of statistical significance you want to achieve. Higher significance provides more reliable insights but requires more time and traffic.

While the test is running, monitor performance and watch for potential technical issues. Additionally, try to avoid making changes mid-test, as this can compromise the accuracy of your results.

Step 6: Analyze results and deploy the changes

Once the test is complete, review the results and draw conclusions. If the data shows that one version clearly outperformed the other, you can deploy the winning change. However, not all tests produce conclusive results. In those cases, you may need to test additional changes or refine your hypothesis to gather further insights.

A/B testing is an iterative process. After one test, you might move on to other pages or stages of the customer journey. By continuously testing and optimizing different pages, elements, and features, you can improve the user experience and drive higher conversions over time.

How do you interpret the results of an A/B test?

A/B test results are measured by key performance indicators (KPIs), which reflect the actions you want users to take. These success indicators might include clicks, views, sign-ups, purchases, or downloads, depending on your goals.

After completing your test, you’ll evaluate whether one variation performed significantly better than the other based on statistical significance and margin of error.

Frequentist vs. Bayesian approaches

There are two common ways to interpret A/B testing results: frequentist and Bayesian.

Frequentist approach: This method assumes there is no difference between variations at the start (called the null hypothesis). Once the test is complete, you’ll calculate a p-value, which indicates the likelihood that any difference between variations is due to chance. A low p-value means the results are statistically significant. However, frequentist tests require the full test to run before results can be finalized.

Bayesian approach: Bayesian testing uses prior data and new evidence from the current test to update predictions. This method allows you to review results mid-test and call a winner early if one variation clearly outperforms the other.

The top A/B testing tools to use

Several tools are available to help businesses set up, execute, and track A/B tests.

The best web and app A/B testing tools

Optimizely

Optimizely is a platform designed for conversion optimization through A/B testing. It allows teams to set up tests on website changes and collect data from real user interactions. Users are routed to different variations, and their behavior is tracked to determine which version performs better. In addition to A/B testing, Optimizely supports multipage and other types of testing to optimize various aspects of the user experience.

AB Tasty

AB Tasty facilitates A/B and multivariate testing, allowing testers to choose between client-side, server-side, or full-stack testing options. The platform also includes features like a Bayesian Statistics Engine to track and analyze results.

Both Optimizely and AB Tasty integrate seamlessly with Fullstory, so you can track user interactions with different test variations.

VWO

VWO is another major player in A/B testing and experimentation software. Like Optimizely and AB Tasty, they offer web, mobile app, server-side, and client-side experimentation, as well as tools for personalizing user experiences.

Email A/B testing tools

Moosend

Moosend helps marketers create and manage email campaigns, including A/B split testing. This feature allows teams to test different variations of marketing emails, measure user response, and identify the best-performing version.

Aweber

Aweber offers split testing for up to three email variations. Users can test elements like the subject line, preview text, message content, and send times. It also supports audience segmentation and allows testers to compare entirely different emails.

MailChimp

MailChimp allows users to A/B test email subject lines, sender names, content, and send times. Users can create multiple variations and control how recipients are split among them. Testers can define the conversion action and duration to determine the winning variation, such as tracking open rates over an eight-hour period.

Constant Contact

Constant Contact offers automated A/B testing for email subject lines. Once the test is complete, the tool automatically sends the winning subject line to the remaining recipients, helping users optimize engagement with minimal effort.

A/B testing and CRO services and agencies

Some companies have the infrastructure and bandwidth to run their own experimentation programs, but others might not. Fortunately, there are services and agencies that can help drive your A/B testing and CRO efforts.

Conversion

Based in the UK, Conversion is one of the world's largest CRO agencies. It works with brands like Microsoft, Facebook, and Google.

Lean Convert

Also based in the UK, Lean Convert is one of the leading experimentation and CRO agencies.

Prismfly

Prismfly is an agency that specializes in ecommerce CRO, UX/UI design, and Shopify Plus development.

Create a data-driven culture with A/B testing

To unlock the full potential of A/B testing, businesses must go beyond isolated experiments and foster a culture of continuous data-driven decision-making.

Here are some tips to help you get started.

Encourage curiosity and experimentation

Teams should feel empowered to ask questions like, "What would happen if we changed this headline?" or "Could a simpler form design improve conversions?" By supporting experimentation at every level, you create an environment where ideas are tested and backed by evidence—not assumptions.

Break down silos

A/B testing is most effective when cross-functional teams, including those from product, marketing, design, and engineering, collaborate.

For example, product teams can optimize onboarding workflows, while marketing can test landing page copy. Sharing insights across departments prevents duplicated efforts and accelerates innovation.

Measure success and share wins

Celebrate and share testing insights across the organization. By communicating wins and lessons learned, you reinforce the importance of testing and encourage other teams to get involved. Over time, this helps establish a sustainable culture of optimization.

Further reading: Struggling to unlock the full potential of your data in your organization? Our 6 Hurdles of Modern Data Teams guide will help you address the biggest data-related challenges.

Drive data-driven success with Fullstory

A/B testing is an effective way to improve user experience and drive business growth through continuous optimization. Whether you're tweaking a landing page or conducting a full redesign, testing helps minimize risk, uncover visitor pain points, and maximize your existing traffic's potential.

Fullstory helps you take testing further by giving you the behavioral insights you need to create better experiences. Want to see how Fullstory can help your business? Get a demo today.

8 of the best customer journey mapping tools

Fullstory — Wed, 28 May 2025 11:00:00 GMT

The customer journey is rarely linear and rarely simple.

Customers jump between devices, revisit decisions, and bring expectations shaped by every other digital experience they’ve had. Journey mapping tools help make sense of that complexity. By visualizing real customer paths, interactions, and actions—specific steps or behaviors customers take during their journey—these tools reveal friction points, clarify intent, and surface opportunities to improve the experience.

Whether you’re designing a brand-new flow or trying to understand why users drop off midstream, this guide covers the top tools for mapping, analyzing, and improving the customer journey, plus a breakdown of what each tool does best.

The best customer journey mapping tools at a glance

Here’s a quick breakdown of the top journey mapping software, whether you’re looking to analyze real user behavior, get started quickly with pre-made templates, or find insights to sketch out your ideal experience.

These are the top picks, organized by what they do best:

Best Overall – Fullstory is the best overall customer journey mapping tool. It combines powerful session replay, real-time analytics, and journey mapping to give you a complete picture of how users behave and where they drop off. Fullstory makes journey mapping easier by visualizing user paths, highlighting friction points, and allowing you to share journey insights or reports with stakeholders.
Great for Journey Visualization – Miro: Ideal for teams who want to sketch, ideate, and collaborate on complex journeys using an infinite whiteboard and drag-and-drop tools. Miro helps teams brainstorm and organize ideas visually, offering digital sticky notes as an alternative to physical ones for mapping and collaboration, which many users like for its flexibility and ease of use.
Great for Data-First Teams – Heap: Offers automatic event tracking and funnel insights to help product and growth teams fine-tune the journey with precision. Heap lets you find valuable insights and opportunities to optimize the customer experience.
Great for Persona-Driven Mapping – Smaply: Focused on detailed journey maps with persona and stakeholder alignment baked in. Smaply is made for specific use cases and offers pre-made templates as customizable frameworks to help you get started faster.
Great for AI-Driven Mapping – TheyDo: Designed to scale mapping across orgs with automation, prioritization frameworks, and strategic alignment tools, consolidating journey management in one platform.
Great for Designers – Figma: Great for visual-first product teams building polished prototypes with real-time collaboration. Users like how Figma streamlines the design process, makes collaboration seamless, and offers sharing features for collaborative design.

What is customer journey mapping?

Customer journey mapping is the process of visualizing how users interact with your brand across every touchpoint—from first visit to conversion (and beyond). Customer journey analytics is more than a flowchart. When grounded in real behavioral data, it becomes a strategic tool to spot friction, understand user intent, and uncover what’s really driving—or blocking—progress.

Journey mapping helps teams pinpoint high-friction or high-impact moments in the experience, including places where form abandonment quietly derails an otherwise strong conversion path.

Done right, journey mapping helps teams:

Pinpoint high-friction or high-impact moments in the experience
Prioritize fixes based on real user behavior, not assumptions
Align teams across product, marketing, and CX around a shared view of the customer
Improve satisfaction, reduce drop-off, and drive long-term loyalty

While this guide focuses on the tools that make mapping possible, we dive deeper into the methodology—personas, touchpoints, and emotional context—in our full explainer: What is customer journey mapping?

Marketing analytics tools show performance at the channel level. Customer journey mapping tools connect those data points into a complete view of how users move from first click to conversion and where they get stuck in between.

In short, digital customer journey mapping, powered by behavioral data and customer insights, is an effective way to turn guesswork into action and turn scattered teams into customer-centric machines.

Why user behavior and sentiment matter more than assumptions

Understanding what customers want—and how they behave—is at the heart of meaningful journey mapping. The right tools help you gather data across touchpoints, identify what matters most to users, and zero in on where the experience breaks down. It's essential to understand customer sentiment, feelings, and motivations throughout their journey to create truly effective maps.

By spotting behavior patterns and intent signals, you can personalize experiences, prioritize smart fixes, and rally cross-functional teams around the moments that matter most.

Key steps include:

Researching customer behavior to inform journey design
Identifying high-value touchpoints for each audience
Using journey mapping tools to visualize and act on insights
Creating experiences that meet real customer needs
Gathering feedback from real people to inform and refine journey mapping

Focus on what matters to customers, and you create journeys that drive retention, loyalty, and long-term growth.

Two types of tools, two lenses on the journey

To actually put journey mapping into practice, you need the right kind of tool—and they generally fall into two categories:

Design and planning tools: These help you visualize the ideal journey—what the experience should look like. Think diagrams, flowcharts, and wireframes that help teams plan and align before a product or experience is built.

Behavior and analytics tools: These show you the real journey—how users actually move through your site or app. With behavioral data, you can see what’s working, what’s not, and what to fix next.

Let’s take a look at the top tools across both categories, starting with design and customer journey visualization.

The best customer journey mapping tools for visualizing user paths

Before you can improve the journey, you need to see it.

These tools help you do just that—by sketching, wireframing, or mapping the intended experience. Whether you’re building early-stage prototypes, running workshops, or aligning teams around an ideal flow, these platforms let you visualize customer paths, interfaces, and key moments.

Some tools are built specifically for journey maps, service blueprints, and blueprints, providing detailed visualizations of both customer experiences and the internal processes that support them. Others offer broader collaboration canvases that teams can adapt for mapping, ideation, or interface design. Many of these platforms also support user journey maps, which are especially useful for visualizing user actions and processes within digital channels to optimize digital experiences.

Here are some of the top picks solely for designing and mapping customer journeys:

Fullstory

Fullstory is a powerful behavioral data platform that offers robust journey map analysis. Unlike alternatives that may focus on limited touchpoints, Fullstory's unique selling proposition is the level of detail and precision it offers in user behavior data.

Pros

Autocapture: Tracks every click, pageview, scroll, form fill, and more—automatically, without manual tagging.
Session replay: High-fidelity replays help visualize what users actually experienced.
Journeys & funnels: Visual pathing tools show how users move, where they drop, and what paths convert best.
Integrations: Strong support for data pipelines (Snowflake, Segment, BigQuery) + collaboration tools.
Guides and Surveys: Collect in-the-moment user feedback and provide contextual help to drive engagement.
StoryAI: Automatically analyzes user sessions to pinpoint top friction moments and quantify their impact.

Cons

May require a learning curve to fully utilize its comprehensive key features.
Costs can vary depending on scale and needs.

Pricing

Free version available
Fullstory offers plans based on your business's specific needs and scale.

Figma

Figma remains a strong choice for collaborative interface design. Its web-based platform allows real-time collaboration, making it ideal for teams working remotely. Recent updates have enhanced its prototyping capabilities, and its integration with other tools has improved workflow efficiency.

Pros

Real-time collaboration across devices
Robust prototyping features
Extensive plugin ecosystem

Cons

Some advanced features may have a learning curve for new users
Pricing changes in 2025 have increased costs for some plans

Pricing

Free plan available
Paid plans start at $20 per editor/month for the Professional plan

Miro

Miro offers an infinite canvas, making it suitable for mapping complex customer journeys. Its intuitive interface and variety of templates support diverse use cases, from brainstorming sessions to detailed journey mapping.

Pros

Infinite canvas for expansive mapping
User-friendly with a plethora of templates
Strong collaboration features

Cons

May be overwhelming for beginners due to its extensive features
Some advanced functionalities require higher-tier plans

Pricing

Free plan with limited features
Paid plans start at $10 per user/month for the Starter plan

Smaply

Smaply is tailored specifically for customer journey mapping, offering tools to create personas, stakeholder maps, and journey maps. Its focus on journey mapping makes it a specialized tool for teams dedicated to customer experience design.

Pros

Specialized features for journey mapping
Supports creation of personas and stakeholder maps
Collaborative features for team-based projects

Cons

May be less versatile for general prototyping needs
Interface may feel dated compared to newer tools

Pricing

Free plan available
Paid plans start at €390 per editor/year for the Repository plan

Sketch

Sketch has been a staple in the design community, especially among Mac users. It offers a robust set of features for designing user interfaces and prototyping, with a focus on vector editing and precision.

Pros

Comprehensive vector editing tools
Extensive plugin library
Optimized for macOS

Cons

Available only on macOS
Collaboration features are not as advanced as some competitors

Pricing

30-day free trial available
Standard plan at $9 per editor/month

TheyDo

TheyDo is a comprehensive journey management platform designed to help organizations visualize, manage, and optimize customer experiences across all touchpoints. It offers a centralized workspace where teams can map customer journeys, identify opportunities for improvement, and align initiatives with business goals.

Pros

Built specifically for end-to-end journey management
Lets teams connect initiatives and backlogs directly to journey insights
Opportunity mapping and prioritization are baked into the workflow
Integrates with tools like Jira, Miro, and DevOps for execution alignment
Offers AI-powered suggestions for gaps, needs, and opportunities

Cons

Pricing is enterprise-tier (starts around $35,000/year)
May be overkill for smaller teams or basic journey mapping needs
Less focused on pixel-perfect design/prototyping compared to tools like Figma

Pricing

Free trial available with limited journeys/editors
Paid plans start at enterprise level ($35,000+/year depending on size and features)

Other journey map analysis tools

Prototyping a journey is only half the battle. To really optimize your customer experience, you need to see what’s actually happening: where users go, where they struggle, and what drives them to convert—or bounce. Teams need to work together to analyze these journeys, share findings, and collaborate on improvements.

That’s where customer journey map analysis tools come in. These platforms give you visibility into real user behavior: clicks, scrolls, drop-offs, rage clicks, and everything in between. Instead of assuming what the journey looks like, you get to see it. They also provide valuable customer insights that help you understand channel usage, backstage processes, and contextual factors, driving better customer experience design.

If you want to know how users actually move through your product—not how you think they do—these tools are built for that.

You can analyze key touchpoints such as products, support, and onboarding. It’s important to keep your journey maps and data up to date to ensure your analysis remains accurate and actionable.

Here’s a breakdown of the best platforms for analyzing behavior, identifying friction, and optimizing the real customer journey.

Heap.io

Heap is a product analytics platform that automatically captures all user interactions, allowing teams to analyze user behavior without manual event tracking. It offers features like funnel analysis, retention tracking, and user segmentation.

Pros

Good segmentation capabilities

Cons

The interface can be complex for beginners
Custom events may be challenging to set up

Pricing

Free plan with limited data retention and features.
Paid plans are custom based on usage, data volume, and add-ons (e.g., session replay, advanced governance).

Glassbox

Glassbox is a customer experience analytics platform that provides real-time insights into user behavior across websites and mobile apps. It offers features like session replay, heatmaps, and AI-driven analytics to help businesses optimize digital experiences.

Pros

Provides a user behavior data.
Real-time data capture

Cons

May be expensive for small businesses or startups
The interface may be less intuitive than some other tools

Pricing

Pricing is customized based on session volume, data retention period, and selected features

Choose journey mapping software that meets real customer needs

It’s one thing to design a customer journey. It’s another to actually understand how it plays out.

That’s why the most effective journey mapping software doesn’t stop at diagrams — it helps teams uncover what’s broken, align around customer needs, and create experiences that work.

If you want a top solution for mapping user journeys that’s easy to use, captures the full picture, and turns every session into an opportunity to improve…

Fullstory is built for that.

From pain point discovery to high-impact wins get the behavioral insights and analysis you need to understand, prioritize, and improve every step of the journey. No assumptions. Just real user data and a faster path to better customer experience.

Watch our 4-minute product tour to get started →

Marketing analytics tools show performance at the channel level. Customer journey mapping tools connect those data points into a complete view of how users move from first click to conversion and where they get stuck in between.

What is user friction? How to avoid the mistakes and optimize your UX

Fullstory — Tue, 21 Dec 2021 07:00:00 GMT

What is user friction?

User friction—also called user experience friction or UX friction—is anything that keeps a user from accomplishing a desired action on a website or app. Just like physical friction prevents your shoes from slipping on a slick hardwood floor, digital friction can prevent users from achieving their goals online—for example, making purchases.

User friction can happen for many reasons:

A site or app isn’t intuitive
There’s a bug or malfunction
The user interface isn’t designed clearly or isn’t usable
An action requires too many extra steps to complete
Pages have slow load times
Forms or other inputs don’t follow best practices
Users mistaken take irreversible actions
User expectations are simply not met

It’s clear that design and product teams need to be vigilant when it comes to reducing user friction. Otherwise, these issues will lead directly to increased abandonment, reduced conversion, and lost revenue.

The different types of user friction

Understanding UX trends can help address the different types of user friction that occur on websites or apps.

Three key categories of user friction are emotional friction, interactive friction, and cognitive friction. Here’s a quick look at what can cause each of these kinds of user friction.

Emotional friction

Emotional friction is often considered the most complex type of user friction and deals with users’ feelings.

Users often experience emotional friction when it’s difficult to complete a task on a site or app, with can result in negative emotions on the user's side. Generally, UI design tools aim to evoke positive emotions and avoid making users feel bad while using a product.

One example of an app that focuses on evoking positive emotions is the project management software, Asana. When a user marks a task “complete,” Asana rewards users with a satisfying shade of green and whimsical, celebratory characters zoom across the screen—creating positive feelings for the user who just completed a task.

You’ll notice this in the many other apps that gamify a user’s journey through rewards, scores, checklists, etc.

Interaction friction

Interaction friction is more easily quantifiable than emotional friction, as it occurs when a site or app’s interface is confusing or not easily navigable for users. When you can create a user experience with little or no interaction friction, you reduce the likelihood that a user will become confused or frustrated and abandon the site altogether.

Apple is a great example of a company that’s minimized UI and interaction friction, effectively revolutionizing mobile devices.

Since its introduction in 2007, the iPhone has offered a level of usability that’s been largely unmatched by any other smartphone manufacturer. This is likely due to Apple founder Steve Jobs’ unique design philosophy, which roughly states that you should start with the user experience and work backward to the technology, rather than the other way around. This is a powerful example of how user experience—rather than technology—can make your product shine.

Cognitive friction

Cognitive friction is a user experience issue that occurs when an interface doesn’t function in the way a user expects it to, and requires too much cognitive load to complete a given task.

To avoid cognitive friction, UX designers try to make an interface mimic the real world as much as possible, increasing the chances it seems intuitive.

For example, when shopping online you add items to your “cart” or “bag,” which is exactly what you would do while shopping in real life.

Imagine if you visited an ecommerce site that prompted you to “save” or “bookmark” an item—you’d probably be confused or feel uncomfortable, and leave for a site that used familiar terms.

How to identify user friction

In the past, finding friction in user journeys required collecting qualitative data. That is, conducting time-consuming interviews or counting on your users to leave feedback. Each of these is highly unreliable, considering only 12% of consumers are likely to provide feedback to a business when they encounter an issue.

That’s why it’s a team’s responsibility to combine different types of data and find insights through both qualitative and quantitative research.

With modern product analytics and a behavioral data platform, you can automatically detect different types of friction. This means you no longer need to wait for users to tell you that they are frustrated—you can proactively find and fix many different types of friction using features like session replay and heatmaps to visualize user frustration.

What types of friction should you be looking for? Here are some signs that your users are having frustrated experiences.

Rage Clicks

Rage clicks happen when users click or tap rapidly on your site or app, usually signaling that a button isn’t working (or isn’t working quickly enough).

Users might Rage Click on many different elements on your site or app, from buttons to graphics to text. Typically, Rage Clicks mean that an element on your site isn’t meeting a user’s expectations.

For example, one fintech company found that people were Rage Clicking on credit card logos on their checkout page, believing they were buttons to select their payment type. In reality, the icons were only there to show accepted cards; this unnecessary checkout friction was actively stalling the user journey. By removing the frustrating element, the company saw a 7% increase in conversion rates.

Dead Clicks

Dead Clicks are the result of a user clicking or tapping on a non-clickable element. Dead Clicks and Rage Clicks are similar, considering that users might end up Rage Clicking on an element that doesn’t actually change the page. However, a Dead Click is a single click or tap rather than rapid-fire clicks.

Dead Clicks are more subtle than Rage Clicks, so it can be more difficult to determine what a user is actually trying to do when a Dead Click occurs. For example, a user may be clicking the background of a webpage, which would result in a Dead Click, but it can be difficult to understand if they are trying to escape an overlay, ensure focus is on the page, or something else.

Error Clicks

An Error Click occurs when a user triggers a client-side JavaScript error, though the user might realize that they’ve encountered a broken element.

By watching user sessions that include Error Clicks, developers can more easily diagnose and solve bugs by seeing the conditions that produced JavaScript errors.

Searching and monitoring the top Error Clicked elements on your site or app can help you understand if a critical button—like an “Add to cart” button—is malfunctioning and potentially costing you revenue.

Thrashed Cursor

Cursor thrashing is when a user moves their cursor rapidly back and forth across a web page, typically indicating some type of frustration.

There are a few instances that might elicit cursor thrashing, the most common being slow load times.

A thrashed cursor could also mean that a user can’t figure out or find something they’re looking for on a page. For example, if a user is trying to locate the search bar on your website, they might move their mouse around quickly while their eyes search the page.

It’s important to keep in mind that Thrashed Cursors could be a false positive; perhaps a users’ mouse is broken or they are trying to locate their cursor on a page. If you’re getting Thrashed Cursor signals on your website, the best first step is to watch a session that contains cursor thrashing and try to see what’s happening.

Form Abandonment

Filling out online forms is almost universally frustrating. Sometimes the “tab” key will take you to the same box and sometimes it won’t, some forms will alert you along the way if you’ve made an error and some wait until you try to submit it, and some forms are just plain long and tedious.

And no matter how many best practices you follow — an easier workflow, instant validation, faster load time, a simple layout, etc. — you’ll notice your users are usually unique to your site.

Whatever the issue, form abandonment is an extremely common problem for online companies and customer experiences. That’s why you should search for browser sessions where users abandoned a form and utilize session replay to watch sessions and understand exactly where and why users give up. You can identify the specific field causing friction and then use that info to make it better for users.

Looking for form abandonments is a smart way to find instances of user frustration, and addressing those issues can help product teams improve conversion funnels and see higher conversion rates.

Pinch-to-Zoom

Though a more subtle frustration signal than others covered here, Pinch-to-Zoom is when a user uses a touchpad or touchscreen to zoom in or out. This signal can be detected from any device that has a touch interface or multi-touch trackpad—including desktop and mobile.

Pinch-to-Zoom almost exclusively indicates that some element of your digital experience is sized improperly or is displaying incorrectly. For instance, an ecommerce site might render on mobile in such a way that the “add to cart button” is cut off, and the user needs to zoom out to get to it. Or, the text on a web page might be too small to read comfortably, causing a user to zoom in.

Is user experience friction always a bad thing?

In short: No. Believe it or not, there are times when friction can actually be beneficial to the user experience.

There are certain times when users appreciate a measured amount of friction—like when handling personal information, finances, or other sensitive information online.

Google’s 2-Step Verification is a great example of good friction. Does anyone really enjoy taking the time to go through a two-step login process for their Google account? Probably not. But when so much of our digital lives is powered through the Google suite, users can appreciate the need to take the extra action to keep their information and documents secure. Plus, Google does an excellent job of making two-step authentication as painless as possible by storing passwords and enabling your devices to communicate with one another.

How can design modification reduce friction?

Reducing friction in your user experience has many obvious benefits: decrease user frustration, increase conversions, drive loyalty and retention, and ultimately boost revenue.

Through navigation and usability testing and product analytics solution to monitor user frustration signals—Rage Clicks, Dead Clicks, Error Clicks, Thrashed Cursors, Form Abandonment, and Pinch-to-Zoom—you can pretty quickly identify the elements on your website that users are getting hung up on.

Once you’ve pinpointed an area of friction, watch user sessions to understand what should be happening. You can also use A/B testing—such as trying different layouts or simplifying workflows—to determine which changes reduce friction most effectively. A/B testing helps you make data-driven decisions to enhance user experience and improve overall site performance. Is it that a button should trigger an event and doesn’t, or that a user should be able to find the item they want and can’t?

Reduce cart abandonment: 8 proven solutions to recover potential sales

The Fullstory Team — Wed, 11 Feb 2026 20:00:00 GMT

If you’re seeing a disconnect between shopping cart fills and completed transactions, it’s time to tackle cart abandonment head-on. Let’s explore a few ways to influence customer decisions, reduce cart abandonment rates, and capture missed e-commerce sales.

Understanding shopping cart abandonment

The shopping cart is where browsing turns into buying, but too often, shoppers leave without completing their purchase. This is a very common challenge for online retailers. According Baymard, the average cart abandonment rate is a staggering 70%. That’s nearly three-quarters of online shoppers retreating at the final hurdle, contributing to an estimated $260 billion in lost sales annually.

Understanding why shoppers abandon their carts is key to winning back lost sales. Common reasons include unexpected shipping costs, forced account creation, and easier price comparisons with competitors. Addressing these underlying causes also helps nurture trust, which is crucial because nearly 9 in 10 customers remain loyal to brands they trust.

8 solutions to tackle abandoned carts

Streamline your checkout flow
Display transparent pricing
Offer guest checkout options
Emphasize secure and diverse payment options
Build brand trust and credibility
Combat window shopping with engagement strategies
Run retargeting campaigns
Improve user experience

1. Streamline your checkout flow

The less friction your customers encounter, the better. A streamlined journey paves the way for a smoother conversion process, improves the online shopping experience, and nudges the average cart abandonment rate in a favorable direction.

Optimizing the checkout experience is key to removing friction from the buying process—especially for new customers encountering your brand for the first time.

Simplify user information entry

The last thing online shoppers want is a complicated checkout process.

Minimizing form fields to just the essentials—like shipping address and payment details—saves customers time and helps prevent errors that can lead to frustration. This is one of the most effective ways to reduce form abandonment on any checkout page.

Offering social sign-in options (like logging in via Facebook or Google) is another great way to streamline checkout and eliminate the need for manual data entry. And for shoppers who don’t want to create an account? A simple, straightforward guest checkout option clears the way for a hassle-free purchase.

Introduce progress indicators

A confusing checkout process can leave customers frustrated and more likely to abandon their cart. In fact, according to Baymard’s research, 21% of cart abandonments were due to a checkout process that was “too long/complicated.”

Progress indicators—or progress bars—can help eliminate that frustration and confusion. They clearly show how far shoppers are in the process and what steps remain. They appeal to the idea of “endowed progress”—that people are more likely to complete a task if they are alerted of the progress (real or artificial) towards this task. It’s a simple way to keep them moving toward the finish line.

Optimize for mobile users

Many of today’s shoppers are mobile-first, and if the mobile experience falls short, cart abandonment quickly follows.

Tools like Fullstory give brands a clear view into how users behave on mobile, helping them pinpoint friction and create experiences that convert. Unlike outdated screenshot-based methods, Fullstory uses on-device data capture to keep sensitive information secure while still offering rich behavioral analytics. It’s a smarter, safer way to understand mobile shoppers and improve checkout performance across every device.

→ Learn how to create a five-star app experience with Fullstory's mobile analytics tool.

2. Be transparent about pricing, shipping, and delivery expectations

One of the most off-putting moments for any shopper is the surprise of hidden costs.

Many potential transactions are lost when customers encounter high shipping costs or additional fees. Transparency is key to more conversions. From the outset, presenting a clear breakdown of costs, including taxes and shipping fees, can significantly diminish the shock and subsequent cart abandonment.

You can also build confidence and help prevent drop-offs by offering promotions like free shipping thresholds or limited-time discounts tied to cart totals.

3. Offer guest checkout options

A surprising number of shoppers abandon their carts when they’re forced to create an account. Offering a guest checkout option accelerates the purchase process and respects the privacy of customers who may be reluctant to share personal information.

Streamlining the process to include only the most crucial details enables retailers to cater to a broader audience and reduce cart abandonment rates.

4. Emphasize secure and diverse payment options

Payment should be the easiest part of the customer’s journey. Yet, many cart abandonments are attributed to a lack of diverse payment methods. Providing a range of secure payment solutions accommodates different preferences and sends a message of inclusivity and flexibility.

From digital wallets to traditional credit cards, the goal is to ensure customers can pay how they want, confidently and conveniently.

Highlight preferred payment methods

Offering flexible payment options can make or break a sale. When customers see their preferred payment method during checkout, they’re more likely to follow through. Offering a variety of options—like Apple Pay, Google Pay, credit cards, and even buy now, pay later—personalizes the experience, reduces friction, and helps prevent or reduce cart abandonment.

It’s a strategy that recognizes individuality and caters to it, thereby reducing the likelihood of cart abandonment.

Emphasize payment security

Customers need to know that their payment information is secure when making online purchases—if they don’t, they’ll leave. Security concerns are one of the biggest deal-breakers in ecommerce, so building trust at checkout is critical.

Reinforce your site’s credibility by:

Displaying security badges
Obtaining SSL certificates
Adhering to standards such as PCI DSS
Using secure payment gateways
Requiring credit card security codes

Clear messaging and calls to action during checkout can also reduce hesitation and guide shoppers through a safe, confident purchase. On the backend, businesses should avoid storing sensitive payment details and consider cyber liability insurance to stay protected. Together, these efforts create a safer shopping experience for both your customers and your business.

5. Build brand trust and credibility

Trust is the foundation of any enduring customer relationship, and in the digital marketplace, website credibility is the bedrock of trust.

Strategically placing trust badges, such as security measures or certifications, on key areas of a website—the homepage, product pages, and checkout page—can significantly boost a customer’s confidence in a brand. Likewise, displaying business verification badges like BBB accreditation can solidify a website’s reputation and convey legitimacy.

Some ways to create a safe and credible online space for customers include:

Using SSL certificates to encrypt communication and protect sensitive information
Adhering to PCI DSS standards to demonstrate a commitment to secure online shopping
Harnessing the power of social proof through customer reviews and testimonials to showcase the value of products and services

6. Combat window shopping with engagement strategies

While window shopping might not be a problem in physical stores, it can be a real challenge online. The key is turning casual browsers into buyers by keeping them engaged.

Exit-intent popups offering discounts, free shipping, or limited-time promo codes create urgency and can give hesitant browsers the nudge they need to complete a purchase. Offering recommendations tailored to the shopper’s interests, or free product trials and samples, can also provide that extra nudge towards a purchase decision.

7. Run retargeting campaigns

Retargeting strategies are an easy way to reconnect with shoppers who have abandoned their shopping carts. Email campaigns tailored to the contents of the abandoned cart, complete with eye-catching, high-quality product images and copy, can be very effective.

Research shows that sending a series of two or three emails can enhance engagement and reclaim lost sales, with such campaigns boasting an open rate exceeding 40%.

Retargeting isn’t confined to email alone; dynamic product ads on both web and social platforms can remind cart abandoners of the items they left behind, increasing the likelihood of purchase completion. With the phase-out of third-party cookies, marketers are turning to first-party data and segmentation to tailor their retargeting campaigns more effectively.

Additionally, SMS and MMS retargeting are popular and effective, thanks to their high open rates and real-time delivery.

The key is sending the right message at the right time—whether it's through email, social ads, or text—to remind shoppers what they left behind and why they wanted it in the first place.

8. Improve user experience

A seamless user experience (UX) is one of the best ways to reduce cart abandonment. Fixing common pain points—like clunky navigation, slow load times, or lack of incentives—creates a more enjoyable and efficient shopping journey.

Including product details and images on the checkout page of your ecommerce site can give customers the reassurance they need to complete their purchase. Offering features like a favorites list can prevent decision fatigue and aid in retention.

Analyze user behavior

User behavior offers invaluable insights that can transform the online shopping experience.

Analyzing the conversion funnel allows retailers to distinguish between casual browsers and serious buyers, pinpointing where and why potential customers drop off due to website friction. Session analysis tools like Session Replay allow for a behind-the-scenes look at the customer journey, revealing pain points and areas for improvement that might otherwise go unnoticed.

Behavioral data provides a narrative of user interactions, preferences, and habits that are critical for personalizing marketing campaigns and optimizing the customer journey.

Using customer data, you can:

Customize your strategies to meet the unique needs and expectations of customers
Make website navigation adjustments
Enhance content
Resolve technical glitches that obstruct the user experience

A/B testing for checkout optimization

A/B testing is one of the most effective ways to optimize your checkout flow. By testing different versions of the experience, you can see what resonates with customers and what drives better conversion rates. It’s a smart, data-driven approach that goes beyond guesswork.

To get the most out of A/B testing, start with a clear, data-backed hypothesis—whether it’s based on user behavior, feedback, or trends. These tests make it easy to quickly try out big changes and see how they impact conversion rates and customer satisfaction. The insights you gain help fine-tune the checkout process, making it smoother, more effective, and less likely to result in cart abandonment.

Use AI to personalize and optimize buying experiences

From real-time personalization to predictive analytics, AI helps retailers deliver smarter, more relevant experiences that keep shoppers engaged.

AI-powered tools can:

Recommend products based on behavioral patterns and past interactions
Trigger automated emails or SMS when cart abandonment is likely
Personalize promotions based on user intent and purchase history
Analyze large-scale customer data to identify friction points in the checkout process
Predict when shoppers are likely to return and surface the right offers at the right time

Plus, AI can enhance testing and optimization, helping you not just run A/B tests, but prioritize which changes are likely to have the biggest impact, based on behavioral and segment data.

→ Learn how StoryAI helps brands like yours make smarter decisions and fix checkout flow issues faster.

Turn browsers into buyers

Reducing cart abandonment isn’t just about saving a sale—it’s about building a frictionless, trustworthy shopping experience that keeps both loyal and new customers coming back.

Streamlining the checkout experience, addressing high shipping costs, and offering promotions or discount codes all reduce friction in the buying process. Add mobile-first design, retargeting strategies that deliver the right message at the right time, and the smart use of customer data—and watch more browsers turn into loyal buyers.

If you're evaluating which platform to use for behavioral analysis, our Amplitude alternatives guide breaks down the leading options for ecommerce and CX teams.

Ready to learn how brands like yours use Fullstory’s behavioral insights to identify friction and fix key experience blockers that cost revenue? Download our Retail & Ecommerce Guide to Behavioral Data.

80% of digital experience pros say AI has increased review workload

Fullstory — Wed, 01 Jul 2026 18:00:00 GMT

AI has fundamentally changed how digital experience teams work, with faster execution, more output, and more capability across every stage of generative AI product development.

But that speed has introduced new challenges around time, with 80% of digital experience professionals saying AI has increased the time their team spends reviewing and verifying outputs before they ship, and 51% saying the increase is significant. Without the right behavioral data behind it, a context chasm forms between what AI produces and what teams can actually ship.

This survey of 719 U.S. professionals explores where that gap shows up in AI product development, why behavioral data is the missing input most teams aren't using, and what happens when teams close it.

“AI is changing how fast teams can execute, but when that comes at the cost of increasing review workloads, it's usually a data problem. Without a complete picture of how users experience the product, AI can't make decisions you can actually trust.”
- Claire Fang, CPTO, Fullstory

32% of teams say their biggest obstacle today is reviewing AI work, not building

Verification has quietly become one of the most time-consuming parts of the product workflow. AI has accelerated execution and output volume, but without the behavioral context to make reliable decisions, teams are spending more time checking work than building it. That tradeoff is costing teams the time AI was supposed to give back.

Two years ago, the top constraints looked very different:

Engineering capacity was the #1 bottleneck at 20%
Data access followed closely at 23%

Only 18% say their biggest time shift is more hands-on building. Where teams once struggled with resources and access, they're now struggling to give AI the behavioral context it needs to make decisions they can trust.

The builder's role has always been about understanding users and making good decisions on their behalf. AI accelerates that process, but only when it has the behavioral context to make those decisions reliably. That's what separates teams that are moving faster from teams that are just doing more.
- Lane Greer, Solutions Engineering Lead, Fullstory

The review burden isn't equal: Mid-market teams lead at 56%

Not every organization is feeling the review burden equally. Mid-market companies ($50M to $500M) are feeling it hardest, with 56% saying review time has increased significantly. Compare that to

46% at enterprise companies and
41% at smaller businesses

Mid-market teams are also the least likely to have behavioral data infrastructure in their stack, with only 47% using session recordings or behavioral data as a primary input compared to 52% at enterprise.

For teams carrying the heaviest review burden, adding behavioral data as a primary input is one of the most direct ways to start reducing it.

Without that data foundation, teams that haven't closed the insight-to-action gap between what AI produces and what users actually do are absorbing the cost of that in review time.

Mid-market teams are in a tough spot. They've invested enough in AI to feel where it's falling short, but haven't yet built the data foundation to fix it. At that stage, AI is making decisions without a complete picture of how users actually behave, and the review burden reflects that.
- Addison Price, Head of Global Solutions Engineering, Fullstory

Only 44% of teams feed AI the behavioral data that 83% say is essential

Eighty-three percent of digital experience professionals say behavioral data meaningfully improves AI decision quality, but only 44% are actually using it as a primary input. Without that signal, AI is missing the semantic DOM it needs to navigate your product reliably.

Instead, more familiar data sources are leading the stack:

CRM: 57%
Product analytics: 56%
Customer feedback: 56%

Teams that do use behavioral data as a primary input are more confident (55%) that their AI understands real user behavior than those who don’t (45%). Higher confidence in outputs means less time reviewing them before you ship.

The teams that trust their AI outputs are the ones that have given it the clearest picture of their users. Behavioral data isn't just another input. It's the one that tells AI what actually happens when a real person uses your product.
- Peter Antoniou, Staff Sales Engineer, Fullstory

Case study: Behavioral context drove a 6% lift in AI resolution rates for Ninety.io

Ninety.io is a SaaS platform that helps organizations align teams and manage execution. As their AI support workflows scaled, the AI was answering questions, but conversations felt impersonal. Customers repeated steps they had already taken, and agents had to reconstruct the journey before they could start resolving issues.

To help, Ninety.io used Anywhere: Activation, Fullstory's tool for delivering behavioral session summaries into external platforms. Ninety.io gave Intercom Fin, their AI support agent, a complete picture of each user before the conversation began.

StoryAI, Fullstory's AI agent layer, put that behavioral context to work, enabling Fin to understand, predict, and act on real user behavior rather than starting every interaction from scratch.

We all experience AI fatigue; AI interactions can feel less friendly. They lack the conversational warmth of a human touch. This is something we didn't realize we were missing. Fullstory adds a layer that makes Fin a more personable and friendly experience for our clients.
- Taylor Paletta, Director of Support and Digital Success, Ninety.io

Since adding behavioral context, 79% of tickets exposed to Fullstory data are resolved, and Ninety.io saw a 6% increase in support tickets resolved overall.

Create intelligent digital experiences with Fullstory

Teams that give AI a complete picture of how users actually behave are more confident in their outputs, spend less time reviewing them, and achieve higher digital experience optimization and AI ROI.

Fullcapture automatically indexes every user interaction without manual instrumentation, giving AI the complete behavioral signal it needs from the start.

To see how that connects into the AI workflows your team is already using, start with Fullstory MCP.

Methodology

The survey was conducted by Centiment for Fullstory. The results are based on 719 completed surveys among U.S. professionals working in product management, engineering, UX and design, growth and experimentation, and executive leadership roles. Respondents represented a range of company sizes and AI maturity levels, from early-stage experimentation to fully agentic systems, across industries including retail and ecommerce, financial services, travel and hospitality, SaaS, and tech.

Improving website performance: Speed, function, and user satisfaction

The Fullstory Team — Thu, 19 Dec 2024 06:00:00 GMT

When users visit your website, they expect to find what they are looking for, and they expect to find it fast. These high expectations are nothing new. In fact, site and app load speeds were ranked as the number one cause of consumer frustration back in Fullstory’s January 2023 global survey.

How well your digital experience performs in this area determines your performance score on Fullstory’s Web Experience Scorecard. From load times to responsiveness, Performance shapes every moment of the user journey.

In this article, we’ll examine website performance, the balancing act between speed and functionality, and how different industries score on the Web Experience Scorecard. We’ll also discuss the importance of regular audits and the role of behavioral data in unlocking further optimization. Let’s dive in.

Website performance explained

Performance is the backbone of a seamless online experience. It’s about how quickly your site loads, how smoothly it responds to interactions, and how stable it feels as users engage with it. These factors shape how users perceive your site and determine whether they’ll stay or leave. Even a short delay can impact engagement and conversions. On the other hand, a fast, responsive site builds trust, encourages exploration, and fosters loyalty.

Several factors influence site performance, including:

Visuals: Images and videos enrich your site but need optimization to maintain speed.
Plugins and integrations: These tools enhance functionality but can add complexity and slow performance if overused.
Code structure: Clean, efficient code supports faster load times, while complex designs may introduce trade-offs.
HTTP requests: Each page element, such as images or scripts, adds requests that can impact load speeds.
Caching and content delivery networks (CDNs): Caching and CDNs reduce server load and speed up content delivery.

It’s important to remember that these elements aren’t inherently bad—they’re part of the trade-offs involved in balancing speed and functionality. At its core, performance is about helping users achieve their goals without distraction, whether they’re making a purchase, exploring content, or completing a task. To stand out, aim to deliver an experience that feels effortless, reliable, and tailored to keep your audience engaged.

A balancing act

Trade-offs are part of creating a website that’s both functional and fast. The key is knowing when an addition enhances the experience versus when it slows users down. Striking the right balance means prioritizing user needs while maintaining strong site performance.

Here’s how to do it:

Focus on what matters - Prioritize the features that provide the most value to your users. For example, a retail site might focus on optimizing product images, while an analytics tool emphasizes real-time responsiveness.
Audit regularly - Use tools like Google Lighthouse or Fullstory’s Web Experience Scorecard to monitor performance and refine trade-offs over time.
Map what works - Analyze how users interact with your site to identify where slowdowns matter and where they don’t. For example, delays on a checkout page may hurt engagement, while slightly slower homepage visuals might not.

Balancing speed and functionality is an ongoing process. By focusing on user priorities, auditing regularly, and using data-driven insights, you can create an impactful experience without compromising performance.

Benchmarking website performance across industries

Wondering how your site’s performance measures up? We’ve evaluated 100 brands in each industry—retail, travel & hospitality, financial services, gaming/gambling, and entertainment—to establish performance benchmarks. These averages from our Web Experience Scorecard provide a clear baseline, helping you see how your site compares to others in your field.

Want to know where you rank? Check your website health and uncover actionable steps to enhance your utility.

The role of behavioral data

Your performance score provides a starting point, but behavioral data gives you the full picture. By revealing how users interact with your site, it helps you identify what’s truly impacting their experience and where improvements will make the biggest difference.

Behavioral data shows exactly where slow load times, laggy interactions, or layout shifts are causing frustration. It also helps distinguish between technical slowdowns that users notice and those that don’t affect their experience. By tracking engagement before and after optimizations, you can see what’s working and refine your approach further.

Making the case for behavioral data →

Where to go from here

Performance shapes every user interaction on your site. By understanding its impact, balancing trade-offs, and leveraging behavioral data, you can deliver an experience that keeps users engaged and satisfied. Performance is also one of the highest-leverage levers in CRO, since a faster site removes friction before a single test is ever run.

Get your performance score and more with Fullstory’s Web Experience Scorecard. Already know your score? Request a demo to see how Fullstory can help you optimize.

10 conversion rate optimization best practices to better your bottom line

The Fullstory Team — Fri, 24 Mar 2023 04:00:00 GMT

Conversion rate optimization (CRO) is one of the most important factors when it comes to scaling your business.

As the process of increasing the probability and percentage of users towards a desired action, it’s the foundation of growth.

Improving your conversions would not be possible if you weren’t learning and applying conversion rate optimization best practices.

The aim is to help improve the probability of the visitor taking desired actions on the relevant page. The other word for these so-called desired actions is “conversions”.

What is conversion rate optimization?

Conversion rate optimization (CRO) is the process of optimizing your website experience based on visitor behavior to help improve the probability of conversions.

CRO was created to help you understand what drives, stops, and encourages your users around the underlying reasons behind why people behave a certain way.

10 conversion rate optimization best practices and tips to help you get started

Below, we break down some of the conversion rate optimization best practices that will help you scale.

Always remember, though: there is no one-size-fits-all approach to CRO. What works for one company might not work for another. Consider these best practices as your starting point.

1. Data should guide you

When it comes to conversion rate optimization, the opinions of people don’t matter. Let the data you acquire guide you to your next experiment.

Make use of both qualitative and quantitative data by using a digital experience analytics or product analytics solution.

Understanding users' actions can help identify areas for improvement. An analytics tool like Fullstory’s behavioral data platform lets you make decisions based on facts and figures rather than instincts. In the CRO process, there are multiple ways to obtain data to understand your results.

For instance, you can fetch relevant information like real-time data, bounce rate, website traffic sources, customer segmentation data, site behavior, and much more. And you can go one step beyond that by finding out user frustration signals like dead, rage, or error clicks to unveil immediate fixes.

By using visitor behavior analysis tools such as heatmaps, session replay, user feedback or customer surveys, analytics, and more, you can see how different features are influencing user behavior.

2. Landing page design matters

Landing page optimization is the process of analyzing and editing website pages to improve the user experience and increase conversions.

This involves analyzing behavioral data to see how users interact with the page, A/B testing these elements, improving performance in search engines, and updating content and CTAs. Here are three ways you can improve your landing pages with CRO:

Find the drivers that lead people to your website with analytics
Find the blocks that might stop them with behavioral data
Find the action items that persuade them to act with A/B testing

3. A/B testing is key

As we’ve learned, understanding your users is the key to success and will help you determine how they’re interacting with your site.

Here are two ways you can use A/B testing for CRO:

Conducting tests and interviews: Watching your customers interact with your website in real-time can give you important data and insights. These can include the pages they frequent, the amount of time they spend on your website, the areas where they’re encountering problems, and more.
Reading case studies and following guidelines on user behavior: Many existing pieces of research and case studies can vastly aid in understanding the collective psyche of your customers, which can, in turn, serve as an excellent source for improving your website’s overall look and feel and increase conversions.

Or you could use an analytics tool to suss out the small nuances that larger testing can’t find.

5. Website copy should match business goals

Creatively written and SEO-optimized content can move the needle. But, if it doesn’t match your business goals, it becomes useless. There is no one size fits all approach, and varying on different industries, a different type of conent will be needed. For example, if run a law firm, you should conside a law firm SEO agency to make sure they will understand your business needs and help you create unique content that adds value to your site

Focus on your brand’s USP
Write for skimmers, not readers
Use succinct language
Bullet points and lists are ideal
Write intriguing headlines
Add high-ranking keywords

6. Have a clear CTA

Your call to action (CTA) is a big factor in conversion rate optimization. When you’re setting out to increase conversions, be sure to include evaluating new and optimized calls to action. You can do this by watching user sessions of older CTAs and improving upon them.

Here are some CTA tips you can implement in order to improve your click-through rate:

Be concise: Your CTA should be clear and provide enough detail to motivate the visitor, let them know what you’re offering and how they can access it.
Clickable buttons: Whether you create a 3D design, a motion graphic, or a flat color, shape your CTA in a way that differentiates it from the rest of your content, which encourages clicking.
Align CTA copy with landing page copy: If the landing page content does not match with the CTA copy, the chance of that visitor bouncing is higher. Keep your copy consistent.
Use numbers: Letting your visitors know a high number of people have already downloaded, purchased, or registered for the offer creates a sense of urgency. Numerical evidence is powerful for CRO.
Create location-specific CTAs: Differentiate the various CTAs on your website. They should not look exactly the same, even if it’s for the same offer.
Include a customer testimonial: Credibility is a nice conversion booster. Sometimes a customer quote or built-in referral makes a difference in conversions.

7. Enlist website navigation

In conversion rate optimization (CRO), users are encouraged to take action on a website by clicking on a desired action. Examples include purchasing a product, adding a product to the cart, signing up for a service, or clicking on a link—that all involves website navigation. In order to improve the website navigation, you can enlist these tips:

Your content is relevant to your audience–including the language you use
Your website’s content is engaging by using a variety of formats
Your website is aligned with the customer journey
Your copy is readable and accessible
Your visuals are compelling
Your links and clickable elements are functioning

You can use behavioral data to analyze any of these website navigation elements as well.

8. Keep forms optimized

Your contact form is key for conversion rate optimization and getting helpful information from leads that your sales team can leverage during the buying process.

Experiment with different versions of your contact form, changing things like:

Color
Text
Number of fields
Copy

Additionally, you can experiment with design elements to make your forms stand out:

Emphasize the CTA: Let your CTA button draw visitors’ eyes to your form with bold colors. Studies have shown that red in particular tends to be higher converting.
Don’t match the background: If your website has a lighter or white background, give your contact form some bold color to make it stand out.
Include large, bold font: Give your contact form a slightly larger font than you use for your regular content, placing more emphasis on the desired action.
Add it above the fold: Your contact form should be placed “above the fold,” meaning users shouldn’t have to scroll down on their screen to locate it.

9. Use maps

With the right analytics tools, you can use heatmaps, click maps, scroll maps, and even customer journey maps to understand user behavior and increase your conversions. Each type of map provides a different layer of insight, helping you pinpoint opportunities for improvement across your site and the broader customer experience.

Heatmaps

Heatmaps are a classic way to understand what users are doing on a single page using graphical representations of data in the form of colors, within a matrix. The areas that attract the most attention are generally marked red while others are shaded in green.

Click maps

Similar to heatmaps, click maps provide data about user interaction on a page by way of where they are clicking the most.

Scroll maps

A scroll map is like a heat map and analyzes how a visitor scrolls through your website. It aids in examining their behavior on various website pages and analyzing areas of concern.

Journey maps

Beyond individual page interactions, customer journey maps help you visualize the complete end-to-end experience users have with your brand, from first awareness to conversion and beyond.

By combining behavioral data from tools like heatmaps and click maps with journey mapping, you gain a more holistic view of the funnel—and can make smarter decisions to improve each stage of the customer’s path.

10. Don’t complicate conversion funnels

Keep your conversion funnels clear of complications. There can be other elements added to the beginning or end of this sales funnel, but keeping the path straight for the customer will allow more conversions on your end.

Any given sales funnel should include these basic steps—attention, persuasion, and decision.

Create a compelling offer. What is it about your conversion funnel that will compel people to click through to the next step?

Are you offering them customized pricing?
A free consultation?
A no-obligation analysis?
An invitation to a workshop?
Sending them a free report?
Giving them “instant access” to your product or service?

Without a compelling offer, you can’t expect people to move through your funnel. From there on out, you can create the steps needed to enable them to continue the customer journey.

Further reading: Start learning how to optimize your conversion funnels

Continual CRO is key

All the above-mentioned methods of CRO have become a mainstream effort because it enables companies to understand their customers better.

Also, it exposes a range of data that helps to make future business strategies. Simply put, CRO is not just any tool to enhance your brand’s online performance but it also makes you stand out from other brands.

Through a behavioral data platform like Fullstory, you can uncover ways to improve conversions every day.

Conversion rate optimization for finserv: Why better data beats more tests

Fullstory — Tue, 31 Mar 2026 12:00:00 GMT

Key takeaways

Masking, logins, and multi-step flows in financial services create blind spots that make CRO tests misleading when behavioral data is incomplete—you can't optimize what you can't see.
Behavioral analytics for financial services (funnels, frustration signals, session replay, journey mapping) reveals friction at each step of account opening, loan applications, and claims.
A CRO program built on complete, compliant behavioral data outperforms one built on opinions and incomplete analytics every time.

Financial service providers spend months A/B testing headlines, button colors, and form fields—only to see the average conversion rate barely move. Those tests are running on incomplete behavioral data. That's why they fail.

When a significant share of key interactions in banking, lending, and insurance flows are invisible or misattributed, running more experiments is just organized guessing. For product leaders responsible for digital revenue, conversion rate optimization for financial services starts with a question most CRO guides skip entirely: can you actually see what's happening in your conversion funnel?

Fixing the measurement layer first converts CRO effort into signal.

Why financial services CRO fails without full behavioral visibility

A Fortune 500 financial institution was losing users during online banking enrollment and couldn't explain why. Standard metrics showed where the drop-off occurred, but not what was causing it. Complete behavioral capture revealed the answer: users were failing on security question setup and VerID verification, tripped up by password requirements that were never clearly communicated. The real friction wasn't on the pages they were monitoring—it was in specific interaction steps their analytics had never properly captured. Fixing those friction points increased enrollment conversion by 10.8% and password change conversion by 51.7%.

It plays out the same way across banking, lending, and insurance. What typically breaks:

PII masking fragments events. When SSNs, account numbers, and income data are redacted, standard analytics tools often lose the ability to connect actions before and after those fields. User behavior becomes a series of disconnected fragments.
Iframe-based calculators and third-party ID verification create black holes. Rate calculators, identity verification widgets, and document upload modules often run in separate contexts that Google Analytics and similar tools can't track.
Secure subdomains break session continuity. When website visitors move from your marketing site to a secure portal, login walls fragment the user journey. Pre-login rate shopping and post-login submissions look like two separate people.
Authentication steps go untracked. Multi-factor authentication, KYC flows, and document verification often happen outside your primary analytics, making it impossible to know where potential customers actually abandon.

When your data has gaps this large, A/B testing on headlines produces noise. Page load delays measurably reduce conversions, but that insight means nothing if your analytics can't show you where delays happen inside authenticated flows.

Compliance as a CRO advantage, not a blocker

Product leaders often treat compliance as the reason they can't run experiments. That's backwards. SEC, FINRA, and FDIC-supervised institutions have design constraints that can improve data quality—if you use them correctly.

US financial regulators, including FINRA, the SEC, and FDIC-supervised institutions, require firms to retain records of customer interactions for supervision and examination purposes. This creates a natural foundation for behavioral analytics that logs user interactions without capturing sensitive information. When your platform is Private by Default, you get the visibility you need without creating compliance risk.

Privacy-safe behavioral capture provides four CRO advantages:

On-device redaction protects user data without losing behavioral signals. You can mask account numbers and SSNs while still logging field focus, validation errors, and abandonment patterns on mortgage and credit card applications.
Role-based access controls satisfy governance requirements. Only appropriate staff see sensitive paths in session replay, while analysts can still identify friction patterns in aggregate.
No PII logging simplifies data retention. When you're not storing sensitive information in the first place, retention policies become straightforward.
Audit trails accelerate legal approval. When legal and risk teams can trace exactly what changed and how it affected customer outcomes, they approve A/B tests faster.

Financial services teams use platforms like Fullstory as part of their evidence trail for model validation, UX changes, and complaint investigations. Transparent behavioral analytics reduces perceived risk because every test has traceable scope and auditable evidence.

Start CRO at the measurement layer, not the experiment

Most CRO playbooks start with a list of A/B testing ideas: test this headline, try that button color, reduce form fields. For financial services, this approach fails before it starts.

Fix your measurement layer before running experiments. If key interactions in masked fields, authentication steps, or secure subdomains aren't captured, your tests aren't measuring what you think they're measuring.

Run a three-part check before launching a CRO program:

Logging check:

Map every step in a core flow: online savings account opening, auto loan application, credit card sign-up.
Confirm behavioral events exist for each screen, each error state, and each click.
Verify that validation errors, timeout messages, and retry attempts are captured, not just successful completions.
Check that events fire correctly on both web and mobile web views.

Identity check:

Determine how you connect pre-login actions (rate shopping, calculator use) with post-login outcomes (application submission, funding).
Use privacy-safe identifiers that don't require storing PII.
Confirm that a single user journey isn't being counted as multiple separate visitors when they cross login boundaries.

Environment check:

Validate behavior capture across web, mobile web, and native apps.
Many US customers start on mobile and complete on desktop. Confirm your data reflects this cross-device reality.
Test that mobile users see the same behavioral capture quality as desktop users.

Mobile optimization isn't optional. In multi-device financial journeys, if your mobile experience isn't fully instrumented, you're missing where many conversion events actually occur.

Behavioral analytics foundations for financial services CRO

A finance CRO program needs a behavioral data platform, not just a web analytics tool, to understand the why behind conversion changes. Standard analytics tells you 40% of users dropped off at step three. Behavioral analytics shows you they rage-clicked on a broken tooltip, hit validation errors twice, and gave up after an unclear error message.

Track both macro conversions (funded accounts, submitted applications) and micro conversions (rate check clicks, document upload starts, e-signature views) to see the full funnel.

Core behavioral signals to instrument for banking, lending, and insurance flows:

Funnels: step-by-step completion rates for account opening, loan applications, claims submissions.
Rage clicks: users clicking five or more times on a non-responsive element, a reliable frustration indicator.
Dead clicks: clicks on non-interactive elements users expected to work.
Error rates: how often validation errors, timeout errors, and system errors appear.
Session replay: qualitative context for understanding why quantitative patterns occur.
Journey length: how many steps and how much time users spend before converting or abandoning.

In practice:

Where users stall in a 5-step auto loan application when income verification fails.
How many claimants retry document uploads before dropping in a claims portal.
Which mobile sign-up screens generate the highest error-message views.

Behavioral analytics for financial services combines these signals automatically through Fullcapture, so teams can ask new questions of past data without re-tagging events. This is the difference between knowing what happened and understanding why.

Mapping high-value financial journeys with behavioral data

Start with three or four concrete journeys and map the micro conversions that matter at each step.

Checking account sign-up:

Micro conversions: "Get started" click, email verification, identity document upload, funding source connection, initial deposit.
Behavioral signals to track: time between steps, retry rates on document uploads, abandonment at funding.

Credit card application:

Micro conversions: pre-qualification check, income entry, credit disclosure view, e-signature, approval confirmation.
Behavioral signals to track: validation errors on income fields, rage clicks on disclosure sections, form submissions vs. starts.

Mortgage pre-approval: Track the rate check, property type selection, income documentation upload, and credit pull authorization. Watch for dead clicks on rate tables, how far users scroll into disclosure pages, and time spent on document requirements before abandoning.

Insurance quote-to-bind: The key moments are quote request, coverage selection, and payment entry. Retry loops in payment processing and comparison clicks between coverage levels often reveal where confidence breaks down.

TBC Bank, Georgia's largest bank with over 550,000 daily digital users, used behavioral analytics to discover their mobile onboarding required 33 steps and averaged 8 minutes. Dead clicks and rage clicks across the flow identified 24 steps that added no value and that standard funnel data had never flagged as a problem. Eliminating those steps cut onboarding time to 90 seconds and lifted conversion 145%.

Think of the journey from ad click to funded account: ad click → landing page → calculator → login → application submit → funding. Data often goes dark somewhere after login. Behavioral analytics exposes those hidden abandons.

Using session replay as one signal among many

Session replay helps teams see real user struggles in context. CRO decisions need aggregated patterns, not individual sessions.

The disciplined approach:

Start with quantitative signals. Identify a cluster of rage clicks on your "Check my rate" button. See that 15% of users are clicking it multiple times before proceeding.
Use replay for context. Watch 5–10 relevant sessions to understand why. Maybe there's a confusing tooltip. Maybe the button state doesn't change to indicate loading. Maybe users expect a different outcome.
Form hypotheses based on patterns. Don't redesign based on a single frustrating session. Look for recurring issues across multiple replays.

In regulated environments, replay must respect field masking and role-based access. Only appropriate staff should see sensitive paths. Both compliance requirements and good data hygiene call for this discipline.

Designing trustworthy experiments in regulated financial journeys

US financial institutions must treat experiments as controlled changes with clear documentation, not casual "try this new hero image" tests. The stakes are higher, and so are the requirements.

Frame hypotheses around behavior, not aesthetics:

"Fewer validation errors on step three will increase completed credit card applications" is testable and behavioral.
"A blue button will convert better than green" is superficial and disconnected from user behavior.

Guardrail metrics beyond conversion rate:

Application quality: Are completed applications being approved at the same rate?
Fraud flags: Is the change inadvertently making fraud easier?
Call-center contacts: Are confused users calling instead of completing online?
Complaint volume: Is the change creating unexpected user frustration?

Example test: Moving required disclosures from a dense block at the bottom of the page to an expandable module near the rate table. Track both completion rate and time-on-step.

Maintaining a behavioral audit trail of which users were exposed to each variant, and how their behavior shifted, speeds compliance sign-off and post-test analysis. When everyone can see the same data, approvals happen faster.

Working with legal, risk, and compliance stakeholders

Bring legal and compliance teams into CRO planning early. Don't surprise them with experiments after they've launched.

Collaboration tactics that work:

Create a pre-approved experiment library. Document safe changes (wording updates to help text, error message improvements, input order adjustments) that don't require new legal review each time.
Share example dashboards. Show legal and risk teams what behavioral data looks like, what you're tracking, and what you're not tracking. Transparency reduces perceived risk.
Align with internal model risk management. If your experiments affect outcomes that feed into credit models or risk scoring, make sure those teams understand the scope.
Document everything. Every test should have traceable scope, expected impact, and audit-ready evidence.

When every CRO experiment has clear scope and auditable results, compliance becomes a partner.

Prioritizing CRO opportunities by business impact, not opinions

Opinions drive most prioritization conversations. Data makes them shorter. Rank issues by three factors:

Factor	Question	Example
Volume	How many users hit this friction point?	10,000 monthly visitors reach step 3
Severity	How frustrated are users?	25% show rage clicks or multiple retries
Revenue impact	What's the value of fixing it?	Each completion = $1,200 average deposit

If 2% more visitors completed the online savings application, with an average initial deposit of $1,200, you can calculate the incremental annual deposit volume. That's customer lifetime value impact you can take to leadership.

Fixing conversion leaks in existing traffic is usually cheaper than buying new visitors to send into the same broken flows. Customer lifetime value and customer acquisition cost frame these decisions in terms finance leadership understands.

StoryAI Opportunities in Fullstory can surface and rank these issues automatically, turning scattered friction points into an ordered backlog.

From insights to shipping: closing the optimization loop

The gap between behavioral insight and shipped improvement kills CRO programs in regulated environments. Close it with this sequence:

Summarize the insight clearly. "40% of users hit a validation error on income entry in the auto loan flow. Rage click density is 3x higher on this step than any other."
Attach relevant replays. Include 3–5 sessions that illustrate the pattern, respecting masking and access controls.
Align on change scope. Be specific about what you're changing and what you're not. "We're updating the error message copy and adding inline validation. We're not changing the income requirements."
Define expected behavioral shifts. "We expect validation error rates to drop by 50% and step completion to increase by 8%."
Track over a full cycle. In credit and lending journeys, track before-and-after behavior for at least one billing period or statement cycle to see downstream effects on key performance indicators.

Activation of behavioral context into other tools (marketing automation, feature flags, support platforms) helps orchestrate and measure these changes. This makes CRO an ongoing product practice, not a side project with orphaned results.

AI and behavioral data: making financial CRO adaptive

AI on top of complete behavioral data can automatically surface where financial customers struggle, without teams watching hours of replays or combing through spreadsheets.

Examples of questions AI agents can answer for a product leader:

"Where are authenticated customers failing to set up external transfers?"
"Which error messages correlate with call-center spikes?"
"What percentage of users who abandon at income verification eventually complete via phone?"

Trustworthy AI for financial CRO depends on high-quality behavioral data—poor capture leads to hallucinated insights and misguided tests. If your data has gaps, your AI will confidently give you wrong answers.

Behavioral analytics for financial services as AI fuel

Behavioral analytics gives AI agents "digital sight" into real customer journeys across channels.

Practical applications:

In-app guides triggered by behavior. When users stall on income documentation or KYC steps, behavioral signals can trigger contextual help without waiting for them to abandon.
Real-time streaming to downstream systems. When behavioral context flows to servicing bots and personalization engines, they can react before customers abandon.
Adaptive onboarding. Over time, account onboarding flows can simplify themselves based on behavioral patterns, reducing friction for users who don't need every step.

Financial institutions that build behavioral data foundations now will be better positioned to use AI as the technology matures.

Conclusion: build CRO on complete, compliant behavioral data

The biggest CRO risk in financial services is not bad copy or button color—it is missing or partial data created by masking, logins, and complex flows. When key parts of your conversion flow are invisible, you can't fix them.

Compliance and CRO reinforce each other when teams build privacy-safe behavioral analytics from the start. The sequence: fix measurement, map journeys, prioritize by impact, run disciplined tests, use AI to scale insight.

Fullstory is an Intelligent Digital Experience Platform trusted by financial institutions to capture, analyze, and act on behavioral data in regulated environments.

Book a demo to see how behavioral analytics for financial services works in practice.

A guide to the most important CRO metrics

The Fullstory Team — Fri, 21 Jul 2023 04:00:00 GMT

What does CRO mean in analytics?

Conversion Rate Optimization, commonly referred to as CRO, is a crucial component of digital marketing and online business strategies.

CRO involves leveraging analytics data and user feedback to improve your website's performance. The ultimate goal of CRO is to improve the percentage of visitors to a website that converts into customers, or take any desired action on a webpage.

The beauty of conversion rate optimization lies in its emphasis on making the most out of the traffic you already have. Instead of investing heavily in attracting new visitors, you focus on optimizing the experience of your existing audience, ensuring they find what they're looking for, which ultimately increases the likelihood of them making purchases.

Implementing a CRO strategy in your business means that you're taking a data-driven approach to decision-making.

It's all about conducting data-driven experimentation to determine what resonates best with your audience. CRO metrics are key performance indicators (KPIs) that can help businesses understand how their website is performing and what potential issues might be hindering their conversion rate.

Businesses across various industries often use tools like Fullstory to carry out their CRO activities. Fullstory is a digital experience platform that gives businesses insights into their customer's experience, helping them create a better user experience. This is achieved through advanced analytics that offers detailed views of customer interactions, thus enabling businesses to make the necessary changes and improvements. Semrush AI SEO Toolkit can complement these efforts by measuring brand presence and sentiment in AI-driven search results, revealing how potential customers encounter a business across large language models. It benchmarks competitors, uncovers unmet user needs, and maps topic associations to guide more effective content and messaging strategies. By integrating these insights, marketers can align CRO experiments with broader visibility goals, ensuring optimizations resonate both on-site and in AI-powered discovery channels.

By examining these important metrics, businesses can find out where and why users may be facing difficulties and then make the necessary improvements to enhance the user experience. This makes CRO a valuable tool for businesses aiming to increase their ROI, enhance customer satisfaction, and ultimately boost revenue growth.

Benefits of tracking CRO metrics

There are numerous benefits to tracking CRO metrics in your business strategy. These benefits not only add value to your website but can also translate into tangible improvements in your business operations.

Improved user experience

CRO metrics offer insights into how customers interact with your website, helping you enhance the user experience. For example, if you have a high cart abandonment rate, it may mean your checkout process is complicated. Similarly, high exit rates from a specific page could indicate issues with content quality.

In banking and lending, the same logic applies, except a large share of those drop-offs happen inside authenticated flows that standard metrics never capture. Conversion rate optimization for financial services starts with fixing that blind spot.

By identifying and addressing these issues, you can create a more satisfying user experience, keeping visitors on your site longer and encouraging them to return.

Higher conversion rates

By focusing on the key metrics that drive conversions, such as the click-through rate and average time on the page, you can make data-driven changes that lead to higher conversion rates.

It's important to remember that even minor improvements in your conversion rate can have a significant impact on your revenue.

Reduced bounce rates

Bounce rate is a crucial CRO metric that measures the percentage of visitors who navigate away from your site after viewing only one page. A high bounce rate might indicate that your site is not meeting user expectations, potentially due to poor content quality or a confusing site layout. By identifying and fixing these issues, you can reduce your bounce rate and increase user engagement.

Read more: How to reduce your bounce rate

Cost-effective marketing strategy

By tracking CRO metrics, you can figure out what works and what doesn't on your website. This helps you optimize your marketing budget by focusing on strategies that have the highest impact on your conversion rate.

Increased revenue

As you improve your website performance based on CRO metrics and increase your conversion rates, you'll naturally generate more revenue. It's important to remember that CRO is not just about driving more traffic to your site, but ensuring that the traffic you receive leads to actual conversions and sales.

Better insights into customer behavior

CRO metrics offer valuable data about customer behavior on your site, including the devices they use, where they come from, and which pages they visit. This information is extremely useful for customizing your site and marketing strategies to fit your customers' preferences and needs.

Enhanced competitive advantage

By understanding and implementing CRO, you gain an advantage over competitors who ignore these insights. An optimized site designed for conversions is more appealing and user-friendly, resulting in increased customer satisfaction and loyalty.

Enhanced marketing team performance

Tracking CRO metrics aids your marketing team in working smarter and more efficiently. By knowing which efforts lead to conversions and which don't, they can allocate their time and resources more effectively.

Key CRO metrics: The foundation of conversion rate optimization

Conversion rate

The conversion rate is an important CRO metric that shows the percentage of visitors who take a desired action on your site, like making a purchase or filling out a form. By monitoring this rate, you can assess how effective your site and marketing efforts are. It helps you identify areas that need improvement for a better user experience and, ultimately, more conversions.

Conversion rate calculation: Total conversions / Total visitors

Bounce rate

The bounce rate is the percentage of visitors who leave your website after viewing just one page. A high bounce rate suggests issues with your site's content, design, or usability. To optimize your site, it's important to lower this rate and encourage visitors to explore more pages. This increases the likelihood of conversions.

Bounce rate calculation: Number of one-page visits / total number of visits

Average time on page

The average time on page shows how long visitors typically stay on a specific page of your website. This metric reveals the relevance and quality of your content. When users spend more time on a page, it indicates they find the information valuable, which boosts the chances of conversion.

Average time on page calculation: Total time on page / (Total page views - total exits)

Pages per session

Pages per session indicates the number of pages a visitor views during one visit to your website. A higher number suggests greater engagement, meaning visitors find your content interesting and valuable enough to explore multiple pages on your site.

Pages per session calculation: Number of page views / Number of sessions

Exit rate

The exit rate is the percentage of desktop, tablet, or mobile users who leave your site from a specific page, after visiting at least one other page. It suggests that something on that page may not be meeting user expectations or causing frustration, which could affect your conversion rate.

Exit rate calculation: Total page exits / total page visits

Customer lifetime value (CLV)

CLV is a prediction of the net profit associated with the entire future relationship with a customer. By understanding this metric, businesses can determine how much they can afford to spend on customer acquisition and retention, helping optimize marketing strategies to increase profitability.

CLV calculation: (Average revenue per account * gross margin) / Churn rate. In other words, customer value * average customer lifespan.

Customer acquisition cost (CAC)

CAC represents the average cost of acquiring a new customer, taking into account all of your marketing and sales expenses. A low CAC means you're efficiently converting prospects into customers, whereas a high CAC could indicate the need to revise your marketing strategy to maximize ROI.

Customer acquisition cost calculation: (Total marketing expenses + total sales expenses) / Total new customers acquired

Funnel conversion rate

The funnel conversion rate provides insight into how well you're moving users through the sales and marketing funnel. This includes steps like moving a visitor from an initial site visit to a product page, adding an item to the cart, and finally, completing the checkout process. A high funnel conversion rate indicates an effective, seamless buyer journey.

Funnel conversion rate: Number of conversions / Number of leads

Average order value (AOV)

AOV is the average amount a customer spends during a single purchase. Increasing AOV is a strategic way to boost revenue without necessarily attracting more customers. Tactics to raise AOV could include upselling, bundling products, or offering free shipping on orders over a certain amount.

Average order value calculation: Total revenue / Number of orders placed

Cart abandonment rate

The cart abandonment rate is the percentage of users who add items to their shopping cart but leave before completing the purchase. A high cart abandonment rate is a concern as it represents lost potential revenue. Strategies to decrease this rate might include streamlining the checkout process or offering various payment options.

Cart abandonment rate calculation: Number of completed purchases / Carts created

Click-through rate (CTR)

CTR measures how often people click on a link or call to action in your content or ads. A high CTR generally indicates compelling content or ads that effectively encourage users to learn more or make a purchase. Constantly optimizing for a higher CTR is key to driving more traffic and achieving higher conversion rates.

Click-through rate calculation: (Clicks / impressions) * 100

Traffic Source

Traffic source identifies where your website visitors are coming from, whether that's organic search, paid ads, social media, or referrals from other sites. Knowing your traffic sources can help you tailor your content and marketing strategies to the channels that are driving the most and highest-quality traffic.

Cost per conversion (CPC)

CPC is a metric that helps determine the cost-effectiveness of your marketing efforts by showing how much you're spending for each conversion. By understanding this metric, you can optimize your marketing spend, focusing more on the channels and strategies that result in the most cost-effective conversions.

Cost per conversion calculation = Total spend / Total conversions

Page load time

Page load time is a critical metric for both SEO and user experience. Slow-loading pages can lead to high bounce rates and low conversions as users tend to leave websites that take too long to load. Therefore, continually working to improve your site's load time can significantly enhance user experience and boost conversions.

The definitive guide to session replay

Fullstory — Tue, 08 Oct 2024 07:00:00 GMT

All web analytics tools exist to solve a fundamental problem: provide actionable, reliable, and timely insights about the user. Understanding the real-life person engaging online, on the other end of the screen, means asking the questions:

What's the customer trying to do?
What is their experience really like?
What works? What breaks?
When, where, and why do users get frustrated?

Traditional analytics tools answer these questions by turning users into numbers: clicks, bounce rates, page visits, conversions, and other abstractions. While this data feels insightful on its own, traditional web analytics miss the forest for the trees.

That's where a session replay tool comes in.

Session replay capabilities elevate traditional web analytics tools by showing a complete picture of the user on a website or app. They provide all the individual data points you get with a typical analytics tool and how those data points combine to create an experience. In other words, session replay helps you definitively answer the question, "What's really happening on my site (or in my application)?"

The result? Teams across your organization—product managers, support agents, product and UX designers, developers, marketers, sellers, and everyone in between—can turn to session replay to better understand the user, learn from their struggles and successes, and build better products. From SaaS and ecommerce to fintech, where session replay doubles as a powerful fintech analytics tool, and even hospitality, session replay is valuable for online businesses no matter the industry.

From SaaS and ecommerce to fintech, where session replay is a critical tool for fintech analytics, and hospitality, session replay is valuable for online businesses no matter the industry.

With session replay you can finally get the answers you need to understand your customers and improve their online experience.

What is session replay?

Session replay is the reproduction of a user’s interactions on a website or web application exactly how the user actually experienced it. Session replay transforms logged user events, such as mouse movements, clicks, page visits, scrolling, tapping, etc., into a reproduction of what the user actually did on the site or app.

For a better idea, watch the reproduced session of a fictional user interacting with the Fullstory.com:

Session replay–which has been around for more than a decade–might also be referred to as session playback, session recording, user experience (UX) replay, or even mouse tracking, among other names.

Session replay is a powerful form of qualitative research, enabling researchers to see and learn from the behaviors of individual users, and combine qualitative and quantitative data.

From a research perspective, session replay deftly avoids bias in user data or A/B testing—i.e. the Hawthorne Effect—because sessions are captured from actual users rather than test subjects in a study.

Traditional qualitative research methods typically require advance setup, can be challenging to execute, and are often prohibitively expensive. With a replay tool, on the other hand, sessions are available on-demand and can be used on any aspect of your online customer experience.

What session replay isn’t

Most session replay tools don’t rely on actual video recordings of the end user’s screen. Rather, they reconstruct events as they occurred.

Imagine someone took detailed notes about your day—where you went and when, what it looked like, what you did, how you acted, etc. Then, they took those notes, rebuilt the scenes and hired actors to play the parts, using the notes like a script to recreate the day just like it actually occurred.

That difference, while subtle, is important because unlike video, session replay is immensely rich in meaningful behavioral data—data that platforms with powerful session replay can use to surface valuable insights about user behavior and interaction with a website or application.

Though the two are sometimes lumped together, heatmaps are not session replay, but are instead a complement to session replay. Often, it's smart to combine session replay and the many types of heatmaps — click maps, scroll maps, and more — in your analyses.

How session replay works

When you first watch a session replay, it can be a surreal experience—how did you create a video of a user's online experience?

Realizing that it's not video, but a high-fidelity reproduction of a person's experience adds to the mystery—how is this possible?

It's complicated, so let's dive into the technical specifics of session replay.

Session Replay and the DOM

You can’t talk about session replay without talking about the DOM, or Document Object Model, which is how users experience a product and interact with the web.

Replay captures everything that occurs in the DOM. We'll avoid going into a detailed explanation of the DOM, but having a working analogy of what it is will help you understand how session replay works.

Imagine your website or app is a building. The DOM is the physical structure of the building—the foundation, the plywood frame, the doors and windows, and so on. HTML, CSS, and JavaScript control how the house is constructed, how it looks, and how it behaves.

By recording the DOM and all mutations to the DOM that occur throughout a user's visit to this "building," a session replay tool can accurately record and recreate not only the building itself, but all the times a user walked through a doorway, moved a piece of furniture, hung a painting on the wall, or even did some remodeling.

Our physical building analogy is similar to what session replay tools accomplish virtually. Session recordings provide the necessary bits and pieces you need to replay how users experienced your web site or app for the explicit purpose of understanding that experience—to learn from it and improve it for future users.

Rounding out our building metaphor, in session replay, the building materials as well as the activities that occur within the building can be classified into two categories: events and assets.

Events: Every action that takes place on the web requires the transfer of information. While the experience is seamless to the user, when that user does anything–like moves the mouse or swipes the screen–information is created. Each of those information transfers is a tiny events.

App session replay is a reconstruction of all of those events from an extremely detailed log.

Assets: In addition to the events being logged, the “scene” within which the events took place must also be captured. That’s why session replay tools also record the associated web assets—that is, all the bits and pieces (like images, HTML, CSS, etc.) that are used to build what you see on a web site or app. Saving the assets is important because session replay tools that do not cache assets are unable to reproduce older sessions if and when the web page or app changes.

What can session replay log?

Websites are complex, and there’s an infinite combination of ways that they can be built. Many web pages and apps invoke JavaScript or AJAX, are responsive in design, and dynamic by default. What about mobile? What about variations in screen resolution, browser window size, etc.?

Web apps might have windows that pop-up, menus that explode out (and disappear), and any number of other complicating factors. That’s why, for mobile session replay tools to be useful, they need to be capable of dealing with all of this complexity.

Below is a partial list of all the things that can be logged in session replay—note that not all session replay vendors record all of these things.

HTML and CSS (And all the most basic web page and app elements)
CSS animations
Vector graphics in SVG (including animations)
Hover cursors and effects
Embedded <iframe>
Script-modified input values
Window resizing
AJAX URL navigation
HTML5 <audio> and <video> playback
Multi-touch events (on mobile devices)
Web Components, and by extension, Shadow DOM

One of the compelling aspects of session replay is that it captures all the nuance experienced by users—nuances of your web site or app and nuances in user devices, browsers, window sizes, etc. Comprehensive session replay tools should also work across mobile applications.

What about session replay and privacy?

Session replay tools, like other behavior analytics tools, require disclosure to users through language in your privacy policy, in-app notifications, etc. If you use other analytics or customer experience services (like Google Analytics, Mixpanel, Contentsquare, Segment, Omniture, Intercom, Optimizely, and hundreds more), you’ve very likely already placed such a disclosure on your site or in your application. The techniques used by these popular platforms to collect data are fundamentally no different than those used by session replay tools.

Additionally, any app session replay tool should allow sensitive data (e.g., passwords) to be excluded from collection and user inputs to be masked from view. Each session replay tool approaches privacy differently, so it’s important to choose a tool that provides your company with strong privacy functionality and implementation options.

What is session replay useful for?

Now that you understand that session replay is a video-like reproduction of the customer experience online, you might be wondering, "Okay, what can you do with that?" The answer is: a lot.

Session replay is the firehose of information about the online experience. As you get comfortable with navigating the massive amount of information captured by a session replay tool, you will discover new ways to mine your sessions for insights—things that you never could have learned with a traditional web analytics platform alone.

So whether you're conducting a routine website health check or diving deep into user behavior patterns, session replay provides the granular data needed to make informed decisions about your digital experience.

If you're new to session replay tools, the following use-cases will help you get familiar with what's possible with replay.

5 problems solved by session replay

Below are 5 common problems solved by session replay tools. For the first four—Reproduce and solve bugs faster, Conversion rate optimization (CRO), Support customers faster through context, and Understand the user experience (UX)—you'll find detailed explanations of the problem along with supporting stories about how session replay was used to solve the problem.

1. Reproduce and solve bugs faster

A nearly unlimited number of things can go wrong with products that live online. Variations in devices, OS, browsers, screen sizes and resolutions, all introduce elements that can make QA and software testing tedious, error prone, and inevitably limited in scope.

When things break and bugs are reported, chances are the problem either won't be reproducible at all, or reproduction will require hours of your dev team's precious time.

Common problems engineers and product developers encounter with session replay:

I need to reproduce bugs in order to understand them, but bug reports don’t give me enough information to deduce what’s going on.
I can’t figure out when my app/site is having errors.
I need to know who's been affected by a bug.
I need a way to prioritize bugs by impact.

Session replay is a game-changer for reproducing and solving bugs. By linking user sessions to bug reporting tools (like Fullstory integrations JIRA or BugSnag) and to customer support tickets (See #3 Support customers through context), session replay ensures you’ll never again need to request more information about a user issue or bumble through tedious screenshares/screenshots.

With session replay tools that capture all sessions, QA and software testing don’t stop at launch. Indeed, session replay can be used alongside product launches, allowing product developers to observe as users engage with a new feature or interface to make sure it's working correctly. In the likely event that something is broken, session replay can speed up the fix by revealing what’s causing the problem.

Depending on the capabilities of the tool, replay may also include info like stack traces, Network views, .HAR, UserAgent info, and more.

Below are examples of how session replay was used to reproduce and solve bugs faster, saving developers' time and companies' resources:

Moosejaw — retail/ecommerce — time saved solving bugs with replay has enabled them to go beyond reactive bug-fixing to proactive bug-finding.
Fortive–industrial technology–Fortive has over 20 brands in its portfolio, and each uses Fullstory to better understand why customers choose not to buy and to run smarter digital experiments.
Sendoso–SaaS, shipping platform–personalizes customer interactions, conserves engineering time, and ideates new features.
Younique–peer-to-peer marketing—increases conversion rates, reduces time-to-checkout, and smoothes internal communication using Fullstory.

Engineers’ time is expensive, which makes tools that speed up bug reproduction extremely valuable. Session replay is a powerful analytics tool that can dramatically reduce the time it takes to understand and eliminate software glitches, coding errors, and other pesky bugs on websites and web applications.

2. Conversion rate optimization

Whether in ecommerce, marketing, or product development, optimizing conversions for your website or app is an ongoing, never-ending process.

Here are just a few problems optimizers face:

I need to understand what is preventing users from converting (or triggering some KPI).
What’s not working about my landing page?
Why are users missing my CTA?
Where are my customers losing interest?

Session replay tools help conversion rate optimization by showing what is happening on a given page or screen on your website or app. This level of detail helps you tease out why users are—or aren’t—converting.

For example, are users getting confused with your UI? Are they missing the CTA? Running into errors? Looking for missing information? And on and on. From these observations, hypotheses can be formed and tests run. Mix session replay with A/B tests and you can get more out of experiments and further optimize experiences.

How to use session replay for conversion rate optimization

Some session replay vendors also bring together search, replay, and funnel analytics so you can conduct aggregated analysis and qualitatively analyze real user behavior at each step of the funnel.

Below are examples of how session replay was used to drive conversion rate optimization (CRO):

SpanishDict — translation service — how a banner ad was pushing a core feature of the product off the page on mobile.
Zenstores — ecommerce platform — uses session replay to optimize the funnel.

3. Support customers faster through context

Support professionals have an exceptionally challenging job. Helping customers who run into problems during their online experience can be an exercise in futility. Here are a few of the problems support professionals face:

How do I get the context necessary to understand the customer's problem or need?
"Could you provide a screenshot?"
How do I answer a ticket that says, "It's not working" quickly and effectively—without further frustrating the customer?

Additionally, some session replay tools can be used to “go live” through low-latency streaming, enabling co-browsing with users (typically a 1–5 second delay) so support agents can help in real time navigate through site or UI problems.

4. Understand the user experience (UX)

It's almost impossible to understand how digital products are used without directly observing users. But designers, developers, and other stakeholders in the user experience must make these observations in order to improve the product. Here are a few of the problems faced and questions asked when trying to understand UX:

Is my design working? Does it make sense to users?
How do I understand the UX across the thousands of devices, browsers, screen resolutions, OS, etc.?
UX research studies are costly, slow, and unreliable—but what other choice is there?

Session replay is a useful tool for gathering user experience research on-demand and “at scale” to help designers see where elements are not understood or how different devices, screen resolutions, and browsers are presenting an experience to users.

Below are examples of how session replay is used to help UX designers understand website visitor use their site:

Jumbo Interactive–ecomm and information technology–saves $10,000s by identifying critical bug fixes using Fullstory.
Aweber — email marketing — understanding how users find the mobile app experience lacks certain desired features that exist on desktop.
Tray — cloud services connector — discovers how users are confused in creating connections resulting in creation of better discoverability around deleting connections.

5. Understand and improve onboarding

Another way session replay can be used to understand UX is by watching sessions of users who go through onboarding flows to understand where users miss things, lose interest, or fail to understand functionality.

Below is an example of how users understand onboarding using session replay:

Wistia — online video platform — improved onboarding resulting in a saw a 30% increase in their customization engagement metric.

How to find the right session replay vendor

Many session replay analysis tool options exist. And due to the technical requirements "under the hood," not all playback tools are the same.

If you're tasked with finding the best replay tool for your organization, arm yourself with the research to evaluate and find a replay analysis tool that meets your needs. To that end, below you'll find a list of the top session replay vendors/tools, the key criteria to consider in reviewing a given tool, and how Fullstory session replay meets or exceeds these criteria.

10 criteria for evaluating session replay vendors

Below are 10 criteria to consider when evaluating any given session replay tool. Each of these considerations should be weighted against a given tool's technical capabilities and features.

1. Search, segmenting, or filtering sessions

Now that you have thousands of user sessions, how do you find specific sessions?

Finding the specific session or set of user sessions that meet given criteria is necessary to get to the “why?” session replay provides. Not all vendors allow you to search or filter and segment sessions by events.

2. Language and code capabilities

Session replay tools record what happens in the DOM. Different session replay tools can capture more (or less) than others as web and app elements vary in their complexity. If your site has a particularly nuanced or complicated setup, it’s worth checking to make sure the session replay tool you’re researching will work.

3. Single-page apps

“Will session replay work with my REACT site? What about Angular.js? Does session replay work with [insert JavaScript framework here]? Are there any caveats? Does it work out of the box or is someone going to have to code things into my page or onto the vendor's backend?"

4. Performance

Session replay tools necessarily use proprietary code to capture data from your site or app. The technology and implementation can affect the speed of your site.

5. Asset caching

Does the session replay tool cache assets to ensure playback fidelity in the event the site changes?

Not all session replay tools save the associated assets (e.g. images, CSS, HTML, and many other materials used to build web pages and apps). For session replay tools that do not cache assets, in the event the web site and app changes, session recordings are likely to lose their utility.

6. Data retention

Session replay vendors must store lots of data, so how many days, weeks, or months of sessions that are retained by a given tool will vary.

7. Sessions recorded (sampling vs. everything)

Some tools only capture a sampling of sessions while others—like Fullstory—capture them all. Sampling limits the ability to dive into customer support tickets by user session (or co-browse), assign sessions to bug reports, generate aggregated insights, gather insights on edge-cases, as well as other limitations.

8. Ability to identify specific sessions by user

Related to differences in sampling vs. capturing all sessions, some session replay tools allow you to identify sessions by users.

9. Implementation

Depending on the tool, implementing the necessary script and/or code to make session replay work on your site can vary in complexity. Some tools take months of implementation or nuanced installation requirements.

10. Price

Session replay vendors can vary in price depending on variables like data retention, sessions covered, additional features, and more.

Job functions that benefit from session replay

If your work touches the customer experience online, session replay is for you. Some common job roles that use Fullstory include product managers, product developers, engineers, designers, UX researchers, customer support, and marketing teams.

Product management, development, and engineering

Session replay is a useful tool for product managers, product developers, and engineers. As explained above, session replay works as a debugging tool as bug reproduction is as simple as finding sessions that contain errors and watching the playback. Meanwhile, if your session replay tool has the capacity to search sessions for events, sessions containing errors can be found through search.

Additionally, when integrated with customer support tools, bug reports from users can be attached to session recordings, eliminating the need to reproduce the bug through trial and error, and saving engineers the time spent decoding user-written bug reports.

Design and user experience (UX) research

Designers understand that even the most elegant, thoughtful site or app design is likely to have unexpected user behaviors once launched. Variations in devices, browsers, and the page and app state can affect design elements. Users can be confused by elements that the design team thought were obvious or intuitive.

Understanding the user experience around design can be as simple as watching sessions, observing behavior and user interactions with the design, experimenting, and iterating on the design further.

Customer support

Session replay has become a must-have customer support tool for professionals. Why? Playback gives support agents important context around solving customer support tickets—even allowing for customer support teams to co-browse with users “live.”

Given the challenges of asynchronous communication and customer inability to articulate in a meaningful way a given web experience through a ticket (or on the phone), watching a session replay saves time, generates empathy in the support team, and results in better, more efficient outcomes.

Additionally, the trail left by session replay allows customer support staff to “close the loop” with product managers when certain aspects of your web site or app continue to cause problems.

Marketing

Marketers measure ROI to track how well marketing and advertising campaigns drive specific behaviors including clicks, lead generation, conversions, and other KPIs. Unlike these abstract marketing metrics, session replay can help you optimize conversions by providing the necessary nuance of real user behaviors. It's that nuance that provides marketers with the answers they need to build better landing pages, run A/B tests, analyze results of experiments and more.

Additionally, if your session replay tool allows you to search and analyze segments of users by their behaviors or acquisition channel, you can accurately assess the quality of a given marketing tactic. For example, you can find and watch sessions of users who arrive on a landing page via Google paid search or Facebook Ads.

Another benefit of replay tools with segmentation is you can isolate a set of users—say your power users or users in a certain geography—and export lists for email marketing campaigns or other marketing strategies.

Tools like Fullstory also provide the ability to build marketing funnels based on the combination of any number of user- or event-based search criteria.

Fullstory and session replay: The keys to building a more effective web experience

Fullstory's powerful platform gives you the power to know your customer's digital experience. And when it comes to session replay, Fullstory is best-in-class. Here's how Fullstory measures up against the 10 criteria for evaluation below.

Search, segment, and filter

Fullstory indexes all user sessions and can be searched for events, users, date & time constraints, clicked elements, URLs, time, location, CSS selectors, and countless other web elements, all of which can be stacked to allow for highly specific segmentation of users based on behavior.

Language and code capabilities

We record everything mentioned in Session Recording Capabilities and are actively working to expand our session recording capabilities.

Single-page apps

The answer will vary by session replay tool, with Fullstory, the answer is usually, “Yes,” without caveats or reservations.

Performance

Fullstory has gone to great lengths and made use of multiple tactics to make the session recording script as lightweight as possible. As a result, the Fullstory script will not affect the performance of your site.

Asset caching

Fullstory caches assets so historical sessions that are retained by customers are protected from future web page and app changes.

Data retention

Fullstory plans can meet whatever data retention required, up to one year.

Sampling vs. everything

Fullstory automatically captures every session where the script is installed. No sampling means no bug goes uncaptured, no niche experience missed, and every support ticket can be tied to a specific customer experience.

Ability to identify sessions by user

Fullstory allows users to identify sessions by users.

Implementation

Installing Fullstory starts with placing a small snippet in the head of your site to begin recording. As part of the implementation, you can exclude specific fields, send over custom data, and integrate Fullstory with other tools in your stack.

Price

Fullstory offers custom plans for businesses based on their needs. Basic capabilities are available on the Free plan.

The hidden costs of choosing the wrong analytics tool

The Fullstory Team — Mon, 24 Jul 2017 16:30:00 GMT

If you work online, you need web analytics tools to understand user behavior in order to make smarter decisions. Yet determining which web analytics software your organization should use can be terrifying. Countless analytics programs exist—Google Analytics, Adobe, Mixpanel, Crazy Egg, Piwik, Alexa, Quantcast, KISSmetrics, Clicktale, Tealeaf, Similarweb, Amplitude, Fullstory (we hope) to any number of other tools—and open source web analytics tools, too.

How do you decide which tool(s) to pick—and is there really such a thing as a "free" analytics tool? What is the real cost of web analytics tools?

It's more complicated than it seems, so we're sharing a framework for analyzing the fully-baked cost of web analytics tools. It covers everything from the sticker price, to implementation costs, impacts on performance, data quality (relevance, reliability, and cognitive bias), and even the costs your tools create when it comes to running analyses.

There's much to consider beneath the surface of any given analytics tool, so buckle up—that fancy, "free" analytics tool may cost more than you think.

Spoiler alert: there's no such thing as a free analytics tool.

Analyzing the Cost of Web Analytics Tools—a Framework.

1. The cost of the tool (or tools), itself.

The “sticker price” of a particular tool is the most easily quantifiable cost of data analysis — and it’s also where discussion around analytics tools can get stuck.

Many analytics tools are free — i.e. there is no monthly fee to use them. Google Analytics is free. Fullstory offers a free version, too, as do many other analytics platforms.

Outside of a free tool, there are, of course, paid, subscription-based web analytics software options. These paid services can vary dramatically in price depending on lots of factors.

When estimating the cost of web analytics, “free” or “paid” should only be the beginning. Don’t get stuck on the sticker price of a tool — particularly if that price is “free.”

Have you ever heard the expression, “There ain’t no such thing as a free lunch?” “TANSTAAFL” is a popular phrase in economics that reminds us that everything has a cost.

Free web analytics tools don't exist. TANSTAAFL. Why? Because it's all the other costs that make web analytics tools expensive. Consider the costs to implement the tool, its effect on your site or app's performance, data quality costs, integration costs, and the costs to extract the insights—these are the real underlying factors that make web analytics tools expensive.

So start at the sticker price, but keep going.

2. The cost to implement the tool.

No magic wand exists that will install a given analytics tool, and implementing a tracking code across your web site or app can get downright complicated.

Depending on how your site or app is built, it can require any number of code injections — from putting code into proprietary, one-off pages to including it in the headers of templates, into widgets in Content Management Systems, etc.

Managing implementation never really stops — as your site or app grows and changes, it has to be maintained. The more complexity baked into your analytics tool, the more expensive maintenance can be.

Estimating the cost of implementation requires identifying what team or individual will own the process and factoring in the cost of their time. The cost to install and maintain analytics code can be measured by in terms of both salary and the work they aren’t doing — time spent maintaining analytics code is time not spent fixing bugs, improving the product, etc.

3. The cost of the tool on performance.

Every bit of code on your site or app has the potential to effect performance. Performance — site speed — can have a subtle, yet profound effect on the online experience for your site users.

Much has been written on this subject and it’s common knowledge that performance matters, but consider one fact: Google has found that a mere 400ms delay in search results can result in a 0.44 percent drop in search volume. Given Google is seeing some 2 Trillion searches per year, that 0.44 drop could mean a 9 Billion decrease in searches.

Site performance matters to your bottom line, so the impact of a given analytics tool (or a number of tools) on the speed and performance of your site or app can be measured in terms of bouncing users, missed sales, and lower KPIs.

4. The cost of low quality data.

This is a big one.

What you pay in terms of data quality is a hard to measure — data quality pivots on many factors from basic things like the relevance and reliability of the data to more complex factors including our own cognitive hard-wiring. Let’s take a closer look.

Relevance of the data.

Is the data relevant to the problems you’re looking to solve? Is it reliable? Relevance depends on whether the data received helps answer the question at hand. It also depends on timeliness — by the time you get the data, is it still useful for answering your question. Analytics tools that don't provide real-time data become immediately less relevant.

There's another aspect to "real-time stats," which is the time it takes to get the data out of the tool. If the data you need isn't immediately accessible or extractable for some reason, it can extend the time it takes to get at the information. If it takes too long, will it still make a difference to your decisions?

This is why Google is a relevance machine: you ask the search engine a question and instantly receive highly relevant search results. Decisions are easier to make when analytics come in real-time.

Reliability of the data.

The metrics provided by a given analytics tool have a strong-yet-subtle effect on the quality of data because metrics have built-in assumptions that frame your understanding. Let’s unpack this.

» What you see is all there is.

In Daniel Kahneman’s book Thinking Fast and Slow, Kahneman shares research that supports the idea that people make decisions using only the basic information available to them — without considering what’s not explicitly known. “What you see is all there is” or “WYSIATI” is a way to remember this source of overconfidence in behavior. As it applies to web analytics, the tendency is to focus on what you know. In that way, the data that analytics tools present becomes authoritative — the singular basis for decisions — and can lead to “jumping to conclusions” without looking for gaps in your information.

» Framing effects.

Framing effects take into consideration how the presentation of information effects how that information is interpreted. Consider the following example of a framing effect — ask yourself, which phrasing sounds more positive? More negative? 90% of users ignored the new feature on your website over the first week after launch -VS- 10% engaged with the new feature in the first week.

Framing effects are a major focus of behavioral psychology because how information is framed matters greatly to decisions. On the web, the effect of framing is far-reaching — consider the default “opt-in/out” switch on a sign-up form being flipped “on” instead of “off.” If it’s toggled to the “on,” position, you are all-but-guaranteed to have more people opt-in by default.

» Assumptions.

Anyone who spends time with data understands that the pretty graph or clean metric comes with a list of assumptions and caveats. As the saying goes, “The devil is in the details.” Meanwhile, given that analysis is conducted to “figure something out,” the person crunching the numbers has the ability to bias the results based on their perspective. In the academic community, Nobel prize winning economist Ronald Coase may have summed up this phenomenon best in saying:

“If you torture the data long enough, it will confess.” — Ronald Coase

Indeed, if you’ve worked around analysts (or are one!) for any length of time, you will inevitably hear the question, “Well, what do you want the numbers to be?”

Meanwhile, settling on a specific metric asserts its validity as a basis for decision-making. You are tied to that metrics' built-in assumptions. Depending on the metric, this can be a problem. Metrics that aggregate data come with their own set of assumptions. For example, using averages without consideration for the nature of the underlying data can lead to bad decisions.

For web analytics tools, bounce rate is a great example of how an assumed metric frames and limits the way you make decisions. Bounce rate captures what percentage of users land on a page only to leave the site.

Bounce rate, which is simple in theory, is complicated in application. For one, the “framing effect” built-in to bounce rate is that a high bounce rate is “bad.” However, given the number of single-page apps, dynamic pages with infinite scroll, and other common nuances within any given web site or application, bounce rate can be downright irrelevant as a standalone metric. By focusing on bounce rate, you have accepted the assumption that bounce rate as a useful metric on which to base decisions (WYSIATI). But in actuality, “it depends” — as is explicitly discussed in Google Analytic’s support documentation on Bounce Rate.

» Sampling.

If your web analytics tool uses sampling, your data quality will unavoidably take a hit. How confident can you be the sampled data reflects the underlying reality?

When it comes to online behavior, edge cases are the rule. Differences in devices (laptops, tablets, mobile devices), browsers, and operating systems, and other factors all combine to make results highly specific to individual users. If a given web analytics tool uses sampling, you have to get comfort on the reliability of those sampling assumptions.

Data quality matters. The cost of low quality data is making poor decisions that cost you time and money. “What you see is all there is” means that your analytics tool stack has a pile of answers ready for you, regardless of whether the answers are any good. If those answers are low quality (or unclear in suggesting what you should do), you find you have no choice but to do additional research through that tool or through looking to other analytics tools.

5. The cost of multiple tools.

The perfect, omniscient web analytics tool that has all the answers doesn’t exist (yet!). That means teams must stack together disparate tools in order to fill gaps in their knowledge.

Each tool will be susceptible to the costs detailed above — maintenance, data quality, effects on performance, etc., but in combination, you get the synergistic bonus cost of friction in having to shift across tools, manage multiple subscriptions, understand how to use each tool including knowing which tool is useful and for what, integrate where possible, translate metrics, and on and on.

With multiple tools, costs compound and the cross-tool transaction costs add up quickly.

6. The cost of running analysis.

Last, but certainly not least, is the cost of actually running an analysis using the tool. This cost breaks down into how hard it is to use the tool, whether or not it can be used by decision-makers directly or if you need to hire analysts to do the number crunching. And both of these factors will cost you most in terms of time.

The ease-of-use of the analytics tool.

Can a decision-maker pick up the tool and immediately extract data — get at an insight? How much training is required to make the tool useful? The more complex the tool, the more friction it puts on getting to data.

Hiring analysts.

Ease-of-use directly affects whether or not decision-makers will need to rely on analysts to crunch the numbers. If the value in a tool requires analysts, you have the obvious cost of extra headcount but the less obvious cost of time—time to requisition an analyst or team to run the report you need to make a decision.

Time.

“Time is money” — so the saying goes. Time has come up before in our analysis as it is a primary driver of data quality. Timeliness is required for data to be relevant. How do you measure the value of time? Answering that would require it’s own analysis; however, a simple heuristic works. The value of information is inversely proportional to to the time it takes to get that information.

You can apply this heuristic to web analytics tools in two ways:

Time-to-data. Measured as how long it takes to get at the raw data, “time-to-data” is affected by the complexity of the tool — setup requirements, ease-of-use, how easy it is to get data out of the tool, etc. If you’ve ever hunted for some obscure information only to find a very useful graph that can’t be exported into a table, you know the pain of time-to-data.
Time-to-insights. Unlike time-to-data, time-to-insights is how long it takes to go from raw data to meaningful, which is to say actionable, information. Analytics tools may have incredible data that is easy to extract, hence low time-to-data, but painfully expensive to turn that data into actionable insights.

Let’s take an example. It takes you 20 minutes on average to extract data for a report — that’s your time-to-data. Then, data-in-hand, it takes you two hours to process that data and tease out an insight—that’s your time-t0-insight. Now, repeat that process a few hundred times and if you’re an analyst, factor in the friction of passing on the report only to find that it needs to be re-run for a different time period or some other variable. Time-to-data and time-to-insights add up in a hurry.

Adding up all the costs of your analytics.

Here’s a quick recap of the six buckets to consider when estimating the cost of your web analytics:

Why go into such detail to estimate the cost of an analytics tool?

Because analytical tools are expensive — even if they are “free,” but it’s not at all obvious just how expensive they are. By analyzing the cost of a given web analytics tool (or stack of tools), you can articulate not only how expensive a given tool is, but also why it’s expensive.

(And that's a very analytical thing to do.)

If you’re just adding into your analytics stack yet-another-free-tool — because hey, it’s a free analytics tool, why not? Remember TANSTAAFL. Will the insights you get out your free tool actually be worth the effort of installing, maintaining, and paying attention to that tool? Will the "free" data you get out of the tool be high quality, reliable, relevant?

The kitten may be free but caring for that cute fur ball is another thing entirely.

Let's take one more example—in this case honing on a popular analytics "feature" — heatmaps. Heatmaps are a fun way to visualize aggregated clicks and mouse-tracking on a website—in some cases they even capture attention tracking. The thing is, there are lots of problems with heatmaps that can make them very expensive. Consider there built-in assumptions and the impact those assumptions have on data quality (E.g. how data is aggregated across devices and browsers and how heatmaps deal with dynamic applications).

Additionally, heatmaps can also be costly to configure and implement.

With all that work done (and the assumptions accepted), once you get your heatmap visualizations, you're suddenly stuck with the problem of WYSIATI—in this case, your web heatmaps present you with lots of colorful splotches imply "insights." But are they actually insights?

(Learn more about web heatmaps, how they work, and their built-in assumptions here.)

The cost of a given analytics tool can add up quick, and as exemplified by heatmaps, the costs aren't always obvious (Assumptions, data quality, unclear insights, etc.).

How expensive is your web analytics tool?

Henry Ford famously quipped that, "If you need a machine and don’t buy it, then you will ultimately find that you have paid for it and don’t have it."

When it comes to web analytics tools, costs matter, but no cost is greater than the that of choosing the wrong tool. The easiest way to choose the wrong tool? Only pay attention to the sticker price.

If you need a machine and don’t buy it, then you will ultimately find that you have paid for it and don’t have it.
— Henry Ford

And whether it’s through developers spending months implementing and maintaining the tools, stakeholders struggling through aggregated reports that lack clarity or obvious actions, poor decisions made from assumption-loaded data, framing effects that limit how you even understand the information, or any number of other problems examined in this analysis, perhaps the juice isn’t worth the squeeze.

Analytics tools only work when they provide reliable, relevant insights that you can act on. The best analytics tools will be clear and actionable, having low time-to-data and time-to-insights.

Keep asking the hard questions about your tools. Run the analysis and discover the real cost of your web analytics stack.

How Fullstory is leading the charge in mobile app analytics tools

The Fullstory Team — Mon, 15 Sep 2025 05:00:00 GMT

The difference between an app people love and an app people delete comes down to one thing: if the user feels understood.

In a time where every tap and swipe can win or lose a customer, seeing your app through their eyes is the ultimate competitive advantage. This isn't just about fixing bugs; it's about seeing the friction, the frustration, and the moments of delight that define your digital experience.

The challenge is that most traditional analytics often provide just quantitative data, without the qualitative why. This leaves product, engineering, and CX teams guessing.

This guide offers a clear analysis of mobile analytics tools to help you choose the right solution for your team.

How they stack up

The method of data capture is a critical differentiator, impacting privacy, performance, and the depth of insights. This table breaks down how each tool approaches data collection.

In-depth tool analysis

Fullstory for Mobile Apps

Fullstory provides unparalleled visibility into how users interact with your mobile applications by capturing high-fidelity visual re-creations of your app's views. Its innovative approach focuses on draw instructions instead of screenshots, ensuring privacy by default and consuming minimal resources. This delivers precise, element-level analytics to help you understand and optimize every digital interaction without compromise.

Pros:
- High-Fidelity Replay: Captures the true user experience without the performance drain of screen recording.
- Privacy by Default: Automatically masks sensitive data on the device, ensuring PII is never transmitted.
- Low Performance Impact: Lightweight SDK with minimal bandwidth and memory usage, preserving the user experience.
- Tagless Autocapture: Automatically captures all interactions, providing complete, retroactive data without manual tagging.
Cons:
- May require integration with other tools for advanced quantitative marketing analytics.

Amplitude

Amplitude is a powerful quantitative analytics platform known for its predictive capabilities and deep user segmentation.

It excels at handling large volumes of data and is ideal for businesses focused on advanced data analysis, cohort analysis, and understanding user lifecycles.

Pros:
- Excellent for in-depth behavioral cohorting and funnel analysis.
- Strong predictive analytics features to forecast user behavior.
- Scalable architecture designed for large datasets.
Cons:
- Can have a steep learning curve for those new to advanced analytics.
- May seek a more deeply integrated qualitative solutions.
- Can be expensive for smaller teams or startups.

Firebase

Backed by Google, Firebase is a comprehensive mobile development platform that includes a robust free analytics solution. It's an excellent choice for teams already invested in the Google ecosystem, offering seamless integration with tools like Google Ads and BigQuery.

Pros:
- Free and integrates seamlessly with other Firebase and Google services.
- Provides real-time analytics and automatic event tracking.
- Includes Crashlytics for detailed crash reporting and analysis.
Cons:
- Focuses more on high-level trends rather than granular, individual user insights.
- Lacks native session replay and heatmaps; requires third-party integrations.
- Can be limited in customization compared to more specialized tools.

Mixpanel

Mixpanel is a popular event-based analytics tool that emphasizes ease of use and powerful reporting. It's a strong choice for teams that want to track specific user actions and analyze conversion funnels and retention patterns in detail.

Pros:
- User-friendly interface with customizable dashboards.
- Powerful funnel and retention analysis features.
- Flexible event tracking allows for detailed measurement of user actions.
Cons:
- An event-based model may require upfront developer instrumentation for tracking user actions.

Glassbox

Glassbox is an enterprise-focused digital experience platform that provides analytics across both web and mobile. It emphasizes customer journey mapping and AI-driven insights to identify user struggles and technical issues.

Pros:
- Strong session replay and journey analysis capabilities.
- Tagless data capture reduces the need for manual instrumentation.
- Excellent customer support and recognized as a leader in multiple G2 categories.
Cons:
- Its broad, enterprise-focused feature set may require dedicated resources to fully utilize.

Smartlook

Smartlook offers a combination of qualitative and quantitative analytics for both websites and mobile apps. It stands out for its cross-platform capabilities and strong A/B testing features.

Pros:
- Tracks user journeys across both web and mobile platforms.
- Provides session recordings, heatmaps, and crash reporting.
- Strong A/B testing features for conversion optimization.
Cons:
- Session recording uses video-based methods, which can have different performance characteristics.
- Privacy configuration may require developer input via available APIs.

UXCam

UXCam is a mobile-only analytics tool focused on providing deep qualitative insights for UX and product teams. It automatically captures user sessions and provides features like heatmaps and frustration analytics.

Pros:
- Strong focus on mobile-specific UX with features like heatmaps and session replays.
- Tagless autocapture simplifies the implementation process.
- Provides crash reporting with session recordings for easier debugging.
Cons:
- Mobile-only focus prevents tracking of cross-platform user journeys.
- Analytics capabilities may be less extensive than dedicated quantitative tools.

Contentsquare

Contentsquare is an enterprise-level digital experience platform that offers advanced analytics for web and mobile. It is known for its AI-powered insights and unique features like zone-based heatmaps.

Pros:
- Advanced customer journey and zone-based analysis.
- Strong focus on AI-driven insights to identify opportunities.
- Recognized as a leader in mobile app optimization and analytics.
Cons:
- Teams seeking highly customized or complex event schemas may need to invest time in manual tagging alongside autocapture features

Fullstory is the best mobile app analytics solution for enterprise platforms

For enterprises focused on deeply understanding and optimizing their mobile experiences, Fullstory emerges as a robust, hassle-free choice. It offers comprehensive, high-fidelity tracking, a streamlined setup process, and an unparalleled commitment to privacy and performance. This unique combination positions Fullstory as the definitive choice for businesses aiming to enhance their mobile app experiences without the complexities and risks that often accompany other analytics tools.

Ready to elevate your mobile experience? Request a demo and see what Fullstory for Mobile Apps can do for you.

What is mobile analytics? The complete guide

Fullstory — Wed, 24 Sep 2025 07:00:00 GMT

Mobile analytics—or mobile app analytics—is both something that you gather and something that you practice.

It has three parts:

Data collection
Data analysis
Data interpretation

"Data," in this case, refers to the way users use and interact with your app, providing crucial analytics for mobile app optimization and user experience improvements.

Effectively utilizing mobile app analytics—gathering real-time insights and building improvements—is business-critical. It helps teams understand user behavior, enabling them to quickly identify friction points, optimize the user experience, and improve retention.

Examples of mobile analytics

Any metrics around user devices (whether Android or iOS) are a form of mobile analytics. Common examples include:

Advertising KPIs
Web analytics
Funnel analysis
An app’s performance and app usage
Heatmaps
Retention rates
Click-through rates
Conversion rates
Response rates to push notifications and marketing campaigns

These insights let teams maximize ROI by making informed decisions about new directions and initiatives.

How are mobile analytics different from web analytics?

Mobile analytics focus on how users interact with your app, while web analytics track behavior on desktop or mobile websites.

Web analytics often include:

Page views and clicks
Online ad performance
Search engine optimization (SEO)

Mobile analytics capture:

In-app behavior and navigation
Device-specific performance
User engagement and retention

Fullstory gives you visibility across web and mobile journeys

Fullstory brings together data from both mobile apps and web experiences to give teams a unified, high-fidelity view of the entire user journey.

And StoryAI is the cherry on top.

With mobile and web data living in one place, StoryAI automatically surfaces meaningful patterns and insights, helping teams uncover opportunities and pain points across the full digital experience—no manual digging required.

Get a demo and see how you can create a perfect digital experience and boost your bottom line.

Why are mobile analytics important?

According to Fullstory data, between 2024 and 2025, mobile app usage surged by 332%. But that explosive growth hasn’t come without challenges—exits caused by errors jumped by 254%, and bounce rates are up 54%. As people spend more time on mobile, they’re also less patient with poor experiences.

If you're not equipped with tools that show exactly how users interact with your app—the friction they face, where they drop off, or what keeps them coming back—you’re operating in the dark. Behavioral data is essential for understanding user intent and improving retention.

→ Explore more mobile app trends.

What are the types of mobile analytics?

Every business is different, so every business needs different data to make the best decisions–and not all mobile analytics platforms are created equal. Understanding your needs and short- and long-term goals will help focus on one or more varieties of mobile analytics.

Mobile advertising analytics

As companies invest thousands of dollars in mobile marketing, it’s important to understand how consumers engage with advertisements.

App monetization analytics

It’s no secret that customers prefer free apps over paid ones. In the current landscape, most businesses offer app downloads for free and create revenue by:

Monetizing with app-to-app advertising
Offering premium in-app currency that costs real money
Providing a shop for in-app purchases
Renting streaming media

The monetization options you choose will vary depending on your verticals. By tracking them carefully, you can review user engagement with your monetization options and understand how you can encourage users to spend more.

Performance analysis

Mobile analytics software shows you who your app works for—or doesn’t. They locate bugs that might appear on iOS but not Android (or vice versa), catch performance issues with crash reporting, and find slowdowns in load times that can damage your sales.

In-app engagement analytics

If you can see how long your customers spend in which parts of the app, you can:

Find bottlenecks in your checkout process
Locate performance issues
Emphasize the most valuable parts of your app

How do different teams use mobile analytics?

Just as different businesses use the insights they gain from mobile analytics in different ways, different departments within a company use different information from the analytics they choose to gather.

Mobile analytics for product teams

Product managers can review analytics to find purchasing and funnel trends before making decisions about changes in product lines, inventories, and discontinuations.

They can even gather insights about the types of customers who buy specific products and pass the information to marketing to improve audience targeting at the advertisement stage.

The real-time updates that mobile analytics afford mean managers can see improvements immediately or find problems in any step of the process for any type of user in any part of the world.

Mobile analytics for marketing teams

Just as web analytics provide valuable information for marketers, mobile analytics give marketers incredible insights into the effects of their campaigns.

Marketers can track:

App-related campaign effectiveness (attribution)
New user installations
The purchasing behavior of new and existing users
Time spent in app
Sales funnel performance and conversions
Cart abandonment rates

Just like product managers, marketing teams monitor sales funnel and conversion rates to determine how best to encourage new and existing consumers to spend money in the app.

Marketing analytics tools that only cover the desktop are leaving half the picture out. Mobile analytics close that gap, giving teams visibility into how campaign traffic behaves, converts, and drops off inside your app.

Mobile analytics for UI and UX teams

Since design trends change constantly and customers update their preferences regularly, whether they realize it or not, UI and UX teams can use mobile analytics to track customer engagement and preferences.

Whether focus-testing or A/B testing design changes or tailoring landing pages to consumer interests with the help of machine learning and demographic identification, UI and UX teams use the immediate, real-time, and unconscious feedback every user gives them to make the experience as smooth as possible.

Mobile analytics for technical teams

The backend is just as intimately linked to the user experience as the UX team. If the app malfunctions for a certain population of users, then the company loses both its mobile revenue and its goodwill for future purchases. An app that crashes over and over again will be abandoned quickly.

With analytics to identify priorities, technical teams can improve app engagement for the best possible user experiences and prevent problems like security vulnerabilities from becoming full-blown PR catastrophes.

The KPIs you should track for your mobile app analytics

While the data you gather will differ between verticals and teams, here are a few common factors to consider and what they help you understand:

Customer retention rates to predict future spend
Lifetime value of customers to assess total worth
User growth rates to assess marketing initiatives
Uninstallation sequences to find opportunities for improvement

When combined with specialized KPIs for each team, the right set of mobile app analytics can transform your mobile initiatives with masterfully informed decisions.

Mobile app analytics tools

With a variety of mobile app analytics tools to choose from, it should come as no surprise that SaaS providers are jumping at the chance to provide quality mobile analytics.

Amplitude

A robust solution for data teams seeking advanced behavioral analysis and user segmentation at scale. Great for lifecycle insights and predictive modeling.

Firebase

Google’s all-in-one mobile platform with a free analytics framework. This tool is best suited for teams already embedded in the Google ecosystem.

Mixpanel

Event-based tracking, a clean interface, and strong funnel and retention reports make this tool ideal for product-led teams that want to optimize user flows.

Glassbox

An enterprise solution combining session replay and AI-driven journey analysis to help large teams identify friction and optimize experiences.

Smartlook

Offers session recording and heatmaps across platforms, with A/B testing capabilities for conversion-focused strategies.

UXCam

Tailored specifically for mobile apps, UXCam provides deep qualitative insights with automatic capture and mobile UX features like frustration detection.

Contentsquare

An enterprise-grade platform leveraging zone-based analytics and AI for sophisticated mobile app optimization.

Fullstory

Fullstory’s mobile app analytics are second to none, boasting seamless data autocapture, high-fidelity mobile session replay, crash and error reporting, user journeys, and more–all while protecting user privacy without sacrificing performance. Fullstory does just what its name implies—it gets you the full story every single time.

→ Want an in-depth analysis? Check out our quick guide to choosing the right mobile app analytics tool.

3 Things to consider when choosing a mobile analytics tool

With countless mobile analytics platforms available, not all tools are created equal—and some can unintentionally introduce challenges that impact user experience or raise privacy concerns.

1. Performance overhead can hurt UX

Some analytics tools rely on heavy SDKs, video-based session recordings, or extensive event instrumentation. These methods can increase app load times, strain device memory, or cause unnecessary network usage—factors that directly affect user satisfaction and retention. Look for tools that offer lightweight data capture methods to minimize performance impact, especially in high-traffic or resource-constrained environments.

2. Not all tools handle sensitive data the same way

Capturing user sessions and behavior can be valuable—but only if done responsibly. Some platforms may inadvertently capture personal or sensitive user data, especially in login flows, payment screens, or chat features, putting your app at risk of compliance issues. Prioritize solutions with built-in privacy protections like on-device data masking and automatic personally identifiable information (PII) blocking to ensure you’re not collecting more than you need.

3. Quantitative vs. qualitative insights

Many traditional tools focus solely on numbers—retention, funnels, bounce rates—without showing the context behind user behavior. On the other hand, some offer session replays or heatmaps to visualize experience friction points, but lack robust segmentation or cohort analysis. The most valuable tools combine both quantitative data and qualitative insight so teams can understand not just what users are doing, but why they’re doing it.

→ Ready to take mobile experiences to the next level? Listen to Fullstory's experts unpack the latest mobile benchmark data, explain why traditional mobile capture tools come with hidden risks, and more in the 2025 State of Mobile Experiences.

A groundbreaking platform that has been committed to user privacy from the very start, Fullstory does just what its name implies—it gets you the full story every single time.

What is digital experience analytics? The complete guide

Fullstory — Tue, 21 Apr 2026 07:00:00 GMT

Digital analytics is the practice of measuring and analyzing user interactions across digital products to understand what users do and why. Unlike traditional analytics, which focus on basic metrics like page views, digital analytics reveals the full behavioral story behind user actions.

Modern digital analytics captures everything from user journeys to friction signals. It reveals where users get stuck, what drives conversions, and what causes abandonment.

Today, digital analytics focuses on three key areas:

Behavioral patterns: Understanding what users do and why they do it
Technical performance: Tracking issues like load times and errors that impact experience
Business impact: Connecting user behavior directly to revenue outcomes

The real value lies in understanding customer sentiment between clicks: those subtle signals that indicate user satisfaction or frustration. With the right digital analytics setup, you can spot problems before they impact revenue, identify optimization opportunities, and create experiences that work for your users.

When you have this level of insight, you're building a clearer picture of what to fix and why.

What is digital experience analytics?

Digital experience analytics involves understanding, measuring, and improving user interactions across digital platforms like websites and apps. It captures detailed insights into user behavior, intent, and sentiment, while identifying friction points and opportunities to enhance the overall customer experience. By analyzing patterns and performance metrics, it empowers teams to create intuitive, high-converting digital journeys.

Digital experience analytics examines user interactions across applications and platforms in granular detail. This comprehensive approach enables teams to analyze multiple data types and understand the complete user experience.

Digital experience analytics solutions include various capabilities and tools to both measure and analyze customer experience issues. Fullstory's behavioral data platform, for example, offers tools such as conversion funnels, heatmaps, click maps, user journeys, and other engagement data, often in real time.

Ultimately, digital experience analytics is valuable since it automatically measures up to billions of different user sessions and trillions of individual user behaviors. More importantly, Fullstory combines digital experience analytics with customer journey analytics to analyze those behaviors and reveal what's happening during every step of their journey on your website or app.

Why use digital experience analytics?

Benefits of using experience analytics

In addition to the primary purpose of digital experience analytics, there are several additional benefits to using tools designed for this goal, including:

Boosted revenue, since customer experiences heavily impact whether someone will make a purchase
Greater personalization can promote improved customer loyalty and retention
Increased word-of-mouth and brand awareness, since satisfied customers will spread the news of how great your platform is to their friends and family members
Better marketplace competition when your prices are very similar to those of your competitors
Improved digital marketing strategies and customer understanding–staying informed about user experience trends can complement data insights, helping you better understand your target audience and connect with them through targeted marketing campaigns.

Who uses digital experience analytics?

Nearly every team can get something useful from digital experience analytics. Even the smallest brick-and-mortar, mom-and-pop shop can benefit from digital experience analytics to improve the experience users find when they visit that shop's site.

Research shows 80% of companies say their digital transformation efforts involve several business units or, in certain cases, their entire organizations. The more people focus on digital experience analytics and product analytics, the more benefits you may see.

That's because every team in your organization affects user experiences on digital platforms. Here are some examples of the types of teams that can use digital experience analytics:

The marketing team, which prepares customers for what to expect, can use experience analytics and qualitative data from session replay to bring the right audience to your site or app
The website design team can use experience analytics to design a more streamlined, welcoming platform
Teams may use a retail and ecommerce analytics platform to detect technical issues or bottlenecks on your site or app
Customer support teams can use behavioral analytics to figure out where users are most frustrated on your platform and gain context on user feedback

Since digital experience analytics spans several teams, it's a good idea to understand it thoroughly and know how to practice it effectively.

How does experience analytics work?

Experience analytics primarily focuses on unifying customer journeys from all digital touchpoints. Digital touchpoints, of course, include every step of the customer journey where a visitor may interact with your app or platform, from clicking on a pay-per-click (PPC) ad to clicking "check out” on an online store.

Your ultimate goal for experience analytics is to capture data and use reduce friction at every step for your visitors and customers. To do that, you need to:

Anticipate customer needs by understanding them better (through analyzing qualitative and quantitative experience data)
Deliver content that makes individual connections

Even with this understanding, customer journey design can be challenging for organizations no matter their sector or industry.

Ecommerce companies, small businesses, and SaaS providers alike need to make sure they collect the right data and establish what their most important key performance indicators (KPIs) are.

In essence, you need to:

Identify which data to collect and analyze most often
Figure out how that data affects your upcoming design/update goals
Implement improvements that lead to better customer experiences across the board

31% of teams now say reviewing and validating AI outputs is their single biggest obstacle, according to our 2026 Digital Experiences Survey. With experience analytics, you can identify the right information needed to improve and streamline your customer journey, which will lead to the benefits described above.

What role does experience analytics play in user journey design?

Experience analytics plays a major role in modern user journey design for mobile apps, desktop websites, and any other digital platforms. In a nutshell, user journey mapping:

Creates a total overview of the complete user flow for every defined experience (i.e. what a prospective customer experiences when they click on a pay-per-click (PPC) ad and go to your online store)
Lets you identify friction points or areas where customers are more likely to churn, that is, abandon a cart or your website entirely
Surfaces areas where you can optimize or streamline the customer journey for maximum effectiveness

Think of experience analytics as the process to ensure your user journey design is the best it can be. It's the microscope your team members can use to design the best possible user journeys for your target audience members.

Experience analytics best practices

Of course, you have to use experience analytics properly to see major benefits and to avoid turning your target audience members away. To do that, you should keep these best practices in mind:

Decide which data points apply to different portions of customer journeys
Choose the right tools to collect metrics relevant to your customers/organizational goals
Translate all gathered data into new customer journey maps and user experience designs. Then you can find the best improvements to those maps and designs and integrate them into your existing elements
Test all new hypotheses with A/B testing. A/B testing lets you find the most effective areas that your users respond to and deploy the best-performing elements everywhere
Don’t stop collecting data after a few improvements. Instead, you should constantly collect more and more data to eliminate new friction points as they come up
Follow all applicable privacy laws. Laws like the California Consumer Privacy Act (CCPA) give some of your consumers the right to opt out of having their internet browsing data shared and used for analytics. For compliance, install a cookie consent manager on your website.

Even better, constantly collecting data will help you stay one step ahead of your competition and ensure that your company is best-in-class.

Digital experience analytics features

Here are the core capabilities that make up a modern digital experience analytics platform.

Session replay

Session replay reconstructs individual user sessions in the browser, including mouse movements, clicks, scrolls, form inputs, and errors. A good session replay tool lets you move from a metric anomaly (checkout dropped 12% this week) to a behavioral explanation (users are hitting a broken element at step 3) without switching platforms. Look for tools that handle replay without capturing sensitive data by default, and that link sessions to funnel events so you can drill from aggregate data into individual behavior.

Conversion funnels

Conversion funnels show where users drop off across multi-step flows: checkout, signup, onboarding, form completion. You define the steps, the platform measures completion rates at each one. The capability that matters most is the ability to drill from a funnel drop into the sessions behind it. That connection between aggregate data and session-level behavior is what turns a funnel from a reporting tool into a diagnostic one.

Heatmaps

Heatmap tools help you visualize on-page behavior by showing concentrations of user activity: where people clicked, how far they scrolled, and what they ignored. They aggregate millions of individual interactions into an easy-to-read visual layer over your page.

Many of the best digital customer experience tools come with different examples of heatmaps, like:

Scroll heatmaps
Attention heatmaps
Dot/click heatmaps
Attribution heatmaps
Behavior heatmaps

Each of these heatmap types can be beneficial for your organization and offer different levels or types of actionable insights.

Journey mapping

Journey mapping shows the actual paths users take through your product: not the flows you designed, but the ones they chose. A good journey map surfaces unexpected entry points, common exit pages, and the loops users fall into when something isn't working. The most useful implementations let you filter by segment (new vs. returning, converted vs. abandoned) to compare behavior across cohorts rather than looking at everyone in aggregate.

Frustration signals

Frustration signals capture behavioral indicators of user friction that don't show up in standard analytics. The two most common are rage clicks (repeated frustrated clicking on unresponsive elements) and dead clicks (interactions that trigger no visible response). These signals are leading indicators: they appear before the conversion impact hits your funnel data, giving teams time to diagnose and fix problems earlier.

Segmentation

Don’t forget segmentation tools. Segmentation analytics tools allow you to focus on targeted content delivery for specific users.

Alternatively, these tools may help you identify different issues experienced by new users compared to repeat users, mobile users compared to desktop users, and more. In other words, segmentation tools help you identify the best and worst elements of your site/app journey for different segments of your user base. This may allow your team to tailor solutions for those groups individually to excellent effect.

AI-powered insights

AI-powered insights automate the discovery layer that previously required an analyst to pull and compare reports. A mature implementation covers at least three modes: proactive anomaly detection (the platform flags issues before you go looking), session summarization (AI distills multi-session patterns into plain-language findings), and natural language querying (teams ask questions about behavior without building segments by hand). The quality of AI features in any digital experience analytics platform depends on the quality of the behavioral data underneath them.

Digital experience analytics tools and platforms

To start using digital experience analytics for your brand, you need to have the best analytics tools for your teams. Luckily, there are many analytics tools you can use starting today.

Platform	Best for	Key strength
Fullstory	Enterprises eliminating friction and powering AI with behavioral data	Intelligent digital experiences powered by human context: the behavioral data infrastructure that makes your AI strategy work
Google Analytics	Baseline traffic and conversion tracking	Free and widely used (GA4)
Amplitude	Product teams focused on retention and funnels	Cross-platform event tracking and self-serve analytics
Contentsquare	Enterprise in-page behavior analysis	Journey analysis and friction scoring (includes Heap)
Quantum Metric	Enterprise teams prioritizing real-time friction detection	Automated anomaly detection and quantified experience impact

Fullstory

Fullstory is the Intelligent Digital Experience Platform. Fullcapture captures every user interaction without predefining what matters, preserving the complete human context teams need to understand behavior, eliminate friction, and prove revenue impact.

StoryAI orchestrates AI agents that analyze behavioral data and act on it, surfacing friction points, summarizing sessions, and answering business questions without manual review. Guides and Surveys personalize experiences in the moment, steering users toward success through targeted in-app guidance and contextual feedback capture.

Those capabilities run across three product lines: Fullstory Analytics for product, UX, and growth teams; Fullstory Workforce for support, CX, and IT teams; and Fullstory Anywhere for data teams, delivering real-time behavioral context to the rest of your AI stack.

Because Fullstory captures behavioral data without bias, it also serves as the ground truth for AI agents: the digital sight that lets agentic systems see what users are experiencing, infer intent, and respond in real time.

Google Analytics

You can't forget Google Analytics: it's arguably the first major experience analytics software suite made available for everyone. The free GA4 platform is flexible and useful for marketers and small business owners who want to improve online conversion rates without paying for a tool. However, it lacks the behavioral depth and session-level context that other platforms provide.

Amplitude

Amplitude is a product analytics platform built around event-based tracking. It's strong for funnel analysis, retention charting, and user segmentation, though it relies on manual event instrumentation and doesn't capture session-level behavioral context out of the box.

For teams that need retroactive querying or behavioral depth without the instrumentation overhead, that's where Amplitude alternatives come in.

Contentsquare

Contentsquare focuses on in-page interactions and micro gestures, turning customer behaviors into friction scores and visualizations. The platform now includes Heap (acquired 2023), extending its product analytics coverage alongside its core journey and heatmap capabilities.

If the fragmentation and setup overhead are slowing your team down, the list of Contentsquare alternatives is a good place to start.

Quantum Metric

Quantum Metric targets large enterprises looking to detect and quantify digital friction in real time. It flags anomalies in user behavior and ties them to revenue impact, which helps teams prioritize fixes. Its focus is on monitoring and alerting rather than the deep session-level behavioral context Fullstory provides.

Putting it all together

Understanding what users do is a start. Understanding why and acting on it fast is where digital experience analytics delivers its real value. In the AI era, behavioral data becomes the foundation both human teams and intelligent systems need to reduce friction, recover revenue, and respond in the moment.

Schedule a demo to see how Fullstory works.

Top 5 enterprise web analytics tools on the market

The Fullstory Team — Sat, 09 Aug 2025 05:00:00 GMT

Picking the right enterprise web analytics platform isn’t just about tracking clicks—it’s about unlocking the insights that drive growth, customer experience, and competitive advantage. With so many tools promising depth, scale, and AI-powered capabilities, the real challenge is knowing which ones truly deliver for enterprise teams.

In this guide, we break down today’s leading enterprise web analytics solutions, their strengths, trade-offs, and what sets them apart so you can make the smartest choice for your business.

Fullstory
Google Analytics
Adobe Analytics
Mixpanel
Amplitude

What is enterprise web analytics?

Enterprise web analytics is a set of methodologies and technologies used to measure, collect, analyze, and present internet data. A proper analytics setup is essential for accurate measurement and reporting, ensuring that website data is collected and configured to provide detailed insights while maintaining privacy and flexibility. This data is used to understand and optimize web usage, providing insights into website visitors'’ behaviors and preferences.

Plus, web analytics provides quantifiable metrics of various aspects of website performance. Traffic metrics are a key category of website data tracked by enterprise web analytics tools, helping organizations monitor visitor numbers, engagement, conversions, and sources. These metrics include the number of visitors, pages viewed per visit, time spent on the site, bounce rates, conversion rates, and more. This data can be tracked in real time, offering valuable insights that help optimize a website's search engine visibility, improve user engagement, and ultimately meet business objectives.

Data collection is only the first step; data processing organizes, filters, and aggregates website data to prepare it for analysis and reporting, enabling meaningful insights into user behavior and website performance.

From an enterprise perspective, web analytics is not just a tool for measuring website traffic but a resource for business and market research—and a means to gauge website effectiveness.

The role of enterprise analytics

In the enterprise landscape, analytics goes beyond measuring website metrics. It's a robust mechanism for capturing, interpreting, and utilizing data across various business functions. This broader scope is commonly referred to as enterprise analytics.

Enterprise analytics is critical because it supports:

Informed decision-making and strategic planning
It helps drive business growth and competitive advantage
It allows you to centralize data from all your data sources, providing a unified view of the business and supporting strategic decision-making across multiple departments
This process allows companies to pinpoint trends, uncover insights, predict future scenarios, and optimize operations.

One of the primary roles of enterprise analytics is to enhance customer understanding.

By monitoring and analyzing customer behavior data, businesses can identify customer needs, preferences, and pain points, leading to more personalized service or product offerings.

Additionally, enterprise analytics can help in detecting and mitigating risks. With the help of predictive analytics and machine learning capabilities, businesses can forecast potential threats or downturns and devise proactive strategies to counteract these risks.

With the rise of big data and advanced analytics technologies, enterprise analytics is also playing a significant role in innovation. Enterprises can leverage the insights gained from analytics to develop new products or services, improve business models, and create new revenue streams. Data modeling helps enterprises structure and visualize all your data for real-time insights and advanced analytics.

At this point, high-scale enterprise analytics is a table stake for any successful business.

Business data and intelligence

Your business runs on data—user behavior, site performance, and marketing results. While standard analytics tools show you what’s happening on your site, the real advantage comes from understanding why.

Enterprise analytics platforms give you the complete picture by pulling data from every source. This lets you move past surface-level metrics to spot trends, analyze complex behavior, and anticipate what customers will do next.

But raw data is just noise. The key is turning numbers into insights your team can use. With clear data visualization, you can spot patterns, track what matters, and share findings instantly. When everyone is on the same page, you make better decisions together.

Handling data responsibly builds trust. The best platforms are built with strict privacy and security standards, protecting your customers' information and your competitive edge.

Ultimately, truly understanding your audience is what sharpens marketing strategies, improves website performance, and keeps you ahead of the competition. It’s simple: good data leads to good decisions.

How to choose an enterprise web analytics tool

Picking a new analytics tool can feel like you're just comparing feature lists. But let's be honest—you're not just buying a tool. You're choosing a partner to help you understand your customers and build better digital experiences. The right platform does more than just show you charts and numbers; it helps you discover the hidden patterns in user behavior that lead to real breakthroughs.

So, how do you move past the buzzwords and find a solution that will actually help your teams?

Here’s what truly matters.

Can you get a complete and truthful picture of your data?

Your analytics platform should be your source of truth for what’s happening across your digital properties. This means it needs to do more than just scratch the surface with metrics like page views and bounce rates.

A great platform automatically captures all interactions, giving you a complete dataset to explore. It should bring together data from every touchpoint, so you can see the full story of your customers' experiences in one place, in real time. This is the foundation for uncovering actionable insights that drive smarter business decisions.

Does it help you understand the why behind user behavior?

Knowing what your users are doing is one thing. Knowing why is where the magic happens. Your analytics tool shouldn't just tell you that users are dropping off in your checkout funnel; it should show you exactly where and why they're getting stuck.

Look for a platform that moves beyond basic metrics to provide deep, qualitative insights into the user experience. This means being able to see how users navigate your site, what content they truly engage with, and where they show signs of frustration. Understanding this "digital body language" is the key to creating experiences your customers will love.

Will it grow and adapt with your business?

Every business is unique, and a one-size-fits-all analytics solution won't cut it. Your analytics partner should offer the flexibility to meet your specific needs today and the scalability to support you as your business evolves.

As you grow, your questions will become more complex. You need a tool that can grow with you, allowing you to dig deeper and answer the new, more challenging questions that will inevitably arise.

Does it connect with the tools you already use?

Your analytics platform shouldn't live on an island. To get a truly holistic view of your business, it needs to connect seamlessly with the other tools in your marketing and development stacks, like your CRM, A/B testing, and marketing automation platforms.

This integration breaks down data silos and ensures that every team—from product to marketing to engineering—is working from a single source of truth, fostering collaboration and more cohesive strategies.

Is it built on a foundation of trust and privacy?

In an era of growing data privacy concerns, respecting your users' privacy is non-negotiable.

The platform you choose must be committed to the highest standards of data privacy and compliance, with robust features to protect user information. This isn't just about avoiding fines; it's about building and maintaining trust with your customers.

Is there a team of experts to help you succeed?

Finally, consider the people behind the platform. A great analytics partner offers more than just a software license. They provide prompt, effective support and training to help you and your team get the most out of the tool.

Look for a partner who is invested in your success and will be there to help you overcome challenges and achieve your goals.

Choosing an analytics platform is a major decision, but by focusing on these core principles, you can find a partner that will empower your team to build better, more customer-centric digital experiences.

Best web analytics for enterprise products

Fullstory

Fullstory has proven itself to be a reliable enterprise analytics solution for businesses seeking to gain deep insights into user behavior on their websites and apps.

Fullstory's standout feature is its ability to autocapture every user interaction. Unlike other platforms that often just tell you "what" happened, Fullstory dives deeper, also uncovering the "why" behind issues and opportunities. This lets your team to understand key metrics and track them over time, helping you make informed decisions.

Key features of Fullstory:

Fullstory autocaptures every interaction, going beyond simply reporting "what" happened to uncover the "why" behind issues and opportunities. This helps your team understand key metrics and make informed decisions.
Fullstory is easy to implement, offering immediate access to insights without slowing down your site.
It prioritizes user privacy by masking text elements to protect their information.
Fullstory offers a comprehensive view of customer interactions, providing user insights to shape your product roadmap and unite teams around a single source of data.
It helps prioritize work effectively through features like funnel analysis, conversion opportunities, and diagnostic capabilities.
Fullstory seamlessly integrates with various tools and platforms, making it a valuable part of your digital ecosystem.
Fullstory is a comprehensive product analytics solution that empowers teams, optimizes work, and helps you build user-friendly products.

Google Analytics

Google Analytics is one of the most popular web analytics tools and an enterprise analytics platform widely used for tracking website traffic and user behavior.

Some organizations may prefer to store data on their own servers using certain web analytics tools, which allows for greater control over data privacy and compliance with strict data protection laws.

Pros of Google Analytics:

Widely used and popular for tracking website traffic and user behaviors
Offers real-time data tracking and custom reports
Advanced features like BigQuery Export enable in-depth data analysis.
Consultative services from dedicated experts are available.

Adobe Analytics is a tool that enables organizations to analyze data from various customer touchpoints and provides insights into customer behaviors on your website.

Advanced features like AI and segmentation in Adobe Analytics offer deeper insights into customer behavior, going beyond basic reporting.

Pros of Adobe Analytics:

Good for gaining insights into customer behaviors
Features like segmentation, time-series analysis, and machine learning
Allows businesses to uncover meaningful patterns and make data-driven decisions

Mixpanel

Mixpanel helps track user interactions on your website.

Pros of Mixpanel:

Friendly interface for tracking user interactions with your website
Focus on event-based analytics, allowing you to track and analyze individual steps
Provides insights into user behavior throughout their journey on your website

Amplitude

Amplitude is a product analytics tool known for its comprehensive insights into user behavior.

Pros of Amplitude:

Insights into user behavior for measuring user engagement and retention
Powerful analytics capabilities to drive growth
Analytics, user journey tracking, and segmentation

Other types of useful analytics tools

While web analytics tools provide valuable insights into your website’s performance and user behavior, several other types of analytics tools can also help you gain a comprehensive understanding of your online presence. Analytics tools can also help track and optimize marketing channels, allowing you to assess the effectiveness of different platforms and improve overall campaign performance.

Behavioral analytics tools

Behavioral analytics tools, like Fullstory, go even further beyond traditional metrics to uncover how users actually interact with your digital experience. Instead of focusing only on traffic volume or pageviews, these platforms capture user sessions, clicks, scrolls, rage clicks, and even hesitation.

This allows teams to diagnose friction points, understand what drives conversions (or drop-offs), and improve experiences with precision. Unlike basic analytics dashboards, behavioral analytics tools help teams visualize the user journey in real-time or retroactively, making it easier to resolve UX issues and prioritize impactful changes.

Pros of behavioral analytics tools:

Deliver a deeper understanding of how users navigate and interact with your product or site
Help pinpoint bugs, confusing UI elements, or underperforming features
Enable cross-functional teams (product, marketing, UX, dev) to collaborate with shared context
Accelerate iteration and experimentation by showing why users behave the way they do

These tools are essential for any organization serious about user experience—and for turning behavioral data into action.

SEO analytics tools

SEO analytics tools, such as Ahrefs, are critical components in businesses'’ digital marketing arsenals, specifically designed to support search engine optimization efforts. They help optimize websites for superior search engine performance and delve into the intricate world of SEO, providing vital insights into keyword rankings, backlinks, site structure, and even competitor strategies.

By closely monitoring competitors, you can uncover effective SEO practices and trends, positioning your website for improved search engine rankings. Ultimately, these tools play a crucial role in your digital strategy, supporting higher visibility, increased traffic, and potentially, improved conversions.

Social media analytics tools

Social media analytics tools such as Sprout Social and BuzzSumo allow businesses to track their social media performance. They offer insights into post engagement, audience demographics, optimal posting times, and competitor performance, facilitating the creation of effective social media strategies that enhance brand visibility and customer engagement.

A/B and multivariate testing tools

Multivariate and A/B testing tools, like industry front-runners Optimizely and AB Tasty, are essential for businesses aiming to deliver the most effective user experience. These tools facilitate experimentation with different versions of web pages or features, enabling businesses to discern which iteration performs best with their target audience.

These tools go even deeper by providing data-driven insights into user preferences and behaviors, painting a comprehensive picture of user interactions.

Pros of A/B testing tools:

Businesses can make informed decisions to optimize the user experience.
Aligning the user experience with expectations and needs leads to improved engagement.
It also reduces bounce rates and increases conversions.
These tools are essential for continuous improvement and staying competitive in the digital landscape.

Content analytics tools

Content analytics tools are crucial for measuring and understanding your content's impact. They track metrics like page views, dwell time, shares, and comments to gauge user engagement. This helps identify resonating content and guides your strategy. By analyzing trends and patterns in user interaction, these tools provide evidence-based insights to refine your content.

Whether it's blog posts, videos, or infographics, they give you the knowledge to meet audience expectations. The outcome is more engaging content, heightened user satisfaction, and a stronger brand image.

Getting your leaders on board

You know that the right analytics platform can be a game-changer. But to get it, you need to convince the people who hold the purse strings. Getting executive buy-in means showing each leader how a deeper understanding of your customers helps them win.Here’s how to frame the conversation for each key stakeholder:

For your CFO: Connect insights to ROI

Your CFO wants to see the bottom line. Connect the dots between behavioral data and financial results. Show how seeing the why behind user actions can directly impact revenue.

Highlight the financial wins: Explain how uncovering points of friction in your checkout funnel can reduce cart abandonment and increase conversion rates.
Talk efficiency: Demonstrate how a self-serve platform empowers teams to answer their own questions, reducing reliance on data science and engineering resources.
Prove the value: Use data visualizations to make the financial impact clear, turning abstract insights into tangible ROI.

For your CIO: Focus on efficiency and security

Your CIO cares about a secure, efficient, and integrated tech stack. Frame your pitch around how the right analytics platform makes the entire ecosystem stronger.

Emphasize better data governance: A single source of truth for behavioral data means more reliable and secure information across the board.
Showcase engineering efficiency: Explain how a codeless solution frees up developers from endless manual tracking and lets them focus on building better products.
Highlight seamless integration: Stress how the platform will connect with your existing systems (like your CRM and A/B testing tools), maximizing the value of your current tech investments.

For your CEO: Paint the strategic picture

Your CEO is focused on the big picture: market position, competitive advantage, and long-term growth. Show them how deep customer insights are the key to a winning strategy.

Connect data to strategy: Explain how understanding customer behavior allows you to build better products, create more effective marketing, and stay ahead of the competition.
Empower the team: Frame it as a tool that gives everyone—from product managers to marketers—the insights they need to make smarter, faster decisions.
Suggest a pilot project: Propose a small, focused project to demonstrate the platform’s value quickly. A quick win can build the momentum needed for a full investment and show how this technology will drive the business forward.

Frequently asked questions

Why is web analytics important?

Web analytics is essential as it provides insights into how visitors engage with your website. It helps track visitor behavior, identify popular content, understand user journeys, and evaluate marketing performance. These insights inform business decisions, improve user experience, enhance marketing strategies, and increase conversion rates. Without web analytics, businesses would rely on assumptions instead of data, potentially harming their success.

What is enterprise-wide analytics technology?

Enterprise-wide analytics technology refers to systems that collect, analyze, and present data from all departments of an organization. They offer a complete view of business operations and customer interactions, enabling data-driven decision-making. These systems incorporate different types of analytics to provide a holistic understanding of the business, leading to improved efficiency, customer understanding, and business growth.

14 of the best real-time analytics platforms in 2026

Fullstory — Mon, 22 Jun 2026 13:00:00 GMT

For product, UX, and data teams, the window between when something goes wrong and when you find out is where conversions are lost. Real-time analytics platforms close it by capturing and processing user behavior as it happens, so teams can act before friction drives up cart abandonment and compounds into revenue impact.

The platforms on this list vary widely in how they deliver on the real-time promise, ranging from batch-processed data and surface-level dashboards to true real-time capture with deep behavioral context. Below, we cover the best options on the market and how to choose the right one for your team.

What is a real-time analytics platform?

A real-time analytics platform captures user interactions as they happen and makes that data immediately available for analysis, without batch-processing delays. However, some of the tools marketed as "real-time" don't actually deliver data in real time. In practice, platforms fall into three categories:

True real-time: Sub-second to seconds
Near-real-time: A delay of several minutes
Batch processing: Lags of hours or days, common among older analytics tools

For product and UX teams, For product and UX teams, which category a platform falls into determines how quickly you can act. Catching a bug in seconds means addressing it before it affects thousands of sessions. Spotting a conversion drop-off in real time means intervening before it compounds.

The platforms below vary significantly in how "real-time" they actually are, and that's one of the key criteria to weigh when evaluating each one.

Best real-time analytics platforms overview

Every platform below offers analytics, but they differ significantly in what they're built for, who they're best suited to, and what 'real-time' means.

Tool	Best for	Note	Real-time capability
Fullstory	Full-fidelity user behavior and product analytics, UX debugging, combining qualitative + quantitative data	More powerful than basic tools; best value at scale	~5-second data capture; 1-5-second latency for active sessions
Google Analytics	Free traffic, audience, and conversion tracking	Limited behavioral depth, support, and flexibility	30-minute real-time report; standard reports 24-48 hour delay
Adobe Analytics	Enterprise-grade attribution and multichannel analysis	Steep learning curve, heavier implementation	Intraday data available within a few hours
Amplitude	Retention analysis, cohort tracking, and product-led growth	MTU pricing scales quickly; experimentation is a paid add-on	p95 event delivery within 60 seconds
Mixpanel	Event-based product analytics and user segmentation	No autocapture; complex pricing	Events appear within minutes; server-side tracking can add delays
PostHog	Developer-first open-source analytics with full data control	Setup is engineering-heavy; smaller enterprise feature set	Events populate within seconds to minutes; can spike under load
Heap	Retroactive event analysis without manual setup	Now part of Contentsquare; pricing not transparent at scale	Data available within minutes; not true real-time streaming
Hotjar	Heatmaps, surveys, and lightweight UX feedback	Shallow quantitative analysis; privacy considerations	No real-time dashboard; data available after sessions complete
LogRocket	Developer-focused debugging and frontend error monitoring	Thin product analytics; primarily an engineering tool	Error alerts and session data available within seconds
Contentsquare	Digital experience analytics for enterprise teams	Broader platform than Heap alone; enterprise pricing	Near real-time; session data available within minutes
Optimizely	Advanced experimentation and personalization	Expensive for smaller teams	Results update within minutes
AB Tasty	Easy-to-use A/B testing and targeting	Reporting depth is limited	Results update within minutes
Ahrefs	Backlink analysis, keyword research, competitor insights	Expensive	Not real-time; rank data updated daily to weekly
Semrush	SEO, PPC, and competitive intelligence	Higher pricing tiers	Not real-time; rank tracking updated daily

1. Fullstory

Category: Product analytics

Best for: Real-time behavioral data at scale

Fullstory is a behavioral data and product analytics platform that gives businesses deep, user-friendly visibility into how customers behave across their websites and apps. At the core is Fullcapture™, which records user interactions without screen recording, giving teams immediate behavioral context.

Fullcapture references your site's DOM to simulate the user experience rather than relying on screen recordings or partial event tracking, capturing every interaction without compromising site performance or user privacy. Since all past interactions are stored, once you set up an event, funnel, or conversion, you get insights instantly.

Fullstory also provides a range of tools and features that help businesses optimize their mobile app or website, including:

Heatmaps: See exactly where users are clicking, scrolling, and engaging across your site
Conversion funnels: Identify where users drop off and take action before it compounds
Advanced analytics: Get the data-driven solutions you need to improve the user experience
StoryAI: Surfaces rage clicks, dead clicks, form abandonment, and friction patterns automatically, flagged and prioritized by business impact without requiring teams to know what to look for in advance

That combination makes Fullstory the strongest option on this list for teams that need real-time behavioral depth and quantitative analytics in a single, accessible platform.

Pros

Complete user session capture and replay
Data-driven insights for user experience
Highly customizable platform
Advanced analytics and optimization tools
Combines qualitative behavioral data with quantitative analytics
Automated friction detection without manual analysis

Pricing

Pricing is customized based on team size, data volume, and feature needs. Visit the pricing page for details.

Review

“The session replay quality and filtering capabilities are excellent. Being able to watch actual customer behaviour at scale rather than relying on aggregated metrics has fundamentally changed how we approach UX research. The search functionality lets us quickly isolate specific user segments or problematic journeys, which is invaluable for both understanding friction points and reproducing bugs that customers report.”
- Lee A., G2 Review

2. Google Analytics

Category: Product analytics

Best for: Free baseline traffic analytics

Google Analytics is a popular self-service business intelligence platform that offers free access to data visualization features. It’s often used by companies that don’t want or cannot afford more comprehensive analytics.

It provides insight into audience demographics, traffic sources, and on-site behavior, though it stops short of explaining why users behave the way they do.

Although Google Analytics has limited customer support and data sources compared to its competitors, it is still one of the best digital analytics tools in terms of usage. It is often used in conjunction with other, more robust behavioral analytics tools, like Fullstory, to get the full picture.

Pros

Free to use
Exceptional website and mobile app analytics platform
Valuable insights for marketers
Customizable dashboards and reports

Cons

Limited customer support
Relies mostly on third-party training
Standard reports lag 24 to 48 hours
No behavioral depth or friction detection

Pricing

Free for standard use
Google Analytics 360 (enterprise): contact Google for pricing

Review

“Google Analytics has been the standard for free website analytics for the 2+ decades I've been in the industry. It has changed quite a bit over that time, but it has always provided powerful analytical tools at no cost to the user.”
- Lindsey M., G2 Review

3. Adobe Analytics

Category: Product analytics

Best for: Enterprise multichannel data analysis

Adobe Analytics is a marketing attribution software that helps businesses understand and measure the impact of their customer interactions. It offers a range of tools, including web analytics, marketing analytics, attribution, predictive analytics, multichannel data collection, and more.

One of the most useful features of Adobe Analytics is the Analysis Workspace. Users can perform ad hoc analysis by dragging and dropping data tables, visualizations, and components.

That power comes with tradeoffs. Adobe Analytics has a steep learning curve, heavy implementation requirements, and a cost structure that makes it enterprise-only in practice. Best paired with a dedicated behavioral platform for teams that need to go deeper.

Learn more about giving your Adobe Analytics setup digital sight with Fullstory.

Pros

Real-time data analysis
Multichannel data collection
Anomaly detection technology
Integration with other applications

Cons

Steep learning curve
Heavy implementation requirements
Enterprise-only cost structure
Not a behavioral analytics tool

Pricing

Custom enterprise pricing: contact Adobe directly for details.

Review

“What I like best about Adobe Analytics is the depth of data and customization it offers. It allows businesses to track customer behavior across multiple channels and build very detailed reports tailored to their goals. The segmentation tools are powerful, and the real-time insights help teams make faster marketing decisions. I also appreciate how well it integrates with other Adobe products, making it easier to manage campaigns and understand the full customer journey in one ecosystem.”
- Emmanuel C. J., G2 Review

4. Amplitude

Category: Product analytics

Best for: Retention and cohort analysis

Amplitude is the category leader for event-based product analytics, with near-real-time event streaming at p95 latency of 60 seconds, meaning user actions populate dashboards within about a minute.

Strengths include retention analysis, behavioral cohorting, predictive analytics, and an expanding suite that now includes feature flags and A/B testing through Amplitude Experiment.

It's the strongest fit for product managers and growth teams at mid-to-large SaaS companies, though Amplitude's MTU-based pricing model can get expensive at scale, and advanced features like experimentation are separate paid add-ons.

For a deeper look at how the two platforms compare, see our guide on Fullstory vs. Amplitude.

Pros

Near real-time event streaming with p95 latency of 60 seconds
Best-in-class retention and cohort analysis
Expanding suite: feature flags and experimentation now included
Strong data warehouse integrations (Snowflake, BigQuery)

Cons

MTU pricing model can become expensive at scale
Advanced features like experimentation are separate paid add-ons
Steeper learning curve for non-technical users

Pricing

Free plan available
Plus plan available for growing teams
Enterprise plan with custom pricing: visit the Amplitude pricing page for details

Review

“Amplitude works well as a shared source of truth across marketing, data, and engineering. The same event taxonomy that drives Braze campaigns also powers the analytics dashboards the wider business looks at, so when a campaign isn't behaving as expected, everyone can investigate in the same tool.”
- Sirigiri H., G2 Review

5. Mixpanel

Category: Product analytics

Best for: Event tracking and segmentation

Mixpanel is an analytics platform designed for businesses to understand how users interact with their products. It has features that allow companies to track user behavior, analyze data, and make data-driven decisions.

Mixpanel can track events in real-time to help businesses identify and respond to issues, such as user drop-off, more quickly.

One potential downside of Mixpanel is that its pricing structure can be a bit complex and may not be suitable for smaller businesses with limited budgets. Additionally, some users have reported that the learning curve for using the platform can be steep.

Pros

Real-time event tracking
Advanced data visualization
User segmentation capabilities
Personalized messaging and content

Cons

Complex pricing structure
Limited dashboard capabilities
Messaging features can be weak
Some users report missing data or data delay
No autocapture or session replay

Pricing

Free plan available for small teams
Plus plan available for growing teams
Enterprise plan with custom pricing

Contact Amplitude for more details.

Review

“I love the super clean and easy-to-use UI of Mixpanel, which means everyone on the team can answer their data questions without needing a data scientist or using SQL queries. It's practical for everyone, even without a technical background, since it's super user-friendly and easy to learn. I find the live data updates particularly valuable, allowing us to see live updates as things go on and helping with testing things to ensure they work correctly and diagnosing issues. This is paramount at an eCommerce company. Mixpanel consistently makes great and actually useful updates. Initial setup was very easy, and with 100+ people using it at my company, it's been practical for all.”
- Aaron T., G2 Review

6. PostHog

Category: Product analytics

Best for: Developer-first data ownership

PostHog is the developer-first alternative for teams that want full control over their data infrastructure. It's open-source, self-hostable, and packages product analytics, feature flags, A/B testing, and surveys into a single platform with near-real-time event capture included.

Events populate dashboards within seconds to minutes, though latency can spike under heavy load. It's the strongest fit for technical teams and startups that prioritize data ownership and transparent pricing over polish and enterprise support.

Pros

Open-source with a self-hosting option for full data ownership
Near real-time event capture built in
All-in-one: analytics, feature flags, experiments, and surveys without additional vendors
Transparent, usage-based pricing with a generous free tier

Cons

Developer-heavy setup; less accessible for non-technical teams
UI is less polished than Amplitude or Mixpanel for non-technical PMs
Community support rather than dedicated customer success at lower tiers
Smaller enterprise feature set compared to Amplitude or Fullstory

Pricing

Free tier available with generous usage limits
Paid plans scale on usage
Self-hosted option available at no platform cost

Visit the PostHog pricing page for more details.

Review

“What I like most about PostHog is how much visibility it gives into user behavior without needing multiple tools. We use it to track events, analyze funnels, understand drop-off points, and monitor product usage in one place. Session recordings and feature-level insights are especially helpful because they help us understand not only what users do, but also why they do it. It is flexible and provides a lot of value for product and growth teams.”
- Mahdi H., G2 Review

7. Heap

Category: Product analytics

Best for: Retroactive event analysis

Heap records every user interaction automatically, letting teams define events retroactively using a visual editor rather than instrumentation in advance. That autocapture model removes the need for a pre-planned event taxonomy and makes historical analysis available the moment you need it, without re-instrumenting your product.

Since the Contentsquare acquisition in 2023, Heap has been integrated into the broader Contentsquare platform, giving buyers access to a wider digital experience suite but introducing some roadmap uncertainty for standalone users.

Data is available within minutes of capture, not true real-time streaming, making it the strongest fit for teams without dedicated data engineers who want to avoid a lengthy tracking setup.

Pros

Autocapture removes the need for a pre-planned event taxonomy
Retroactive analysis without losing historical data
Visual event tagging is accessible to non-technical team members
Access to broader Contentsquare ecosystem post-acquisitio

Cons

Not true real-time; data available within minutes
Data governance complexity at scale due to autocaptured event volume
Cost can be high at volume; pricing not fully transparent
Roadmap uncertainty for standalone Heap users

Pricing

Free trial available
Enterprise pricing available through Contentsquare
Paid plans based on usage and session volume

Review

“I like that heap is so easy for non-technical team members to be able to create tracking events without the need for any coding knowledge or getting the engineering team involved.”
- Milos D., G2 Review

8. Hotjar

Category: Product analytics

Best for: Lightweight heatmaps and feedback

Hotjar is an analytics and user feedback tool that combines heatmaps, feedback polls, and surveys in a single platform. It's a popular entry point for teams getting started with behavioral data, though its quantitative analysis is shallow by design, conversion funnel and form analytics offer a general overview rather than deep diagnostic insight.

On the real-time front, Hotjar has no live dashboard. Heatmaps and recordings are available after sessions are complete, not as they happen. Combined with screen recording privacy considerations, this factor makes Hotjar a tool most teams outgrow as their product and data needs scale. For teams that need behavioral depth and real-time visibility, Fullstory is the natural next step.

Pros

User-friendly interface
Powerful feedback collection features
All-in-one platform saves time and money

Cons

Limited reporting and analysis capabilities
Simple user feedback features don’t provide robust quantitative analysis
Screen recording could pose a risk to user privacy

Pricing

Free plan available with limited features
Plus plan starting at $39/month
Business plan starting at $99/month
Scale plan with custom pricing for larger teams

Review

“Hotjar makes it easy to understand how users actually interact with site through clear heatmaps and session recordings. Everything is simple to set up, and the insights are genuinely useful for improving user experience quickly.”
- Kanti G., G2 Review

9. LogRocket

Category: Product analytics

Best for: Engineering-focused debugging and monitoring

LogRocket is built for engineering teams that need real-time visibility into frontend errors and performance issues. Where Fullstory is designed for product and UX teams, LogRocket skews toward developers with built-in error tracking, network request logging, and Redux/Vuex state capture.

Error alerts fire within seconds as frontend issues occur, and Galileo AI proactively surfaces high-impact issues with severity scoring so engineers can prioritize without manual triage.

It's the strongest fit for teams where engineers drive debugging and performance-optimization workflows, though its product analytics layer is thin compared to dedicated platforms.

Pros

Near real-time frontend error monitoring; alerts within seconds
Strong developer tooling: Redux/Vuex state capture, console logs, and performance metrics
Product analytics features included
Galileo AI proactively surfaces high-impact issues with severity scoring

Cons

Thin product analytics; primarily an engineering tool
Less suited for non-technical product or UX teams
Pricing scales with volume and can get costly
Fewer integrations than broader platforms like Fullstory or Amplitude

Pricing

Free plan available with limited sessions
Team plan with custom pricing based on session volume
Enterprise plan available, contact LogRocket for details

Review

“I like how easy it is to get started to understand the platform flows. I work as a software engineer, and my most common use case is to track and evaluate how the user got into a bug or issue in our platform. We combine it with Sentry to have a pretty clear picture of the bug.
Its Session Replay works smoothly even for long sessions, having what the user saw and the api calls on the same view is really useful to have the most important things to do my work efficiently.”
- Oscar B., G2 Review

10. Contentsquare

Category: Product analytics

Best for: Enterprise-scale experience analytics

Contentsquare is an enterprise-grade digital experience platform combining zone-based heatmaps, journey analysis, and experience analytics across large, complex digital properties. With Heap now part of the ecosystem, the platform adds autocapture and retroactive event analysis to an already broad suite.

Session data is available within minutes, not true real-time streaming, making it the strongest fit for large enterprises in retail, financial services, and travel with dedicated analytics teams. See how Fullstory compares to Contentsquare or browse other Contentsquare alternatives worth evaluating.

Pros

Enterprise-grade digital experience analytics at scale
Zone-based heatmaps and journey analysis go beyond standard click tracking
Heap integration adds autocapture and retroactive event analysis to the suite
Strong compliance and privacy features for regulated industries

Cons

Enterprise pricing; not accessible for smaller teams
Implementation complexity requires dedicated resources
Not true real-time; data available within minutes
Overkill for teams that only need core product analytics

Pricing

Custom enterprise pricing: contact Contentsquare for details.

Review

“I like how Contentsquare provides detailed behavior analysis of our targeted users on both the website and the app. The visual tools - such as heatmaps, session replays, and journey analysis - give us clear, in-depth insights into what users are doing. I also enjoy creating dashboards, and I appreciate how easy it is to build them while still keeping the level of detail we need.”
- Manoj B., G2 Review

11. Optimizely

Category: A/B testing & CRO

Best for: Advanced product experimentation

Optimizely is a powerful digital optimization platform that lets businesses run experiments, gather data, and make data-driven decisions to improve their online customer experience. The platform provides a range of features and tools for A/B testing, personalization, and analytics, allowing businesses to optimize their websites, mobile apps, and other digital experiences.

One of Optimizely's strengths is its ease of use. The platform is designed to be user-friendly, with a drag-and-drop interface that allows even non-technical users to create and run experiments. Moreover, Optimizely’s integration with popular marketing and customer experience analytics tools makes it easy to analyze and act on experiment results.

Another key feature of Optimizely is its ability to personalize experiences for individual users. The platform uses machine learning algorithms to tailor content and experiences based on user behavior and other data, improving engagement and optimizing conversion rates.

Pros

User-friendly interface
Integrates with popular tools
Machine learning-powered personalization
Robust feature set

Cons

Can be expensive
Limited behavioral depth outside of experimentation
Requires pairing with a dedicated analytics tool for full visibility

Pricing

Custom pricing based on team size and feature needs: contact Optimizely for details.

Review

“Optimizely One makes it easy for our teams to manage content, boost engagement, and be more agile all in one place!”
- Evie L., G2 Review

12. AB Tasty

Category: A/B testing & CRO

Best for: Easy no-code experimentation

AB Tasty is a user-friendly A/B testing and CRO platform that helps businesses improve their digital experience and drive revenue growth. The platform is equipped with A/B testing, split URL testing, personalization, funnel analysis, and more to help businesses understand their customers’ behavior and optimize their websites accordingly.

AB Tasty stands out from the crowd with its user-friendly interface. Without any coding knowledge, users can effortlessly create and launch tests and campaigns for their customers. Furthermore, businesses have access to user segmentation and targeting tools to personalize experiences for certain customer segments; this helps increase engagement rates as well as conversions.

AB Tasty makes it simple to incorporate into your existing marketing stack thanks to its extensive selection of integrations with other popular marketing tools and platforms.

Pros

User-friendly interface
Personalization and targeting capabilities
A/B testing and funnel analysis
Integrates with other marketing tools

Cons

Limited reporting capabilities
No behavioral depth or session-level context
Requires pairing with a dedicated analytics tool for full visibility
Can be expensive for smaller teams

Pricing

Custom pricing based on team size and feature needs: contact AB Tasty for details.

Review

“A big advantage of AB Tasty is that it’s an all-in-one platform. Instead of juggling multiple tools, AB Tasty brings together testing, personalization, feature experimentation, and user segmentation in one place.”
- Aliya S., G2 Review

13. Ahrefs

Category: SEO analytics

Best for: Backlink and keyword research

Ahrefs is an all-inclusive search engine optimization toolset that offers a myriad of functions to manage and optimize search engine analytics. Through keyword research, competitor analysis, content assessment tools, rank-tracking capabilities, and more, Ahrefs purports to be a one-stop SEO solution for many businesses.

If you’re looking for a powerful tool to improve your website’s link profile and rankings, there’s the backlink assessor. This feature provides detailed insight into both the quality and quantity of links pointing to your site. Utilizing this feature can help identify areas where you can develop better connections while boosting your rank higher than ever before.

Ahrefs’ keyword research tool is one of its most powerful features, offering an abundance of data on search volumes, difficulty levels, and related keywords. This allows you to easily identify high-value keywords and supplement your content for optimum SEO performance.

Pros

Comprehensive SEO toolset
Detailed backlink analysis
Robust keyword research
Valuable competitor research

Cons

High monthly cost
Steep learning curve for new users
No behavioral or on-site analytics
Not a real-time analytics tool

Pricing

Lite plan starting at $129/month
Standard plan starting at $249/month
Advanced plan starting at $449/month
Enterprise plan with custom pricing

Review

“I have been using Ahrefs for the last 6 years, and it’s one of the best tools for SEO keyword research. It provides very accurate data on my current rankings, keyword trends, search volumes, and difficulty. Recently, Ahrefs also launched its AI visibility tracking feature, which is useful for tracking our visibility in AI overviews, ChatGPT, and similar platforms.”
- Aditee K., G2 Review

14. Semrush

Category: SEO analytics

Best for: SEO and competitor analysis

Semrush is another popular SEO tool that enables users to conduct detailed keyword research, evaluate competitor campaigns, and monitor website rankings across all major search engines.

The platform also gives businesses a fuller picture of the competitive landscape, surfacing competitors' ad types, target keywords, and copy in one place.

SEMrush also provides a comprehensive site audit tool that helps users identify technical issues and opportunities for improvement on their website. This includes analyzing page load speed, identifying broken links, and flagging duplicate content issues.

Pros

Comprehensive suite of SEO tools
Detailed competitor analysis
User-friendly interface
Customizable reporting options

Cons

Higher pricing compared to some competitors
No behavioral or on-site analytics
Not a real-time analytics tool
Can be overwhelming for new users

Pricing

Pro plan starting at $117/month
Guru plan starting at $208/month
Business plan starting at $416/month
Enterprise plan with custom pricing, contact SEMrush for details

Review

“SEMRush is the goat of SEO software that I've been using for 15 years. It's easy to use, and has robust features to help me manage most aspects of client deliverables, including audits, keyword research, competitive research, content optimization, and AI insights.”
- Melissa K., G2 Review

How to choose the right real-time analytics platform

Once you know what type of data and insights you need, it's time to shop around. Start by answering these three questions:

What do you need to see in real time? Traffic, event-based actions, and behavioral context are distinct, and not every platform delivers all three.
Who will use the data? Engineers, product managers, and UX teams have different workflows and setup tolerances.
What's your tolerance for complexity and cost? Factor in implementation lift and how pricing scales with data volume.

From there, match your needs to the right category:

Category	Tools	Best for
Behavioral analytics	Fullstory, Contentsquare	Friction detection, session context, UX depth
Event-based analytics	Amplitude, Mixpanel, PostHog	Funnels, retention, experimentation
Autocapture	Heap	Retroactive analysis, minimal instrumentation
Lightweight UX	Hotjar	Heatmaps, feedback, entry-level teams
Developer-focused	LogRocket	Frontend debugging, error monitoring

Then follow these steps to evaluate your shortlist:

Research: Not all platforms offer the same real-time capabilities, so compare options carefully before narrowing down your list.
Define your insights: Make sure any tool you evaluate delivers the specific data your team needs to act on.
Get recommendations: Colleagues and industry experts can tell you what works in practice, not just on paper.
Test before you commit: Most vendors offer demos or free trials. Use it to validate real-time data accuracy and confirm the platform fits your team's workflow before making a long-term decision.

Why Fullstory leads for real-time behavioral analytics

Most analytics platforms show you events and numbers, but can't tell you what was actually happening when those numbers moved. That's the gap that real-time analytics platforms built solely around event tracking can't close.

Fullstory closes it with a combination of real-time capture and behavioral depth that event-based tools can't match:

Fullcapture: Automatically records every interaction without a tracking plan, retroactively queryable from the moment it's deployed
StoryAI: Surfaces rage clicks, dead clicks, and form abandonment in real time without requiring teams to know what to look for in advance

The result is faster friction detection, faster fixes, and fewer sessions lost to problems that could have been caught in the moment, making Fullstory the strongest option on this list for teams that need real-time behavioral analytics at enterprise scale.

How Fullstory powers comprehensive, compliant, and collaborative AI

The Fullstory Team — Tue, 11 Feb 2025 06:00:00 GMT

AI-driven personalization is reshaping digital experiences, but businesses need rich, structured data to unlock its full potential. Many struggle with fragmented data collection, limiting AI’s effectiveness. That’s where Fullstory stands out.

Fullstory leads the AI transformation in two key ways:

Providing a complete, structured dataset of user behavior that seamlessly integrates with AI/ML tools.
Developing AI-powered features that deliver real-time insights and predictive analytics.

The future of digital experience depends on leveraging AI effectively. Fullstory is paving the way.

The data completeness advantage

Fullstory captures every user interaction—from scrolls and hesitations to errors—creating a high-resolution view of behavior. The result? Sharper insights and more accurate AI/ML models that continuously improve over time.

Fullstory competitors rely on manual tagging and fragmented data collection, such as:

Mixpanel & Amplitude: Manual event tagging.
Quantum Metric & Contentsquare: Custom scripting.
Pendo & Heap: In-platform tagging.

These approaches lead to incomplete datasets, increased human error, and resource-intensive processes—ultimately limiting AI’s effectiveness. In contrast, Fullstory’s automated, comprehensive data capture ensures AI models are trained on complete, unbiased information, enabling real-time personalization and superior decision-making.

Privacy-compliant AI

AI innovation must align with evolving data privacy regulations. Fullstory prioritizes privacy and security, empowering businesses to harness AI while maintaining compliance.

Many competitors struggle with rigid privacy frameworks, exposing businesses to data breaches and regulatory fines. Without a strong foundation in privacy, companies risk eroding customer trust and falling behind in AI adoption. Fullstory’s privacy-first approach ensures brands can confidently deploy AI without compromising security.

Cross-functional AI collaboration

Fullstory breaks down silos by providing a unified platform for product, marketing, and sales teams to analyze user behavior collaboratively. This cross-functional approach fuels richer datasets for AI/ML training, leading to more effective AI-driven features.

In contrast, many competitors operate in silos:

Pendo: Focuses on product insights.
Hotjar: Caters to marketers and UX teams.
Datadog: Specializes in support monitoring.

These narrow approaches limit AI’s ability to generate holistic insights. Fullstory’s broad, integrated dataset provides a more complete picture of user interactions, enabling businesses to train AI models on truly representative data.

Leading the AI-powered digital experience revolution

Fullstory stands at the forefront of AI-powered digital experiences, thanks to:

Comprehensive data capture: Eliminates reliance on manual tagging, ensuring richer datasets.
Privacy-first AI: Enables compliance while driving innovation.
Cross-functional collaboration: Unites teams to maximize AI’s potential.

While competitors struggle with incomplete data, privacy concerns, and siloed insights, Fullstory delivers a holistic, AI-ready foundation. Businesses seeking to unlock AI’s true power need a platform that captures every interaction, safeguards user privacy, and fosters collaboration.

That’s why Fullstory is the ideal partner for AI-driven success. Interested in learning more? Request a demo for a personalized conversation with an expert.

Top customer experience analytics tools and how to choose the best ones

Fullstory — Tue, 16 Jun 2026 15:25:00 GMT

Many CX teams have plenty of data from support tickets, surveys, chat logs, and other sources, but still struggle to understand why customers churn or where journeys break down. Customer experience analytics tools can help answer these questions.

They collect, connect, and analyze customer data across touchpoints, so teams can understand behavior, measure satisfaction, and learn what drives retention and conversions.

However, different CX analytics tools often track different things: customer feedback, support performance, behavioral data, or something else entirely, which makes it difficult to understand which tool is actually a good fit.

This guide will help you cut through the noise. Whether you're a CX manager, marketer, or product lead, you'll find the leading options across categories here, plus learn how to evaluate them.

Best customer experience analytics tools compared

Use this overview as a starting point for evaluation.

Tool	Best for	Key features
Fullstory	Behavioral data and digital experience analytics	Behavioral data Friction detection Funnel analysis
Amplitude	Self-serve product teams running heavy event-based experimentation	Event tracking Cohort analysis A/B testing
Contentsquare	Enterprise marketing teams focused on web merchandising and zone-based analysis	Zone-based analysis Frustration scoring Journey analysis
Mixpanel	Smaller teams that prefer a self-serve, low-configuration setup.	Event tracking Cohort analysis Custom dashboards
Qualtrics XM	Large enterprises running structured survey and research programs	NPS/CSAT programs Text analytics Closed-loop feedback workflows
Medallia Experience Cloud	Multi-brand organizations consolidating feedback across offline and online channels	Real-time feedback capture Role-based dashboards Text analytics
Pendo	SaaS product managers running in-app guides and onboarding flows	In-app guides Product analytics Feature usage tracking
Zendesk	Support and service teams analyzing ticket data within their helpdesk	Pre-built CX dashboards Post-interaction surveys Support trend reporting

Fullstory

Fullstory is the top choice for understanding users' behavior and experience across digital platforms, such as your website and mobile apps. It delivers privacy-first records of user interactions, extracts critical behavioral signals from each, and converts them into easy-to-review summaries.

Its AI-powered autocapture solution, Fullcapture, automatically logs behavioral data across web and mobile. It doesn't require manual tagging to index interactions, which means you don't have to define and tag every interaction you want to track in advance.

You can simply analyze previous sessions at any point using new criteria. This ensures no data gets lost, even with minimal upfront configuration.

Key capabilities:

Behavioral data: Shows how users interact with your website or app: where they click, what errors they experience, what pages they navigate to, and more.
Friction detection: Captures error clicks, dead clicks, rage clicks, thrashed cursors, and other friction points that frustrate users.
StoryAI: Delivers AI-powered summaries and answers questions about user behavior and experience.
Session replay: Delivers compliance-grade, privacy-first reconstructions of every user interaction.
Funnel analysis: Combines quantitative data with session replays for end-to-end customer journey tracking.
Heatmaps: Shows how users move through your pages or app, what captures their attention, and what they scroll past.
Anomaly detection: Detects unusual behavior, such as increased drop-offs at critical conversion points, to help fix issues before they escalate.

Pros

Privacy-first, reconstructed session replays based on logged activities
Comprehensive behavioral data across all user interactions
AI-powered summaries for non-technical and busy teams
Unified qualitative and quantitative user insights
Retrospective querying
Granular indexing controls
Automatic sensitive data masking

Cons

No behavioral cohort analysis
No experimentation features

Amplitude

Amplitude is an AI customer experience platform that provides both high-level and granular behavioral data, but its tracking is based solely on the specific events you set manually.

It exclusively tracks user actions across predefined events, such as page views or account creation, and analyzes how these actions impact retention, conversion, and product usage. While this can help product teams improve their funnels and products, it also means that Amplitude requires intentional setup.

Unlike Fullstory, Amplitude doesn't automatically capture user behavior and doesn't support retrospective querying. Teams need to define what events they want to track in advance, or the data gets lost.

Key capabilities:

Event tracking: Tracks key user actions, from activation milestones, like first feature use, to retention events such as login frequency.
Cohort analysis: Segments users based on past behavior or predicted future actions.
A/B testing: Enables web and feature experiments to help teams identify winning variants.
Retention analysis: Analyzes which events increase user retention and drive product adoption.

Pros

A/B testing capabilities
Comprehensive retention analysis
AI-powered session replay summaries
Unified qualitative and quantitative user insights
Behavioral cohorts for large-scale segmentation
Sensitive data masking at capture

Cons

Records actual user sessions, which can raise privacy and compliance concerns
Requires upfront event instrumentation
Lacks insights into individual user behavior

Contentsquare

Contentsquare is a strong fit for enterprise marketing teams focused on web merchandising and zone-based analysis.

It automatically captures user journeys across mobile, web, social, conversations, and agent interactions. Still, a more comprehensive cross-channel analysis may require additional tools.

The platform is particularly useful for web optimization, as its heatmaps, session replays, and zone-based insights help teams understand on-page behaviors and optimize layouts for conversion.

Compared to Fullstory, though, Contentsquare offers more limited masking of sensitive data and is built from multiple tools rather than a single unified product. This may lead to a more disconnected experience and more frequent technical issues.

Key capabilities:

Heatmaps: Analyzes how users interact with your pages and what grabs attention.
Zone-based analysis: Shows how specific on-page elements, like images and links, drive desired outcomes.
Session replays: Delivers raw recordings of user sessions and AI-generated summaries.
Journey analysis: Tracks end-to-end customer journeys, from first page visit to conversion or cart abandonment.
Frustration scoring: Automatically identifies friction points that cause frustration.

Pros

Automated masking of personal information
Zone-based heatmaps for element-outcome analysis
Cross-channel qualitative and quantitative insights
Easy friction detection

Cons

Limited masking on mobile
Built from multiple tools through acquisitions (Heap, Hotjar, Clicktale)
Captures raw user sessions

Mixpanel

Mixpanel is a behavioral analytics platform focused on conversion funnels, retention, and product optimization.

Similar to Amplitude, it provides cohort analysis, deep product analytics through event-based tracking, custom reports, and visibility into complex funnels and retention paths. However, an easier setup makes it a better fit for smaller, self-serve, and less technical product teams.

Additionally, unlike Amplitude, Mixpanel can't build predictive cohorts. Compared to Fullstory, it also offers less robust privacy and compliance features and less granular autocapture.

Key capabilities:

Event tracking: Tracks how pre-defined events contribute to specific outcomes, such as conversions.
Funnel analysis: Shows how users perform events in series and where they drop off.
Retention reports: Tracks events such as messages sent or login frequency to help teams assess user engagement.
Cohort analysis: Groups users by demographic and behavior for high-level analysis.
Custom dashboards: Enables report organization in a single dashboard, built from scratch or using templates.

Pros

Robust event-based analytics
PII management tools
Cohort-based segmentation for large-scale analysis
Intuitive dashboard for at-a-glance reporting

Cons

Autocapture is limited to high-level insights
Requires manual instrumentation for deeper analysis
Captures raw user sessions
No predictive cohorts

Qualtrics XM

Qualtrics XM is different from the customer insight tools we've reviewed so far. Instead of capturing user data passively in the background, it focuses on gathering insights directly from users.

The platform excels at collecting feedback across surveys and contact centers using NLP and predictive modeling. It also listens to what customers say across digital channels, like review sites and social media, and triggers automated follow-up actions.

This makes it a strong fit for large enterprises running structured survey and research programs.

Key capabilities:

NPS/CSAT programs: Identify what decreases or increases your NPS/CSAT scores based on omnichannel analyses.
Text analytics: Uncovers what customers say about you across topics such as pricing and product reliability.
Journey orchestration: Creates 360-degree customer profiles, delivers predictive insights, and provides recommendations for customer journey optimization.
Closed-loop feedback workflows: Automatically triggers actions based on customer feedback, such as creating tickets or sending follow-up surveys.

Pros

Automates omnichannel VoC analysis
Enables advanced NPS/CSAT programs
Provides comprehensive customer profiles
Automates customer feedback collection

Cons

Lacks automated behavioral data capturing
Lacks real-time product, web, and app analytics

Medallia Experience Cloud

Medallia is another customer analytics platform focused on actively collecting user feedback and understanding what customers say across platforms.

Like Qualtrics, it unifies signals from surveys, support, and social data to measure experience performance. It also turns those signals into easy-to-review scores and surfaces emerging themes.

What sets Medallia apart is that it combines solicited user feedback with passively collected behavioral signals, such as rage clicks and time on page. This behavioral data is less robust than what you'd get with tools like Fullstory, but it could be sufficient.

Overall, Medallia is a solid fit for multi-brand organizations consolidating feedback across offline and online channels.

Key capabilities:

Real-time feedback capture: Tracks basic behavioral signals to trigger timely surveys and chat prompts.
AI-driven text analytics: Shows what customers say across channels, summarizes key takeaways, and extracts emerging themes.
Role-based dashboards: Enable different dashboards for different roles.

Pros

Combines solicited user feedback with passive behavioral tracking
Provides comprehensive customer profiles
Automates customer feedback collection
Unifies insights from multiple channels

Cons

Lacks session replays and deeper behavioral tracking capabilities
May require extensive setup

Pendo

Pendo is an AI-powered analytics and adoption platform that works best for SaaS product managers running in-app guides and onboarding flows.

It combines product analytics with in-app messaging to guide users through onboarding, feature adoption, and product updates. This makes it a good fit for product adoption use cases, but not the strongest choice for end-to-end customer journey optimization.

Key capabilities:

In-app guides: Enable in-app onboarding flows, walkthroughs, and other experiences that help users accomplish their goals inside a product.
Product analytics: Shows how customers interact with your product to increase adoption.
Feature usage tracking: Tracks how users are responding to new features and which ones are gaining the most traction.
User segmentation: Groups users by behavioral, demographic, or customer attributes for targeted insights.

Pros

Robust product analytics
In-app guide creation
User segmentation for high-level analysis
Reconstructed session replays

Cons

Requires manual event tagging
Weak automatic capture of behavioral signals

Zendesk

Zendesk is a support-focused platform with built-in analytics for tracking ticket volume, resolution time, and CSAT.

Unlike other customer experience tools on this list, Zendesk doesn't focus on analyzing user behavior or digital experience, but rather on evaluating support team performance.

It works best for support and service teams analyzing ticket data within their helpdesk. For a full view of the customer journey, you may need to pair it with other tools. Key capabilities:

Pre-built CX dashboards: Enable easy setup and metric tracking.
Post-interaction surveys: Capture satisfaction data within 24 hours of ticket resolution.
Support trend reporting: Tracks trends in human and AI agent performance through intuitive charts and historical reporting.

Pros

Pre-built dashboards for quick setup
Native CSAT and post-interaction surveys
Historical trend reporting
AI assistants for human agents

Cons

No behavioral or digital experience analytics
Deeper survey capabilities require third-party integrations
Not built for customer journey analysis

Types of customer experience analytics tools

CX analytics tools are obviously a broad category. Some track user behavior, others collect customer feedback, and still others center on tracking service team performance.

Use this brief overview to shortlist the options that match your use case.

Behavioral analytics and digital experience platforms

Usually, when people talk about customer experience analytics tools, they think of platforms like Fullstory.

These tools are built to analyze user behavior and digital experience by capturing user interactions, typically through session replays, heatmaps, and various behavioral metrics.

For instance, Fullstory's Fullcapture automatically indexes all user behavior on websites and apps. You can go back and review it at any point, whether through session replays or extracted insights, even if you haven't defined what to track upfront.

Overall, these tools are the strongest choice for diagnosing friction in digital experiences—for example, to learn why users abandon carts or sign-up forms.

Voice of customer (VoC) and survey platforms

VoC and survey platforms, like Qualtrics and Medallia, collect direct feedback from users and analyze their responses to surface sentiment trends. They're the strongest fit for tracking user satisfaction over time or after specific interactions.

For example, you can use them to calculate your NPS/CSAT scores across review sites, or to send follow-up surveys after support chat interactions automatically.

Customer journey analytics tools

Customer journey analytics tools map and analyze movement across channels to identify where journeys stall or diverge. Platforms like Salesforce and Sprinklr often combine these analytics with CRM data for a more granular analysis and more detailed customer profiles.

These tools work best for managing complex, multi-channel customer relationships.

Contact center and support analytics tools

Tools like Zendesk and Talkdesk fall into this category. They analyze support tickets and call transcripts to identify recurring issues and agent performance.

Overall, they're the strongest option for teams looking to reduce handle time and improve resolution rates.

Product analytics tools

Tools like Mixpanel and Pendo are similar to behavioral analytics platforms, like Fullstory, but focus specifically on product usage rather than broader digital experiences.

They're designed to increase product adoption and retention through insights into feature adoption, retention cohorts, and other in-product behavior trends. This makes them a strong fit for product managers and growth teams.

Key features to look for in a CX analytics tool

With an overview of different categories in mind, here's how to evaluate tools on an individual basis.

Cross-channel data integration

The more data your tool draws from, the better insights you'll get. So, check whether a tool can integrate with your CRM, support platforms, product databases, or other data sources you might use.

Native integrations are generally better than custom APIs because they're faster to deploy and easier to configure.

Behavioral data capture

Next, check how a tool captures behavioral data: do you need to tag events manually in advance, or does it capture everything automatically? Automatic capture reduces setup time and ensures no data slips through the cracks.

Sentiment analysis and text analytics

Open-ended customer responses and chat transcripts contain valuable signals, but reviewing them manually at scale isn't realistic. If you need insights from this data, choose tools that automatically process it, especially through NLP-based categorization and sentiment scoring.

Journey mapping and funnel visualization

Visualizing key customer flows, such as onboarding and checkout journeys, can help identify drop-off points and prioritize fixes. If this matters for your use case, look for tools that can segment journeys by customer cohort to speed up analysis.

Real-time alerts and anomaly detection

Real-time visibility can help you catch anomalies and issues early, such as spikes in abandonment or drops in NPS after updates. If you need this level of responsiveness, choose tools that provide real-time visibility and send alerts tied to key metrics.

Role-based dashboards and reporting

Different stakeholders often need different views of the same data. Product leads may prioritize feature usage and retention metrics, while CMOs focus on overall satisfaction and NPS. If this applies to your organization, choose tools that support role-based dashboards.

Privacy controls and compliance support

Finally, verify that a tool complies with GDPR, CCPA, SOC 2, and other relevant regulations. Since CX analytics tools often capture sensitive information, compliance is essential for avoiding legal risks and maintaining user trust.

Behavioral analytics tools should, at the very least, mask user data. For example, Fullstory masks sensitive information at the source, so it never even reaches Fullstory's servers or the client.

How to choose the right CX analytics tool for your team

Here are the final questions to ask before committing to any one CX analytics tool.

Start with your CX goals, not the feature list

Don't choose tools based on the most impressive or the widest range of features. Instead, choose the one that matches your use case and desired outcomes, even if it's less robust than another option.

Identify your goals first. Are you trying to reduce checkout abandonment, improve NPS, or something else? Without that clarity, you might overpay, underutilize, or choose a completely wrong tool.

Ask: Does this tool help me achieve my CX goals?

Map the touchpoints you need to cover

Consider whether a tool covers all the channels you want to track, such as your website, mobile app, live chat, or customer support interactions.

Ask: Does the tool cover key channels and fill existing data gaps?

Audit your existing tech stack

The best tools integrate seamlessly with your existing stack and draw from your data.

Ask: Does the tool integrate natively with my CRM, data warehouse, customer support tools, analytics platforms, or ecommerce platform?

Define who will actually use the tool

Different CX analytics tools serve different audiences: product teams, support teams, or marketers. Choose tools that match your primary users' needs and technical backgrounds.

Ask: Does this tool match the technical level and day-to-day needs of primary users?

Set a realistic budget and understand pricing models

Most customer experience analytics tools use one or more of the following pricing structures:

Per seat: Fees are charged for each active user or license.
Per usage: Charges are based on consumption of predefined credits or events.
Tiered platform pricing: A fixed recurring fee is charged based on feature tiers and usage limits.

For example, some tools combine tiered and seat-based pricing. They may offer a base plan with a set number of users, with additional charges for seats beyond that limit. It's important to understand how pricing works to realistically evaluate budget fit.

Note that enterprise platforms, like Qualtrics, are priced for large organizations, while others, like Fullstory or Mixpanel, offer more mid-market and SMB-focused tiers.

Ask: Does this tool fit my budget?

Run a structured pilot before committing

Don't commit to full-scale deployment right away. Instead, test selected tools first through structured 30- or 60-day pilots:

Define your test use case and KPIs first.
Next, collect relevant baseline data, such as NPS scores, cart abandonment rates, or the number of returning users.
Finally, break your pilot into phases, preferably two weeks for setup, two weeks for testing, and two weeks for monitoring.

By the end of your pilot, you should know whether a tool helped you achieve your KPIs. Apart from that, it's a good idea to evaluate general fit, ease of use, and other factors that might be important, but less straightforward to measure.

Ask: Did this tool help me hit my pilot KPIs, and does it fit how my team actually works?

Evaluate vendor support and onboarding

Finally, consider whether a vendor provides appropriate support and onboarding. Implementation ease varies from one tool to another, and some might require more hands-on support or technical knowledge than others.

Assess if the vendor truly provides what you need by checking customer reviews on independent sites, like G2 or Capterra, or by directly asking for references from customers with similar team sizes.

Ask: Do I trust this vendor to set my team up for success?

Why CX analytics matter for retention and revenue

Customer experience analytics helps you reduce churn based on real signals rather than your team's best guesses.

In 2026, companies are increasingly trying to counteract this by improving CX in various ways. Most notably, they're shifting from reactive to predictive listening, using AI to deliver data-driven, personalized experiences, and creating unified customer profiles based on multi-channel signals.

Reliable CX analytics are the foundation for all these efforts, as well as general optimizations your team should implement.

For instance, CX analytics tools help you:

Catch drop-off points in the journey, so teams can prioritize a UX fix over a new feature.
Identify which customer segments are at risk of churn so teams can trigger proactive retention campaigns.
Measure whether CX changes actually improved satisfaction scores, so teams can refine future CX initiatives.

Additionally, robust customer data and behavioral signals let you deliver more personalized experiences, whether through AI or otherwise.

Turn customer behavior into better experiences

Customer experience analytics tools should help you eliminate friction, drive desired outcomes, and increase customer satisfaction. After choosing a tool, evaluate whether you're actually achieving these goals using CX metrics that match your use case.

If you're still weighing your options, Fullstory is hard to overlook. With analytics that cover both behavioral and digital experience data, you'll have everything you need to start fine-tuning CX across platforms.

7 behavior analytics tools for enterprise user insights in 2026

The Fullstory Team — Tue, 04 Mar 2025 12:00:00 GMT

Every user who visits your website or mobile app leaves a trail of digital breadcrumbs- clicks, scrolls, hovers, and drop-offs. Behavioral analytics tools help teams turn these signals into actionable insights, revealing friction points, improving engagement, and boosting revenue.

Not all tools offer the same benefits. Some focus on heatmaps, others on session replay, and some provide deep product analytics. The right choice depends on what you need to measure, optimize, and scale.

Here’s a look at the top behavior analytics tools enterprises use in 2026.

1. Fullstory: The smarter way to capture and action behavioral data

Many behavioral analytics tools claim to deliver insights, but they often require manual tagging, leaving you uncertain about what truly matters. Fullstory removes that guesswork.

Why it stands out

Many analytics tools claim to offer autocapture, but often at the cost of data completeness—missing key user interactions or requiring manual coding for deeper insights. Fullstory eliminates that trade-off. With Fullcapture, teams get both effortless setups and the most comprehensive, granular qualitative data available, ensuring nothing slips through the cracks.

That means complete behavioral data from the very first day.

Notable features:

Enterprise-ready security – SOC 2 & ISO 27001 certified, with role-based access control.
Seamless integrations – Works with Google Analytics, Salesforce, Optimizely, Jira, and more.
Tag-free, automatic data capture – No tedious setup required.
Session replay with pixel-perfect accuracy – See exactly where users struggle.
Frustration detection – Identify rage clicks, dead clicks, and other pain points.
AI-powered insights – Let StoryAI highlight trends so you can focus on solutions.
In-context feedback and guidance – Use Guides and Surveys to collect user feedback and offer proactive help directly within your app.
Privacy-first approach – Built-in PII redaction ensures compliance with GDPR and CCPA.

Pricing options:

Fullstory isn’t just a product analytics tool, it’s an investment in better user experiences that translate into higher engagement and conversions. Fullstory offers custom pricing tailored to business size and specific needs. Fullstory's free plan is packed with behavioral value

Want to see Fullstory in action? Book a demo.

2. Contentsquare: Optimize digital experience with visual analytics

Contentsquare zeroes in on digital experience analytics, helping teams gain a deeper understanding of how users interact with individual page elements.

Notable features:

Zone-based heatmaps – Identify which areas of a page drive engagement.
Journey analysis – Spot unexpected patterns in how users navigate a product.
Session metrics & segmentation – Compare engagement across different audiences.
Performance monitoring – Connect UX issues to revenue impact.

Pricing options:

Contentsquare offers custom enterprise pricing, with a demo required to receive a quote.

3. Quantum Metric: AI-powered digital experience optimization

Quantum Metric provides real-time visibility into user struggles and site performance.

Notable features:

Real-time friction detection – AI-powered insights highlight problem areas instantly.
Session replay + heatmaps – Visualize engagement and frustration points.
AI-powered anomaly detection – Automatically flag shfits in customer behavior.
Performance monitoring – Catch slow load times and site errors before they impact conversions.

Pricing options:

Quantum Metric provides custom pricing based on usage, with a free trial available.

4. Mixpanel: Deep-dive product analytics

Mixpanel is designed for teams looking to track product engagement, feature adoption, and customer retention.

Notable features:

Custom event tracking – Define and monitor key user actions.
Retention analysis & cohort segmentation – Understand how behavior evolves over time.
Self-serve reporting – Get insights without relying on engineering support.

Pricing options:

Mixpanel offers a free plan, a Growth plan starting at $20/month, and custom enterprise pricing.

5. Pendo: Blend analytics with in-app engagement

Pendo combines behavioral analytics with in-app engagement tools, allowing teams to act on insights directly within their products.

Notable features:

Product usage analytics – Track feature adoption and engagement over time.
In-app guides & walkthroughs – Provide tooltips and onboarding flows without engineering support.
User feedback & NPS surveys – Gather sentiment data to improve product experiences.

Pricing options:

Pendo offers a free version with limited features, with custom pricing for enterprise plans.

6. Microsoft Clarity: Free session replay & heatmaps

For teams looking for a free and simple way to analyze user behavior, Microsoft Clarity is a great option.

Notable features:

Session replay & heatmaps – Get a clear view of how users engage with your site.
Rage click detection – Identify frustration points quickly.
No-impact performance – Runs passively without slowing down your site.

Pricing options:

Microsoft Clarity is completely free, with no enterprise pricing plans.

7. Amplitude: Data-driven product decisions

Amplitude helps teams understand long-term user behavior through advanced lifecycle analytics.

Notable features:

Advanced funnel analysis – Identify where users drop off in multi-step processes.
Predictive analytics – Forecast user behavior trends based on historical data.
Customer journey mapping – Visualize engagement patterns over time.

Pricing options:

Amplitude offers a free plan, a Growth plan starting at $49/month, and custom enterprise pricing.

Choose the right behavioral analytics tool

Finding the right behavioral analytics tool depends on balancing ease of use with data depth. Many solutions promise "autocapture," but often at the expense of completeness—missing key behavioral signals or requiring manual setup for deeper insights.

Fullstory’s Fullcapture stands apart by delivering both an effortless setup and the most comprehensive user behavior data available without compromise. That’s why leadifng enterprises choose Fullstory to power smarter decisions and frictionless digital experiences. As the first in the industry to achieve ISO 42001 certification, it also leads the way in responsible AI governance.

Whichever tool you choose, the goal remains the same: to turn raw data into smarter decisions and better user experiences.

See how Fullstory helps teams move faster and smarter. Book a demo.

Best enterprise analytics software to understand and improve user behavior

Fullstory — Fri, 31 Jul 2026 07:00:00 GMT

If your enterprise analytics software doesn't help you understand how to improve conversions or reduce drop-offs, the problem likely isn't your analysis.

The chances are, your software only shows you what's happening on your digital properties, or turns that data into presentable charts. While helpful, this doesn't explain what drives user actions or help you decide what to do next.

What you need is deeper insight into user behavior. That's why this guide focuses on software that offers that missing layer: behavioral data that actually helps direct next actions.

Behavioral data is the missing layer in most enterprise analytics stacks

Most enterprise teams have two to three analytics tools in their stacks:

Web analytics for traffic reporting
A product analytics tool for funnel and retention data
A BI layer for dashboards (if they’re lucky)

While necessary, these tools don't explain why users behave the way they do.

Without this context, teams miss hidden revenue leaks, like rage clicks, dead clicks, and cart abandonment patterns, and often discover checkout friction, onboarding drop-offs, and bugs only after they appear in revenue and performance reports.

The problem becomes even bigger if you're using AI to analyze digital experiences. When AI models lack access to behavioral data, they miss important signals, fill in the gaps on their own, and produce misleading outputs that can send your team down a completely wrong path.

That's why behavioral data should come first. Behavioral analytics tools typically collect it using one of three approaches: manual instrumentation, autocapture, and high-resolution behavioral capture.

Manual instrumentation records what engineers tag upfront, but other events and signals get lost.
Autocapture records signals automatically, but the set of interactions it captures is limited. It might miss important user behaviors and leave gaps in your data.
High-resolution behavioral capture, like Fullstory's Fullcapture™, logs every user interaction. All behavior is indexed automatically on the server side, with full retroactivity. No data gets lost, ever.

It's obvious that the latter option is the best choice. So, while evaluating the tools below, consider the depth of data they capture and whether that capture happens automatically or requires manual setup.

Best enterprise analytics software overview

The tools below span behavioral analytics, product analytics, BI, and web analytics. Understanding which category a tool belongs to is the first step to building the right stack.

Tool	Best for	Behavioral depth
Fullstory	Enterprise teams connecting high-resolution behavioral data to business outcomes	High: Fullcapture indexes every interaction automatically, server-side
Amplitude	Product teams running event-based funnel and retention analysis	Medium: strong on funnels, limited on page-level behavioral context
Contentsquare	Enterprise teams analyzing page-level journeys and zone behavior	Medium: zone-based, acquisition-built data model
Mixpanel	Self-serve product teams running event-based analytics	Low-medium: instrumentation-dependent, no behavioral capture
Heap	Product teams wanting retroactive event data without manual tagging	Medium: automatic capture, but limited compared to high-resolution behavioral data
Glassbox	Regulated industries requiring compliance-grade behavioral data	High: strong compliance controls and mobile depth
Quantum Metric	Enterprise retail and financial services quantifying revenue impact of UX issues	Medium-high: strong anomaly detection and revenue quantification
Tableau	BI and data teams building visual dashboards from structured data	None: visualization tool, not a behavioral platform
Looker	Data teams running SQL-based reporting and embedded analytics	None: BI layer, requires behavioral data from another source
Adobe Analytics	Enterprise marketing teams running multi-channel attribution	Low: traffic and attribution focus, minimal behavioral context

1. Fullstory

Best for: Enterprise digital and product teams that need high-resolution behavioral data connected to AI-powered insights

G2 Rating: 4.5/5

Fullstory is an enterprise analytics software that captures more user behavior data than almost any other tool.

With most platforms, you either need to tag the behaviors you want to track in advance, or settle for a limited set of interactions that get captured automatically.

Fullstory is different because it automatically captures everything. It indexes every user interaction on the server side, so nothing gets lost and unreported. You don't have to manually tag events, and you can always go back and analyze behaviors you didn't think to track initially.

Rich context also enables StoryAI, Fullstory's AI intelligence layer, to analyze user behavior in more depth and answer questions more accurately.

Other features, like heatmaps, funnels, and session data, draw from the same behavioral dataset. Metrics stay consistent, and all data gets fully used.

Fullstory's Private by Default architecture further ensures that sensitive data is masked at the source. Real data, like card details, never reaches Fullstory's servers or your dashboard, regardless of whether you're tracking web or mobile sessions.

Key features:

Fullcapture: High-resolution, server-side behavioral data capture across web and mobile. No manual tagging, full retroactivity.
StoryAI: AI agents that surface summaries, answers, opportunities, and predictions using the full behavioral dataset.
Enhanced heatmaps: Auto-configured, revenue-overlaid. No setup required when pages change.
Friction detection: Rage clicks, dead clicks, error clicks, and thrashed cursors surfaced automatically.
Funnel analysis: Connects quantitative drop-off to individual behavioral sessions for root cause analysis.
Anomaly detection: Flags behavioral pattern changes before they impact revenue.
Fullstory Anywhere: Exports structured behavioral data to your data warehouse for BI and advanced modeling.

Pros

Highest-resolution behavioral data capture on the market
AI built on a complete behavioral dataset, not partial instrumentation
Private by Default, including mobile masking
Full retroactivity, query behavioral data for questions you hadn't thought to ask yet
One data model across all features — no acquisition-built fragmentation

Cons

Enterprise pricing, not suited for SMBs or limited budgets
Initial deployment requires engineering for script installation
Advanced features have a learning curve for non-technical users

Pricing

Free: 30K monthly sessions, 12 months of data retention.
Business/Advanced/Enterprise: Custom pricing. Product analytics, session insights, and more.

Reviews

Our company has been with FullStory for about 5 years, and we have ABSOLUTELY LOVED IT!! We have integrated with our online customer feedback tool so that we can watch every session where a customer had left us feedback. All of our Customer Service Reps have access, and can easily look up sessions when someone calls-in, and this has helped us identify and resolve issues much more quickly.
- Jarom G., Director Product Management

2. Amplitude

Best for: Product teams running event-based funnel and retention analysis

G2 Rating: 4.5/5

Amplitude is a strong option for those looking for a product analytics platform. Its funnel visualization, retention analysis, and cohort comparisons are genuinely useful for understanding what users do inside digital products.

But unlike Fullstory, Amplitude doesn't explain why users take specific actions. For instance, it might surface that users are dropping off at a particular funnel stage, but not what on the page that caused it.

Amplitude also only captures events that are tagged upfront, so new friction points that might emerge in untracked flows remain invisible. This limits the scope of Amplitude's AI capabilities, as the AI can only draw insights based on captured data.

That means Amplitude works best when it complements a behavioral analytics platform rather than replaces it.

Key features:

Event-based funnel analysis: Identifies how users move through the funnel based on pre-defined events.
Retention and cohort reporting: Tracks how often users return to the product and groups users by past or predicted behavior.
A/B testing and feature flag integration: Compares feature performance and lets you enable or disable them without redeploying code.
Custom dashboards: Shows user-selected metrics in one place.
Data warehouse connectivity: Brings data from the rest of your digital ecosystem into Amplitude.

Pros

Strong self-serve product analytics experience
Good funnel and retention reporting
Free tier available for smaller teams
A/B testing and experiment support

Cons

No behavioral capture, instrumentation-dependent
Data gaps wherever events aren't tagged
No page-level behavioral context or friction signals
AI insights limited by incomplete data capture

Pricing

Free: 2M events/month
Plus: Starts at $0 for 2M events/month. After that, pricing goes up to $0,00007 per event.
Growth: Custom pricing. Unlimited MTU or events.

Reviews

The behavioral cohorts are what keep me coming back. I can actually see how different user groups move through the funnel instead of just guessing. Setting up charts is fast once you know your way around, and the fact that I don't need to bug an analyst every time I want to slice the data differently is huge for someone working solo.
- Verified User in Financial Services, Mid-Market

3. Contentsquare

Best for: Enterprise teams analyzing page-level zone behavior and customer journeys

G2 Rating: 4.6/5

Contentsquare is an enterprise behavioral analytics platform known for its journey analysis and zone-based heatmaps, which help identify conversion drivers and friction points with precision.

One trade-off is that Contentsquare has evolved through acquisitions of products like Heap, Hotjar, and Clicktale, so data models across features might be inconsistent. This can cause fragmentation issues and steep learning curves that you wouldn't encounter with tools built around a single, unified data model.

Data capture also relies on instrumentation, so the tool requires ongoing maintenance. If you implement any changes to SPAs, dynamic content, or page structures, you'll need to manually update tracking to avoid data gaps.

Compared to Fullstory, Contentsquare also offers more limited mobile data masking capabilities, which may not meet the standards of regulated industries.

Key features:

Zone-based heatmaps and scroll maps: Shows how specific page elements affect user behavior.
Customer journey analysis: Shows what users do before converting or dropping off.
Friction scoring and frustration signals: Identifies the biggest friction points on websites and apps.
AI-assisted insights: Data analysis, fresh insights, and answers to internal questions.
Revenue attribution per zone: Shows how different page zones impact the revenue.

Pros

Strong journey analysis capabilities
Good zone-based heatmap tooling
Established enterprise customer base
Revenue attribution overlaid on page behavior

Cons

Acquisition-built structure creates fragmented data models
Data capture breaks when sites change — requires manual fixes
AI tied to predefined metrics, no retroactive behavioral analysis
Limited mobile privacy masking

Pricing

Free: 200k monthly sessions, 1 project.
Growth: Starts at $39/month. 13-month data retention, 3 projects.
Pro/Enterprise: Custom pricing. Expanded or unlimited projects and sessions.

Reviews

I like how Contentsquare provides detailed behavior analysis of our targeted users on both the website and the app. The visual tools - such as heatmaps, session replays, and journey analysis - give us clear, in-depth insights into what users are doing.
- Manoj B., Assistant Manager - Digital Marketing

4. Mixpanel

Best for: Self-serve product teams running event-based analytics

G2 Rating: 4.5/5

Mixpanel is one of the best business analytics tools for product teams. It focuses on event tracking, funnels, and retention, while its self-serve experience makes it easy to draw meaningful insights.

One drawback is that Mixpanel typically requires extensive setup and lacks behavioral depth. Autocapture needs a fair amount of manual configuration and, even when it's running, offers far less context than Fullstory.

This makes it a weaker fit for teams that want to understand the underlying behaviors behind drop-offs or conversions. Overall, the platform works best when it complements a dedicated behavioral tool.

Key features:

Event-based funnel analysis: Shows how users perform events in series.
Retention and cohort analysis: Assesses user engagement and groups users by demographic and behavior.
Custom dashboards: Gather the most important metrics in one place.
User segmentation: Groups users by behaviors or properties, such as origin country.

Pros

Strong self-serve funnel analytics
Accessible free tier
Clean, intuitive UI
Good retention reporting

Cons

No behavioral data capture
Data limited to a narrow set of events
No page-level behavioral context or friction signals
AI capabilities limited by instrumentation dependency

Pricing

Free: 1M events/month.
Growth: Starts at $0 for 1M events/month. After that, pricing goes up to $0.28 per 1K events.
Enterprise: Custom pricing. Unlimited monthly events.

Reviews

It's a very passive application for me. The initial set-up was pretty easy, and now I receive an email from Mixpanel that I check everyday. If the numbers are good, no action needed from me. If the numbers are bad, Mixpanel is the first place I go to see which customer accounts need my attention.
- Verified User in Real Estate, Small-Business

5. Heap

Best for: Product teams wanting retroactive behavioral data without manual tagging

G2 Rating: 4.4/5

Although Heap is primarily a product analytics platform, it offers more in-depth behavioral data than most comparable tools, including Mixpanel.

It automatically captures events, so you don't have to instrument everything upfront. This also enables retroactive analysis, which means you can define new behaviors and analyze historical data as you go.

Note that Heap was acquired by Contentsquare in 2023, so its product roadmap and ownership structure now sit within the same acquisition-built ecosystem. This could present challenges around integration, consistency, and fragmentation.

Another issue is that capture, though automated, is quite limited. Heap only records a specific set of interactions, so key behavioral context might go unreported. Compared to Fullstory, which indexes all user interactions, Heap falls short.

Overall, while autocapture presents a step up from manual instrumentation, incomplete data can weaken AI-powered analysis.

Key features:

Automatic event capture without manual tagging: Captures key user data automatically.
Retroactive funnel analysis: Analyzes historical user journeys against newly-defined criteria.
Session replay: Shows how users navigate products.
User segmentation and journey analysis: Groups users by behavior and shows how they perform actions in series.

Pros

Automatic capture reduces instrumentation burden
Retroactive analysis without re-instrumentation
Good funnel and journey analysis
No manual tagging required

Cons

Now owned by Contentsquare
Limited capture compared to high-resolution behavioral platforms
Client-side capture creates data completeness gaps
AI insights limited by capture fidelity

Pricing

Free: 10K monthly sessions, 6 months of data retention.
Growth/Enterprise/Premier: Custom session pricing. Access to Sense Contentsquare’s AI, 12 months of data retention.

Reviews

I like that heap is so easy for non-technical team members to be able to create tracking events without the need for any coding knowledge or getting the engineering team involved.
- Milos D., Product Design Manager

6. Glassbox

Best for: Regulated industries requiring compliance-grade behavioral data

G2 Rating: 4.9/5

Glassbox is another strong option for enterprise behavioral analytics, especially for financial services, insurance, and regulated retail sectors that need native compliance controls.

The platform offers behavioral depth through capabilities like struggle detection, session replay, and funnel analysis, alongside compliance-grade data handling and mobile masking.

While Glassbox is genuinely strong in regulated verticals, it's less applicable as a general enterprise behavioral analytics platform.

It serves narrower use cases than tools like Fullstory or Contentsquare and offers somewhat less flexibility. It's also completely sales-led, with no public pricing or free plan.

Key features:

Compliance-grade session replay with audit trail: Captures all user interactions automatically, masks or omits PII at capture, and keeps auditable records of digital sessions.
Struggle detection and friction identification: Detects, analyzes, and scores friction points.
Native mobile masking: Masks and omits PII from mobile sessions.
Funnel analysis and journey mapping: Tracks user journeys and analyzes funnels proactively and retroactively.

Pros

Strong native compliance and privacy controls
Genuine behavioral depth with struggle detection
Mobile analytics with native masking
Purpose-built for financial services and regulated retail

Cons

Narrower use case outside regulated industries
No public pricing or free plan
Sales-led procurement process
Less self-serve accessible for non-technical teams

Pricing

Production Operation, Marketing/Business, and Product: Custom pricing. 2M or 3M monthly sessions.

Reviews

I love the simplicity of the design in Glassbox, as it offers me convenience in managing everything because it handles all matters, saving me time and effort.
- Verified User in Retail, Enterprise

7. Quantum Metric

Best for: Enterprise retail and financial services teams quantifying the revenue impact of UX issues

G2 Rating: 4.6/5

Quantum Metric is another enterprise behavioral analytics platform, but with a focus on connecting UX friction to revenue impact. This makes it particularly suitable for retail and financial services teams looking to justify investments in digital experiences.

The platform excels at anomaly detection, especially surfacing behavioral pattern changes and quantifying their revenue implications automatically.

Compared to Fullstory and Heap, though, Quantum Metric offers a less accessible setup for non-technical teams and might require vendor support.

Key features:

Session replay and journey analysis: Captures all sessions across web and mobile and enables segment-driven journey analysis.
Automated anomaly detection: Detects friction and sends automated alerts.
Revenue quantification tied to UX issues: Quantifies the impact of fixing identified issues.

Pros

Strong revenue quantification for UX issues
Good anomaly detection capabilities
Purpose-built for retail and financial services

Cons

Enterprise-only pricing, no accessible entry point
Less self-serve accessibility for non-technical teams
Sales-led with no public pricing

Pricing

Customer Operations/Business Operations/UX Operations/Developer Operations: Custom pricing. Includes team training.

Reviews

Being able to view assisted channels and customer interactions within the same software. Glassbox needed to have two different logins and had to do some digging for end-to-end logs to find the appropriate sessions.
- Verified User in Consumer Services, Enterprise

8. Tableau

Best for: BI and data teams building visual dashboards from structured data sources

G2 Rating: 4.4/5

Unlike most enterprise analytics platforms we’ve reviewed so far, Tableau is a BI and data visualization tool rather than behavioral analytics software.

Instead of capturing raw data itself, Tableau connects, cleans, and transforms it into easy-to-understand dashboards and charts. This can be extremely useful in exploring your data and generating insights, but it won't be helpful if you don't have behavioral data to begin with.

Luckily, Tableau pairs well with behavioral analytics platforms.

For instance, with Fullstory Anywhere: Warehouse, you can export behavioral data to your data warehouse and have Tableau visualize it alongside other business data. This provides more comprehensive and easily accessible insights.

Key features:

Data visualization and dashboarding: Facilitates data exploration by turning it into visual formats, such as charts, graphs, and maps, and by pulling key metrics into a single visual display.
Multi-source data blending: Combines data from multiple sources or visually presents the results side by side.
Enterprise connectors and integrations: Brings data from other systems into Tableau or embed analytics in custom web portals, apps, and products.
Self-serve analytics for BI teams: A drag-and-drop interface, Tableau Agent, and other features make Tableau accessible even for non-technical end users.

Pros

Best-in-class data visualization
Strong multi-source data blending
Broad enterprise adoption
Works well alongside behavioral platforms via data warehouse

Cons

No behavioral data capture capability
Requires behavioral data from another tool to add experience context
Steep learning curve for non-technical users
Per-user pricing adds up at enterprise scale

Pricing

Tableau Standard: $15/user/month.
Tableau Enterprise: $35/user/month.
Tableau Next: $40/user/month.
Tableau Cloud+, Tableau+ Bundle: Custom pricing.

Reviews

Tableau is very helpful for turning raw data into clear dashboards and visuals, the interface is also user friendly once you get used to it. It connects well with all data sources which makes the integration of it very easy. Performance is generally good.
- Kapil S., Project manager

9. Looker

Best for: Data teams running SQL-based reporting and embedded analytics

G2 Rating: 4.4/5

Similar to Tableau, Looker is a BI and embedded analytics platform rather than a behavioral analytics tool.

It's a solid option for data teams that want a governed, SQL-first analytics layer on top of a data warehouse. LookML modeling also enables teams to create business-friendly data models and maintain consistent metric definitions across the business.

The problem is that Looker doesn't capture behavioral data on its own, but rather needs that data fed into it before it can surface experience insights.

It works best as a complementary BI layer rather than a standalone solution for digital experience analytics.

Key features:

LookML semantic modeling: Creates easy-to-understand data models from SQL data.
Embedded analytics: Integrates analytics directly into other applications, websites, and products.
Data warehouse connectivity (BigQuery, Snowflake, Redshift): Enables unified reporting and a single, updated source of truth.
Governed metric definitions: Defines metrics once and applies them consistently across dashboards and users.

Pros

Strong governance and consistent metric definitions
Good embedded analytics capabilities
Native data warehouse connectivity
Works well alongside behavioral platforms via Fullstory Anywhere: Warehouse

Cons

No behavioral data capture
Requires engineering to build and maintain LookML models
Not accessible for non-technical users without embedded builds
Google Cloud-dependent for best performance

Pricing

Standard/Enterprise/Embed: Custom pricing. Pricing also depends on user license type: Developer User, Standard User, Viewer User.

Reviews

I really like Looker for its seamless integration and connectivity, especially with tools like Google AdWords and Google Analytics. The way it allows data to be consolidated in one place with just one click is really handy.
- Sameer A., Digital Marketing Manager

10. Adobe Analytics

Best for: Enterprise marketing teams running multi-channel attribution and traffic analysis

G2 Rating: 4.2/5

Adobe Analytics is an enterprise web analytics and attribution platform that works best for managing complex multi-channel measurement.

It captures traffic, conversion, and attribution data well, but stops short of explaining why users behave the way they do on the page or in the product.

The platform is also known for its implementation complexity and high total cost of ownership. Teams might even require specialist support to get full value from the tool.

Overall, the teams that will benefit from this platform the most are those already deeply embedded in the Adobe Experience Cloud ecosystem. Native integrations allow you to easily pull and unify data from other Adobe tools, like Adobe CX Enterprise.

Key features:

Traffic and conversion reporting: Captures users, sessions, and conversion events on your website or app.
Multi-channel attribution modeling: Assesses the performance of different channels.
Audience segmentation: Groups users by behaviors, traits, and activity.
Adobe Experience Cloud integrations: Connect with tools like Adobe CX Enterprise, Adobe Creative Cloud, and Adobe Target.

Pros

Strong multi-channel attribution for marketing teams
Deep Adobe ecosystem integration
Robust audience segmentation
Established enterprise customer base

Cons

No behavioral data capture or friction detection
High implementation complexity and TCO
Requires specialist support for full value
Not built for product or digital experience teams

Pricing

Five different plans with custom pricing.

Reviews

I use Adobe Analytics for work analysis to surface what we collect across web and app user behavior on the front-end. I find the Workspace feature particularly useful as it helps stakeholders access and analyze the data at their fingertips. -Emily W., Mid-Market

How to evaluate enterprise analytics software

By now, you probably have a shortlist of enterprise analytics solutions you want to explore in more depth. The best way to evaluate the tools is to use free trials, get demos, and ask questions that publicly available materials might not cover.

Here are some key areas to explore on your demo calls and during trials:

Ask how the platform captures data: Does it rely on manual instrumentation, autocapture, or high-resolution behavioral capture? The answer determines what your AI and team can actually work with.
Test retroactivity: Can the tool surface insights from data collected before you thought to instrument for them? If not, it may leave permanent data gaps.
Push on AI data quality: AI built on incomplete data produces incomplete insights. Ask the vendor what dataset the AI layer is built on. Is it the full behavioral record or just predefined metrics?
Verify mobile behavioral depth: Mobile behavioral data is often shallower than web. Ensure both depth and compliance by asking for a direct demo of mobile capture and masking.
Get a total cost of ownership breakdown: Implementation time, instrumentation maintenance, professional services dependency, and engineering overhead all affect the total cost of software. Ask the vendor for a custom estimate before you commit.
Test non-technical accessibility: Consider whether a product manager or CX leader can get to meaningful insights without filing an engineering ticket.
Ask about data activation: Can behavioral data move to your data warehouse, CRM, or marketing automation tools, or does it stay siloed within the platform? If it's the latter, the tool might place too much manual burden on your team and stop them from leveraging it to its full potential.

What to look for before you pick a platform

Beyond assessing different analytics software companies and tools, you should also assess your own needs and how you plan to use the platform.

Consider your goals and any limitations you may need to work around. Here are some quick tips to get you started:

Start with the use case, not the feature list. Not all data analytics platforms serve the same purpose. BI tools and behavioral analytics platforms, for example, are both considered "enterprise analytics software," but they answer fundamentally different questions. That's why you should define your use case first and search for tools that match it second.
Consider what type of data capture fits that use case. If you want to understand why users behave the way they do, how friction affects revenue, or simply equip your AI with the data it needs to surface accurate insights, you'll need a platform with high-fidelity behavioral data capture.
Prioritize platforms that capture behavioral context automatically and retroactively. This allows your team to answer questions you haven't thought to ask yet.
Evaluate the data layer first, and AI features second. If the data is incomplete, even the best AI features will produce inaccurate outputs.
If you're in a regulated industry, privacy architecture is non-negotiable. Verify that masking happens at the source rather than as a post-processing step. Also, ensure that masking is applied to mobile sessions, too.

Find the friction your analytics stack is missing

In most enterprises, analytics stacks include disconnected tools that count things but can't explain them. This ends up adding more gaps, as teams struggle to understand what to actually take away from the data.

Fullstory closes those gaps. With a single script, it captures rich data that explains user behavior in greater detail than most tools. This enables more accurate analysis, whether you're conducting it with Fullstory's built-in AI layer or connecting the platform with the AI tools you already use.

For enterprise teams that need behavioral clarity without the setup overhead and analyst dependency, Fullstory is the strongest choice.

5-point checklist for deploying and maintaining a killer SwiftUI app

The Fullstory Team — Tue, 27 Sep 2022 04:00:00 GMT

You want to build a SwiftUI mobile app, maintain a current SwiftUI app, or transition an existing app to SwiftUI—but you’re not sure where to start or what to keep in mind.

You’re in the right place.

Learning, using, and mastering new iOS APIs is hard work; there’s a steep learning curve. There is a lot that goes into being ready to build your SwiftUI app, so we’ve created a five-point checklist that’ll help.

You’ll be able to focus on creating a beautiful mobile app without having to scour the internet for best practices and need-to-knows. Plus, Fullstory supports SwiftUI, so you can step up your app game even more.

Let’s get into it.

1. Understand what SwiftUI is (and isn’t).

We know you’ve likely heard of SwiftUI, but for posterity, it’s Apple’s newest mobile UI development framework. It's a declarative framework that makes the UI portion of app development, to put it plainly, really fast. In using a declarative framework like SwiftUI, developers can create safer, more understandable, and more scalable code.

With a declarative framework, developers tell SwiftUI how they want the app to look and function, and the framework figures out how to do so as the developer interacts with it.

Essentially, the programmer writes only one function that constructs the entire view tree, and the framework calls it automatically every time anything needs an update. The framework intelligently figures out what has changed each time the view tree is reconstructed. In this new way of doing things, the structure of code directly represents the structure of what’s happening on the phone’s screen.

This is quite different from an imperative framework, which is the more traditional programming type used by UIKit. In an imperative framework, the programmer is responsible for manually creating objects to represent anything that exists on the screen, for linking into a UI tree, and for updating each view when needed. This requires a great deal of code, potentially obscuring how the UI actually works together.

With the rise in demand for declarative frameworks, SwiftUI is becoming more and more popular. According to app developer Paul Hudson, “[SwiftUI is] 100% absolutely going to be the future of app development on Apple’s platforms.”

2. Get your resources right.

SwiftUI, as you now know, is the hot, new framework on the market. If you aren’t using it already, you likely will be soon.

With that, it’s imperative to have a list of resources from which to pull. Luckily, our expert Fullstory developers have done the research for you. Add the below to your bookmarks for some helpful reading material that’ll set you up for great SwiftUI success.

Apple’s official SwiftUI tutorials
Full list of Apple’s SwiftUI documentation
Apple’s “Demystify SwiftUI” video
Apple’s SwiftUI video from the 2022 Worldwide Developers Conference
One of our favorite SwiftUI quick-start guides
SwiftUI lab: A blog dedicated to SwiftUI experimentation
Open Source Apps

3. Join a community to share your insights and grow together.

Resources are important, but so is the collective. You’re only as good as those from whom you learn, and there are a number of online communities dedicated to understanding, learning, developing, and maintaining SwiftUI apps.

These are some great communities you’ll want to be a part of as you embark on your SwiftUI journey:

Reddit - r/SwiftUI subreddit
- What it’s good for: asking general questions, hearing others’ opinions on how their builds are progressing, and obtaining a general understanding of how others are developing SwiftUI apps (don’t forget to review the “Community info” for gems of information).
Apple - SwiftUI developer forum
- What it’s good for: getting answers for specific coding questions, accessing keyboard shortcuts, addressing roadblocks, and anything else that’s super technical when it comes to developing SwiftUI apps.
Reddit - r/iOSProgramming subreddit
- What it’s good for: This is a more general community for all things iOS programming and mobile applications. It’s a great community to review others’ apps, ask questions, vent, and discuss framework comparisons.

Pro tip: Join the new Fullstory Community to connect with other Fullstory for Mobile Apps users, ask questions, and share ideas.

4. Know who is doing SwiftUI apps well and study them.

Aristotle believed that imitation was a born instinct and a necessary good. With that in mind, if you want to create a successful SwiftUI-developed app (or improve an already-existing app with SwiftUI), you must know who’s doing it right and study the why.

We’ve discovered some companies who have beautifully designed SwiftUI apps. Check those out below and learn more about why we love them:

Wayfair
- Why we love their app: they just released a 3D room feature, allowing app users to see life-sized versions of products in any space.
Epsy Health
- Why we love their app: Their award-winning app provides an elegant and easy-to-use user interface.
OKCupid
- Why we love their app: OkCupid uses a similar UI to today’s most popular social media apps, and that’s a win for positive user experience.
On-Running.com
- Why we love their app: they have a clean and simple app that is both functional and aesthetically-pleasing.

5. Listen to your app users and keep your app running smoothly.

The success of an app comes from the dedication to optimizing and maintaining its usability.

How do you know your app is working as it should? How are you ensuring your users enjoy their app experience? In what ways are you solving common problems users are experiencing?

These answers might surface anxiety, and don’t worry–we get it. That’s why it’s so important to make sure you’re using a comprehensive mobile analytics tool on your SwiftUI-built mobile app. Not only do you need a tool that will reveal powerful metrics that help you understand what’s happening in your app, but you need a tool that also uncovers the mysteries of why.

Fullstory now supports all apps made with SwiftUI, so you can stay on the pulse of new frameworks without sacrificing critical behavioral data.

Customer sentiment: Shaping how you engage with your audience

The Fullstory Team — Mon, 09 Sep 2024 13:00:00 GMT

Imagine walking into your favorite coffee shop and the barista knows your order by heart. They even throw in an extra shot of espresso because they remember you have an important meeting today that you mentioned last week. That’s not just good service—that’s the power of understanding how you feel. What if your business could do the same for every customer?

What is customer sentiment?

Customer sentiment is a metric that measures the emotional connection and overall feelings customers have towards a brand, product, or service. It’s the difference between a customer who simply buys your product and one who passionately recommends it to everyone they know. This isn’t just about satisfaction surveys or star ratings; it’s about tapping into the deeper emotions that drive loyalty—or, conversely, send people packing.

Why it's important

We’ve all had that one bad experience that made us swear off a brand forever. On the other hand, a brand that gets us, that listens and responds (sometimes through well-implemented AI in customer service), can become part of our daily lives. Businesses that get customer sentiment right don’t just avoid the pitfalls—they create raving fans. It is the secret sauce that turns ordinary customer interactions into long-term relationships.

Steps to increase customer sentiment

Measuring customer sentiment involves using a combination of behavioral data, direct feedback, social listening, and AI-powered insights to understand how customers feel about your brand. By analyzing these different data points, businesses can uncover emotional drivers behind customer actions, allowing them to personalize experiences and improve satisfaction.

Behavioral data: Every click, scroll, and hover tells a story. Platforms like Fullstory help you understand the ‘why’ behind customer actions—whether it’s cart abandonment or lingering on certain pages. Behavioral data offers real-time, actionable insights into user behavior patterns, revealing emotional cues that traditional feedback often misses. When you pair behavioral data with other forms of feedback, you get the full picture — helping you move from guessing to knowing what drives customer frustration or delight.
Direct feedback: Sometimes, the best way to gauge sentiment is to ask directly. Surveys, feedback forms, and customer satisfaction scores like CSAT (Customer Satisfaction) and NPS (Net Promoter Score) offer a clear window into customer emotions, helping you fine-tune your approach.
Social listening: Social media platforms are rich with customer opinions. Monitoring tweets, comments, and reviews enables you to capture real-time public sentiment and respond thoughtfully, strengthening your relationship with customers.
AI-Powered insights: AI can analyze vast amounts of data, providing a constant pulse on customer sentiment. AI-driven tools help you identify trends and make data-driven decisions that improve customer experience and loyalty.

Turning customer sentiment into action

Understanding customer sentiment is one thing, but acting on it is where the real impact happens. When you tune into your customers' emotions and act on that insight, you can anticipate their needs, prevent churn, and even turn a negative experience into a positive one. The benefits go beyond just improving customer interactions—they can elevate your entire business.

Personalized interactions: Sentiment data allows you to tailor customer service responses, marketing campaigns, and even product offerings to individual customer preferences. This level of personalization not only makes customers feel seen, heard, and valued but also increases satisfaction and brand loyalty. In fact, personalized experiences can lead to higher conversion rates and stronger customer retention, driving long-term business growth.
Prevent churn before it happens: Spot the warning signs of dissatisfaction before they escalate. With the right tools, you can intervene early and turn things around, keeping your customers happy and loyal.
Create raving fans: Happy customers don’t just stick around—they bring their friends. By consistently acting on positive sentiment, you can turn satisfied customers into enthusiastic advocates for your brand. These advocates can drive organic growth through word-of-mouth referrals, increasing your customer base at little to no acquisition cost.

The future of customer sentiment

As technology continues to evolve, so does our ability to understand and engage with customers on a deeper level. AI, predictive analytics, and behavioral data are just the beginning. The businesses that embrace these tools won’t just survive—they’ll thrive, creating experiences that resonate on a personal level.

Best practices for getting started

Start small: Collect customer sentiment data from a few essential channels, such as surveys and social media.
Use AI wisely: Let AI do the heavy lifting by analyzing large data sets to uncover patterns you might miss.
Act fast: Use real-time insights to address customer issues before they escalate.
Keep improving: Regularly update your sentiment analysis strategy to adapt to changing customer behaviors.

Ready to tune into your customers’ frequency? Because the future belongs to brands that listen. Request a personalized Fullstory demo today.

9 AI business applications to make money and boost efficiency

The Fullstory Team — Thu, 27 Jun 2024 20:00:00 GMT

Artificial Intelligence (AI) is revolutionizing business operations across the board. As a strategic tool for innovation and operational efficiency, business AI empowers organizations to optimize tasks, improve customer engagement, and drive growth. Whether it’s product development, data analysis, content creation, or marketing, AI boosts productivity, efficiency, and scalability, giving us a glimpse into the future of business.

While AI won’t magically solve all problems overnight, practical AI solutions are powerful implementations that address specific business challenges and enhance operational efficiency. Investing time to understand and leverage AI can bring significant benefits, complementing human efforts with precision and insight.

Here are a few quick links to find the AI business use-case that aligns most with you:

So, what are the ways businesses use AI to make money?

AI unlocks new opportunities in business

AI is changing how businesses work, and it's giving companies real tools to grow and find new ways to succeed. When you use AI tools—machine learning, computer vision, and generative AI—you can change how your business runs, how you make decisions, and how you connect with customers.

AI helps companies make sense of complicated data, predict what customers want, and handle the repetitive work that used to eat up your team's time. Take AI chatbots and virtual assistants. They can help your customers around the clock, answer questions instantly, and keep people happy. For managing inventory, AI helps you keep the right amount of stock, see what you'll need before you need it, and make your supply chain run smoother. This saves you money and makes everything work better.

When you bring AI into how your business operates, you're not just getting more done—you're building a foundation for smarter choices and staying ahead of competitors. AI handles the routine stuff and shows you what matters in all that data, so your team can focus on the big picture work that actually grows your business. Whether you're using machine learning to spot patterns in how customers behave or computer vision to check quality, AI helps you stay competitive when everyone else is trying to do the same.

Here's what matters most: Companies that embrace AI solutions can cut costs, manage inventory better, and give customers experiences they'll remember.

1. Transform customer engagement with AI-powered tools

With AI’s foray into the business world, the term ‘customer engagement’ has taken on a new dimension. Gone are the days of one-size-fits-all marketing; we’re now in an era of AI-powered tools that understand and anticipate your customers' needs. These generative AI technologies are transforming digital marketing, making every interaction an opportunity to deepen the relationship with your audience.

Imagine virtual assistants engaging with customers, providing instant responses to queries, and managing complex tasks like creating digital art or writing software code—all in real-time. These AI chatbots aren’t just about efficiency; they’re a leap toward a future where AI enriches every aspect of the customer experience. As the AI gold rush continues, businesses are rapidly adopting these tools, integrating general AI assistance seamlessly into websites and platforms.

Incorporating AI into customer engagement strategies isn’t just about embracing new technology. It’s about leveraging machine learning algorithms and business intelligence to extract insights from customer data, enabling predictive analytics that transform how businesses interact with their target audience. It’s about automating repetitive tasks to free up human workers for more creative and strategic roles while ensuring high levels of employee engagement that drive the business forward. The result? A symbiotic relationship where business and technology work hand in hand to create unforgettable customer experiences.

2. Enhance data analysis and insights

Data is the new currency in the digital age, and AI is the astute banker making sense of it all. Machine learning, a subset of artificial intelligence, excels at analyzing data, turning vast amounts of raw information into actionable insights. It’s like having a crystal ball that predicts future trends and offers a roadmap on how to navigate them.

Imagine harnessing the power of deep learning, with its complex neural networks, to understand intricate patterns within data that the human eye could never catch. These machine learning algorithms work tirelessly in the background, providing accurate diagnoses of business health and enabling businesses to:

Make swift and informed decisions based on predictive analytics
Identify trends and patterns that may not be immediately apparent
Optimize processes and improve efficiency
Personalize customer experiences
Detect anomalies and potential risks
Automate repetitive tasks

By leveraging deep learning and machine learning algorithms, businesses can gain valuable insights and stay ahead in today’s data-driven world.

AI reporting goes beyond mere number crunching; it’s about presenting data in a way that makes sense. Natural language processing translates complex data into narratives and digestible performance dashboards. With powerhouses like RapidMiner, DataRobot, and Domino at the helm, companies find that their data analysis and business intelligence efforts are more efficient and precise, enabling a strategic advantage that was once out of reach.

3. Automate repetitive tasks in business processes

Efficiency is the watchword in today’s business climate, and AI is the key to unlocking it. By automating repetitive tasks, AI plays a crucial role in reducing human error, thereby streamlining processes that once bogged down productivity. From data entry to code writing, AI is reshaping the landscape of business operations, making tasks faster and improving data quality.

Consider the role of machine learning algorithms in business applications:

They’re not just tools for data processing; they’re the architects of a more efficient future.
With their ability to recognize patterns and validate data, these algorithms are elevating quality control to new heights, including implementing computer vision.
Intelligent data capture systems enable businesses to extract relevant data from various sources, minimizing errors and reducing the need for manual intervention.
This innovation extends to virtual assistants and AI chatbots, revolutionizing the complex process of maintaining consistency and integrity in business strategies.

AI’s impact goes beyond the digital realm with practical applications in automating manual tasks, such as:

Warehouses where virtual assistants guide logistics
Design studios where generative AI assists with digital art creation
Companies like JPMorgan Chase using virtual assistants like COiN to automate intricate back-office operations

These examples prove that AI’s potential in business processes is as boundless as beneficial.

4. Leverage AI for personalized marketing strategies

The digital marketing landscape is undergoing a seismic shift, with AI at the forefront of this transformation. Personalization is the new mantra, and AI is the maestro conducting this symphony of tailored content, adapting marketing messages in real-time to resonate with each unique individual. By scrutinizing customer data, AI can unearth preferences and buying behaviors, allowing for precision targeting that feels less like a sales pitch and more like a conversation.

This shift from generic to highly personalized strategies is not just a subtle change—it’s a revolution. AI-driven marketing is redefining how businesses interact with their audience. With tools like generative AI, companies can craft a variety of content, from blog articles to social media posts, each piece fine-tuned to the nuances of its intended audience. Brands like Coca-Cola are already embracing platforms like Albert to fine-tune their advertising campaigns, ensuring each dollar spent maximizes its potential.

AI’s marketing prowess extends beyond content creation to strategic ad placement. By harnessing machine learning algorithms, AI enhances the ad delivery process on social platforms, analyzing engagement data to optimize placement and timing. This is how businesses are not only reaching their target audience but also engaging them in a way that translates into tangible results. It’s a clear demonstration of AI's business value, affirming that when it comes to marketing campaigns, AI is the secret ingredient for success.

5. Improve financial operations with AI systems

The financial sector is reaping the benefits of AI’s analytical prowess. With applications ranging from predictive modeling to fraud detection in finance and iGaming fraud prevention, AI is not just improving operations—it’s revolutionizing them. For individual and institutional investors alike, AI is the compass guiding their decision-making, the tool that helps them confidently navigate the often-tumultuous waters of the stock market.

When it comes to risk assessment, AI is a vigilant guardian, constantly on the lookout for unusual patterns and behaviors that may indicate fraudulent activity. Its machine learning algorithms are the ever-watchful eyes, tirelessly scanning transaction data to ensure the security of financial operations. AI systems are crucial in managing risk and compliance, streamlining financial processes, and reducing operational costs. These are not just small improvements, they're transformative changes that position businesses for a future where precision and foresight are the keys to success.

Moreover, its implications stretch beyond the high-stakes world of finance. The practical implications of AI extend to everyday business applications, including:

Automating data entry
Enhancing data security
Safeguarding financial data
Ensuring the integrity of operations

AI is proving to be an indispensable ally for businesses in these areas, especially with the help of an AI tool to leverage AI effectively.

6. Revolutionizing supply chain management with AI applications

Supply chain management is the backbone of any product-based business, and AI is the catalyst for innovation in this domain. By optimizing inventory and production plans, AI ensures that companies are always aware of market fluctuations and consumer demand. Introducing AI into the supply chain is not just an improvement; it’s a complete overhaul of how businesses manage their logistics.

AI tools revolutionize inventory management by tracking product movement and forecasting stocking needs in ways previously deemed impossible. It’s not just about keeping shelves stocked—it’s about automating tasks like sorting and allocating resources, resulting in a well-oiled machine that operates with unprecedented efficiency. Companies like UPS lead by example with their AI-powered logistics platforms, like ORION, which dramatically reduce costs by optimizing delivery routes.

The impact of AI on supply chain management can be quantified in reduced logistics costs, improved inventory levels, and enhanced service levels, with early adopters witnessing significant benefits. Additionally, AI tools offer a level of supply chain visibility that was once unattainable, providing businesses with insights that go beyond tracking orders to understand the intricacies of their supply chain truly. In times of disruption, AI’s ability to simulate different strategies can be the difference between a swift recovery and a prolonged struggle.

7. Use AI in human resources for better workforce management

Human Resources is ripe for AI's transformative power. AI streamlines HR processes from recruitment to payroll, making departments more efficient and effective. In the hiring process, AI elevates efficiency by scanning resumes, sending outreach letters, and analyzing video interviews to identify the best candidates, as seen with Unilever’s use of HireVue.

AI’s influence in HR extends beyond hiring. It enhances day-to-day operations like payroll processing and benefits administration, providing employees with a seamless experience and helping businesses navigate complex healthcare policies. Additionally, AI boosts employee engagement by identifying workflow issues and facilitating feedback collection, fostering a more dynamic and responsive work environment.

The role of AI in workforce management is multifaceted, touching every aspect of HR, from performance management to skill gap analysis and employee onboarding. By employing AI, businesses are not just improving efficiency; they’re enhancing the human experience within the workplace, ensuring that the company and its employees grow together in a symbiotic relationship.

8. AI in IT operations: AIOps for enhanced performance

As businesses grow more reliant on technology, the importance of efficient IT operations cannot be overstated. AIOps, or Artificial Intelligence for IT Operations, stands at the intersection of technology and innovation. It uses AI and machine learning to revolutionize how IT systems are managed. By analyzing data in real-time, AIOps offers actionable insights and predictive analytics that keep systems running smoothly and efficiently.

Incorporating AIOps into IT operations goes beyond maintaining infrastructure to enhancing it. AIOps frees IT teams to focus on strategic initiatives that drive business growth by automating routine tasks such as data backups and workload scheduling. With machine learning models predicting potential issues before they occur, system reliability is greatly improved, ensuring businesses can operate without the fear of unexpected downtime.

Embracing AIOps is a visionary step that unifies diverse IT components into a cohesive environment, offering a level of visibility and performance monitoring that was once unimaginable. It’s a testament to the transformative power of AI in business, demonstrating how artificial intelligence is not just a tool for the business but an integral part of it.

9. AI-powered content generation for business growth

Content is king in the digital world, and AI is the power behind the throne. AI-powered content generation tools, like innovative AI writing tools, make the content creation process exponentially more efficient. They can generate high-quality content in a fraction of the time it would take manually, including written content and AI-generated art that captivates audiences visually. For businesses, this means a direct path to increased content marketing success and a higher return on investment, with 82% of companies reporting success after integrating AI tools into their content strategies.

The advantages of AI in content generation go far beyond efficiency. AI helps small business owners improve their SEO performance by identifying content gaps and clustering keywords, ensuring their websites rank high in search engine results. Tools like ContentShake AI can create optimized content in seconds, integrating relevant target keywords automatically for maximum impact.

AI's contribution to business strategies transcends content generation to the development of robust content marketing strategies. By scrutinizing the target audience, product, and marketing objectives, AI can propose action plans that harmonize with real-world market trends, equipping businesses with a roadmap to seize their audience's attention and foster growth.

Real-world success stories with AI-integrated products

AI integration has proven to be a game-changer for many businesses, leading to substantial improvements in efficiency, revenue, and customer satisfaction. Here are a few compelling examples of how companies have leveraged solutions like Fullstory's behavioral data platform to achieve remarkable success:

Chipotle

Chipotle's analytics team utilized AI-driven session replay technology to uncover a service error affecting a small group of customers during the order completion process. Despite its infrequency, this error led to significant revenue loss when considering Chipotle's entire customer base.

The team prioritized a fix by replicating and analyzing the issue, reclaiming over 70% of the lost revenue. Additionally, by tracking gaps in individual store inventories, Chipotle stressed the importance of item availability, resulting in an immediate decrease in unavailable items and a notable increase in mobile customer conversions.

Results:

71.5% of lost revenue reclaimed
3,500+ increase in mobile customer conversions

Read the full story here.

Gap Inc.

Gap Inc. faced the challenge of maintaining performance and monitoring issues across its custom-built online shopping experiences. Gap Inc. could swiftly diagnose and resolve site issues by gaining real-time troubleshooting capabilities, even outside standard business hours. This efficient issue resolution saved valuable time and revenue. The seamless integration of AI-powered insights into their existing tech stack enabled Gap Inc. to prioritize fixes efficiently and enhance overall operational efficiency.

Results:

10% time saved for the digital operations team
12 minutes faster session visibility compared to competitors
200 active internal users of the behavioral data platform

Read the full story here.

Getting ready for AI: revolutionizing business operations for growth and efficiency.

Integrating AI into business operations isn't just an upgrade—it's a strategic necessity. AI drives growth and efficiency across all sectors by boosting productivity, personalizing customer engagement, and delivering actionable insights. We've explored the power of AI-ready behavioral data, showing how AI can transform data analysis and product development to enhance user experiences and optimize business processes.

Whether you're a data leader or head of product development, embracing AI today positions your company for unparalleled efficiency and competitive advantage. Lean into AI as it evolves, and watch its ability to transform business operations, enhance customer experiences, streamline processes, and drive profitability.

Embrace AI, harness its potential, and watch it redefine your business landscape, creating new success opportunities. The AI journey is just beginning, and the possibilities are endless.

Learn more about Fullstory's StoryAI ➜

Trading site performance for DX insights? Here’s why you don’t need to

The Fullstory Team — Tue, 19 Jul 2022 04:00:00 GMT

As a digital experience (DX) pro, you’re well aware that site performance is important to earning customer trust.

After all, nearly 70% of consumers say that a site’s load speed impacts their willingness to buy from that company. And every little bit counts—for every one second delay, website conversion rates drop by over 2%, according to the same survey.

But you also know that consistently winning and retaining customers means continuously improving the digital experience, which requires capturing data.

Complicating matters, some product analytics and digital experience tools demand a tradeoff: logging that data can ding page performance. But, as shown by the stats above, this results in a lose-lose situation—would-be customers grow impatient at the loading delay and bounce before they can even generate the DX data you’re hoping to capture.

Luckily, that tradeoff is avoidable when you choose a Digital Experience Intelligence platform like Fullstory that has no discernible effect on site performance.

But don’t take our word for it—let’s look at two industry-leading companies who are using Fullstory’s Digital Experience Intelligence while maintaining site speed.

Gap Inc. tries on DX data without compromising performance

With an estimated 50+ million visitors each month, Gap Inc. places a huge emphasis on site speed, which is a key driver of their overall Net Promoter Score (NPS). So, when they began evaluating DXI solutions, the ability to maintain performance was on their list of must-haves.

Other tools Gap Inc. was evaluating required considerable bandwidth to operate and capture data. On the other hand, when Gap Inc. tested Fullstory, they found that Fullstory not only collected more comprehensive DX data, it also had no noticeable effect on their site performance.

The nuts and bolts of Fullstory and performance

How does Fullstory ensure that it doesn’t impact page speed? To put it quite simply, the script that Fullstory uses for data capture (fs.js) is carefully built with speed in mind. To get more technical, there are three aspects we considered in order to build the most lightweight script possible: Page rendering and interactivity, UI thread, and upload bandwidth.

For the JavaScript nerds who are still with us, let’s look more closely at each of these.

Page rendering and interactivity

Fullstory uses three techniques to ensure that its script is not allowed to delay rendering or interactivity of your site:

The script is very, very small. Zipped, it’s roughly 60KB, which is smaller than most scripts and most images. (For reference, most website images range from 200 to 500KB.)
The script is edge-cached on Google’s CDN to reduce fetch time—it typically takes under 20ms to fetch. This is especially important because browsers only allow a fixed number of outstanding resource requests at any given time. The faster the Fullstory request completes, the faster it frees up space for other resource requests.
The script is loaded asynchronously. JavaScript requires that an inline script blocks evaluation of a page until it’s loaded, which can impact page load time. Fullstory skirts this risk by installing a very small snippet that loads the full fs.js script asynchronously. For perspective, this is the same strategy Google Analytics uses.

UI thread utilization

After a browser loads a web page, its most precious resource is CPU time spent in the main UI thread. Since all JavaScript is executed on this thread, a delay of more than about 50ms can result in user-visible slowdowns. Fullstory uses a straightforward asynchronous processing technique to avoid these delays. Here’s how it works:

Fullstory uses Event Handlers that simply add all user interaction events to an in-memory queue to be processed later, rather than processing them in real time. This means the script does the minimum amount of work possible and returns control to the browser’s event loop.
When it’s time to upload a set of events to the server, Fullstory’s data capture script processes each outgoing event, which takes more time than adding an event to the queue. However, “more time” still isn’t much—this process takes about 1ms, far below the threshold of user visibility.
There are instances where the queue can take longer to process, such as when there is a significant DOM mutation that must be compressed before upload. In these cases, the data capture script does far less work than the script that produced the mutation in the first place. These instances are almost always associated with major user actions like paging a table or list view, where a few extra milliseconds isn’t noticeable. We’ll talk more about compression in the next session.

Upload bandwidth

Of course, the Fullstory script doesn’t want to push more data upstream than is strictly necessary. Fullstory’s script captures the entire document object model (DOM), which can be fairly large—though still smaller than the size of the downloaded HTML.

To mitigate this, the script compresses all HTML before it’s sent to the server using an algorithm specifically built to run efficiently in JavaScript. The script also doesn’t send external fetched images or CSS over the network, which often make up most of a page’s data. Instead, these are fetched later by Fullstory servers to use in Session Replay.

If you’re using a Digital Experience Intelligence solution that’s slowing down your site, you could be driving users away before they even have a chance to interact with your brand.

It doesn’t have to be that way. Fullstory’s DXI solution is designed to be lightweight—collecting the critical DX data you need to make smart decisions without slowing things down.

Introducing Fullcapture: The best way to unlock behavioral data insights

Scott Voigt — Tue, 29 Oct 2024 05:00:00 GMT

Fullstory was founded with a simple but ambitious mission: to help companies perfect digital experiences by understanding user behavior in real time. At the time, digital analytics relied on manual event instrumentation—a painstaking and error-prone process where every event, like a click or scroll, had to be individually defined by engineers. This process was slow, limited, and often left key insights undiscovered. We aimed to turn the paradigm of data capture on its head. We envisioned and built something radically different: a technology that would capture every user interaction automatically, without requiring teams to predict and tag each event in advance. This new autocapture approach meant anyone could instantly access complete behavioral data without the expensive bottlenecks that result from manual instrumentation.

Today, we're thrilled to see the decade-long debate over "manual instrumentation" vs. "autocapture" finally put to rest, as more digital analytics professionals recognize the benefits of autocapture. Even Amplitude finally adopted autocapture after being adamantly against it for many years. Autocapture is finally the implicitly agreed-upon industry standard for Digital Analytics solutions. Hurray! As the inventor of the Rage Click, Fullstory has led the innovation on a rich palette of experience signals. Other solutions have followed, claiming “10s” or “100s” of signals, and now many have embraced doing so “automatically” too. But the approach we’ve been perfecting over the last 10 years is much more than simply “automatic”. There is a new frontier beyond the manual instrumentation vs autocapture debate, and Fullstory is at the forefront of it with our capture and processing technology: Fullcapture.

Fullcapture vs. Traditional Autocapture

Fullstory’s autocapture differs significantly from other "autocapture" solutions in its depth, reliability, and architecture. Traditional autocapture tools typically capture a limited set of predefined user interactions on the client side, like clicks and form inputs. These methods often rely on client-side scripts, which can slow down load times and impact user experience, especially on dynamic or complex sites.

In contrast, Fullstory’s Fullcapture leverages a patented server-side model that minimizes the client-side load. By processing data server-side, Fullstory can capture and structure an extensive range of behavioral data without burdening the user's device or impacting site speed. To wit, Fullstory and a competitor were recently running simultaneously on a prospective customer’s site. The performance advantages of Fullstory were stark when comparing a few Core Web Vital (CWV) metrics:

Total CPU Time: Fullstory 287ms vs. Competitor 725ms
Script Evaluation: Fullstory 205ms vs. Competitor 652ms.

Our Fullcapture approach not only enhances performance but also allows Fullstory to collect a much more comprehensive set of experience signals beyond basic interactions, including custom error tracking, dynamic content changes, and even unique "long-tail" behaviors that are often overlooked by traditional tools. Additionally, Fullstory’s AI-powered system indexes this data for quick querying and ensures retroactive data retrieval when needed, which provides unparalleled visibility into user behavior and experience issues that might otherwise remain hidden.

Let me offer you an analogy to explain the differences among the three capturing technologies: manual instrumentation, traditional autocapture, and Fullcapture.

Imagine you are interviewing someone. You have a few different approaches you can use to capture what the interviewee says and their behaviors.

Note-Taking (Manual Instrumentation): Here, you take notes on only what you think is important. If you miss something or realize you need more detail, you can’t go back and add it—the moment’s passed. This is similar to manual instrumentation, where each interaction has to be individually tagged, requiring heavy engineering time and leading to you missing the full picture if anything goes untagged. There’s no way to capture what wasn’t planned for, and performance is impacted by each addition.

Transcription App (Traditional Autocapture): Now, you’re using an app that automatically logs basic points, saving you effort. However, it still misses additional context, like tone, pauses, or nuances. This is traditional auto-capture: it collects a specific, predefined set of interactions but sacrifices performance if you try to capture additional data. Anything not initially captured is lost.

Video Recording (Fullcapture): Finally, imagine using a video recorder. You capture everything seamlessly, with no missed details, and no extra strain on the device. In post-processing, you can retrieve any detail—tone, gestures, or expressions—instantly. Fullcapture works like this, automatically recording every interaction in rich detail, on the server-side, with no added client-side load. If a new signal is needed, you can retrieve it from the server-side data without impacting performance.

Simply put, Fullcapture gives you visibility into any user behavior that matters to you, when you need it, and how you need it, all while keeping client-side performance at peak and security and compliance requirements met, with minimum maintenance burden on our customers.

“Fullstory gives us more data and faster. Because we have more data, we can make better decisions, and because getting that data is faster, we can take corrective actions faster. If there’s an opportunity to improve or iterate something, we can do it quickly and start earning money.”
Carla Manent, Head of Product, Mammut

What’s next for Fullcapture?

Looking ahead, we’re committed to making Fullcapture the most powerful, high-performing, and secure behavioral data capture technology available. Beyond enabling your digital teams, we aim to make behavioral data more flexible—ready to train your models and power your AI agents (more to come on this!). Ready to unleash the full potential of your customer data? Fullcapture is here to make it happen.

Fullstory for Customer Agents: session context to resolve, not interrogate

Lucy Huang — Wed, 12 Aug 2026 11:00:00 GMT

Support teams don't fail because they aren't smart. They fail because every conversation starts blind. Fullstory for Customer Agents streams real-time behavioral context to your AI and human agents, so the first connection with the customer starts with already knowing what happened.

If you're in support, you've lived this moment; your deflection rate hit a wall about six months after you turned on your AI support agent.

The first stretch looked great. Password resets, billing questions, where-do-I-find-this. The agent handled them, volume dropped, and the business case wrote itself. Then the curve flattened. Not because the agent got worse, but because the tickets left over are the most complex to investigate and the right answer depends on what the customer was actually doing.

Those conversations still open the way every conversation opens: "Hi, what can I help you with?"

By then the customer has tried three times. They hit an error. They clicked the same button until it stopped feeling like their mistake and started feeling like the product's fault. And now something is asking them to recall every attempted action with precision.

Then it escalates, and your human agent opens the same ticket and starts the same conversation over.

That context gap is not an intelligence problem. Your agent reasons well, your knowledge base is comprehensive, and your support team is good at their jobs. But nobody in that conversation can see what the customer tried to do.

Our latest solution: Fullstory for Customer Agents closes that gap. It delivers real-time, AI-generated session summaries directly into your support stack, so the agent handling a conversation, human or AI, already knows what the customer did before they typed a word.

Why deflection plateaus and escalations drag

An AI support agent is a reasoning engine attached to a knowledge base. Both of those have gotten very good, very fast. Situational awareness has been the historic gap.

Without situational awareness, agents compensate the only way they can, by asking. Every question it asks to reconstruct the situation is a moment the customer can abandon the conversation, escalate, or decide the whole thing is not worth it. The questions are also the part customers describe as robotic, because a human colleague who could see the screen would never ask them.

Your human agents pay the same tax in a different currency. When a ticket reaches a person, the first stretch of their handling time goes to reconstruction: reading back through the thread, checking the account, asking the customer to walk through it one more time. None of that is solving. It is catching up. And the customer, who has now explained the same problem twice, is already unhappy before the real work starts.

David DiGiammarino, who leads partnerships at Fullstory, recently made the longer version of this argument: an AI agent is a brain, and a brain with no eyes has to open with a question. The same is true of a person handed a ticket with no context.

Scenario	Agent without behavioral context	Agent with behavioral context
First message	"Hi, what can I help you with?"	"I see the upgrade failed twice. Want me to retry it on the card ending 4412?"
Questions before diagnosis	Three to five, reconstructing what happened	Zero. The situation arrives with the conversation.
On escalation	The human re-asks the same questions	The human inherits the same context and starts at the fix
What the customer experiences	Explaining themselves to a machine	Being recognized and given an empathetic, aware response

What is Fullstory for Customer Agents?

Fullstory for Customer Agents is a packaged offering that streams behavioral context from a live or recent session into whoever is handling the conversation. It sits on Anywhere: Activation and works with any support platform that can call an MCP server or an API.

That context arrives in one of two shapes, and which one you use depends on who is consuming it.

A session summary is a narrative recap of what the person did, generated by Fullstory and shaped by a prompt profile you define. It reads the way a colleague would catch you up. That is what a human agent wants when a ticket lands in their queue, and it is what Ninety.io feeds to Fin.

Session context is that same ground truth delivered as structured events rather than prose, transformed for a language model to consume directly. Fullstory does not write a summary; your own model reasons over the raw signal. That is what you want when you have built your own agent and you want it drawing its own conclusions with full situational awareness. No signal loss and event logs built for LLMs to scan through and reason with.

Most teams end up using both, and the agent decides which to reach for. A fast recap for a simple ticket. Full context when the case is tangled enough to reason through.

Either shape carries the same underlying record:

What the person did in the session, in order, including navigation and the actions that matter to your product
Errors they hit, including the ones your instrumentation never captured
Frustration signals such as repeated clicks on an unresponsive element
User, organization, location, and device context
Any custom events you already send to Fullstory

How it works

Three steps, and only the third one takes any setup.

1. Capture

Fullcapture captures every interaction automatically from one line of code: every click, navigation, error, frustration signal, and custom event, across web and mobile. Nothing gets tagged in advance, which matters here more than anywhere else. Support conversations are about the thing that went wrong, and the thing that went wrong is almost never the thing someone thought to instrument last quarter.

2. Summarize, or hand over the raw signal

When a conversation starts, Fullstory either generates a summary of that user's session on demand (generate_session_summary) or returns the structured session context for your own model to work from (generate_context). What goes into a summary is yours to define. Prompt Manager lets you write prompt profiles that pull out exactly what your support team needs to triage, and you can return the result against a custom schema if your platform expects structured fields.

3. Deliver

The context reaches your agent at the start of the conversation over one of two paths. Fullstory for Customer Agents connects to any MCP-capable client and authenticates with OAuth. The API connector works with support platforms that expect a standard data connector. Either way, the same context follows the conversation, so when an AI agent hands off to a person, the person starts where the AI left off instead of starting over. This integration works seamlessly with any of the leading platforms on the market today: no rip and replace, just a better path to successful outcomes.

What changes for your human agents

The AI half of this gets the attention, but the escalated ticket is where handling time actually lives, and that is a human being's problem.

When a conversation reaches a person, the session context is already in the ticket. No hunting, no separate tab, no asking the customer to start over. The agent opens the queue and the first thing they read is what the customer did, what broke, and where they gave up.

That removes the reconstruction phase from the front of every hard ticket. It is the single largest recoverable chunk of average handling time in most support orgs, and it is pure overhead: the customer already lived it, the product already logged it, and the agent was rebuilding it by hand anyway.

It also changes what gets escalated. An agent who can see the actual failure writes a bug report an engineer can act on, instead of a ticket that says "customer reports checkout not working, unable to reproduce."

Why this isn't just another AI session summary

AI session summaries are becoming table stakes. Most experience analytics tools ship some version of one now, and if you are evaluating this category you should assume everyone will claim it. Two things separate a summary an agent can act on from a summary that sounds plausible.

The first is what the summary is built from. A summary assembled out of tagged events can only describe the moments someone predicted in advance. Everything between those events is inference, and inference is where support cases live: the spinner that never resolved, the validation error that flashed for half a second, the button that did nothing. Fullcapture captures the session completely, so the summary describes what happened rather than narrating around the gaps. For a support agent that difference is the difference between a correct first response and a confident wrong one.

The second is who decides what matters. A canned summary optimizes for a generic reader. Your support team is not a generic reader. Prompt Manager lets you shape the summary against your own taxonomy and domain expertise, so the agent gets the three things your team always needs to know and none of the things it never uses.

And when a summary is the wrong instrument, you are not stuck with it. An agent that needs to reason for itself can pull the full structured context instead.

What Ninety.io saw

Ninety.io runs a SaaS platform for team alignment and execution, and their support team was living the plateau. Their AI workflows lacked situational context, so customers repeated steps they had already taken and agents rebuilt user journeys before they could start resolving anything.

They used Anywhere: Activation to deliver Fullstory session summaries into Fin at the start of each conversation. Fin could then reference what the person had actually done inside the product. When a conversation escalated, the human agent inherited the same context.

Tickets that reached Fin with Fullstory context resolved 79% of the time, a 6% increase over those that did not.

"We all experience AI fatigue; AI interactions can feel less friendly. They lack the conversational warmth of a human touch. This is something we didn't realize we were missing. Fullstory adds a layer that makes Fin a more personable and friendly experience for our clients." — Taylor Paletta, Director of Support and Digital Success, Ninety.io

Read Ninety.io's full story →

Where support teams start

Four places, in the order most teams get value from them.

Cut handling time on the tickets that need reconstruction. Most of an agent's time on a hard ticket goes to figuring out what happened, not to fixing it. Context at the start of the conversation removes that step for both AI and human agents.
Push deflection past the plateau. The tickets your AI agent currently escalates are mostly the ones where it could not establish what was going on. Give it that, and a portion of them resolve without a person.
Lift resolution rate, not just response rate. A meaningful share of tickets stall because nobody can confirm what the customer actually experienced, so they sit, bounce between queues, or close without a real fix. When the session behind the ticket is available from the first reply, fewer tickets end in a workaround and more end in an answer.
Resolve the first time correctly. Repeat contacts and escalations drive satisfaction scores more than response speed does. An agent that opens with an accurate read of the situation gets more first attempts right.

Availability

Fullstory for Customer Agents is generally available now. Enterprise and Advanced plans include a complimentary Activation quota to get started, and you can add quota as your usage grows.

Interested? Request a demo to get started or explore what else support teams can do with Fullstory.

A quick guide to building with Fullstory’s Server API

Dan Kuebrich — Fri, 22 May 2026 06:00:00 GMT

Most teams think of Fullstory as something that watches what happens in the browser or mobile app. That’s true—but it’s only part of the picture. You also have information about your users that comes from outside an app experience: backend transaction or event data, non-behavioral segmenting/targeting properties, and more. And maybe you want to activate all of this data outside Fullstory’s web UI. Fullstory’s server APIs let you extend that picture programmatically: sending data in, pulling intelligence out, and building workflows that react to user behavior in real time. In practice, teams that get the most out of Fullstory are the ones who stop treating it as a passive observer and start treating it as an active participant in their stack. Here’s a practical look at what’s actually available today.

Enrich the context you already have

Your browser-based sessions are rich, but they don’t know everything. A checkout confirmation, a support ticket ID, a backend error that never surfaced in the UI—these things live in your systems, not in the session. Server-side events fill that gap. Using the Events API, you can create custom events tied directly to a specific session or user, and they get stitched into the existing session timeline. Your team ends up with a complete picture of what the user experienced, not just the clicks.

User data works the same way. The Users API lets you create, update, or enrich user records with custom properties—pricing plan, account tier, total spend, or any attribute that lives in your backend.

Choosing the right import pattern

There are three ways to write data into Fullstory via the API, and the right one depends on your volume and latency requirements.

For most real-time use cases—tagging a user when they complete onboarding, stitching in a server-side conversion event—the standard synchronous endpoints work fine. You POST to /v2/users or /v2/events, get a response immediately, and move on.

For high-volume or backfill scenarios, the Batch Import workflow is the right tool. It’s available for both users and events. You submit up to 50,000 records in a single request, get a job ID back, and then poll for status and errors asynchronously. It’s the pattern to reach for when you’re syncing historical data or running large-scale enrichment jobs.

There’s also a Stream Import option for users specifically—a fire-and-forget pattern that returns a request ID immediately and processes the record asynchronously in the background. It’s useful when you need to create user records without waiting on a synchronous response, but you still want the ability to check on status later via the request ID.

Across all three patterns, the API supports idempotency keys—a header you can pass on any create request to ensure that retries don’t result in duplicate records. If a network error causes you to retry a request, passing the same idempotency key guarantees the operation only executes once. Keys expire after 24 hours, so they’re not a long-term deduplication mechanism, but they’re exactly what you want for handling transient failures gracefully in production.

Pull intelligence when you need it

The Sessions API is where things get particularly interesting for teams building real-time workflows. It’s designed for real-time inspection and online inferencing, returning data within seconds of events being captured. Note that this won’t be your best option for data export or bulk transformation—that’s what Anywhere: Warehouse is for.

There are three ways to access session data programmatically, and which one you reach for depends on what you’re building.

1. If you need the raw event stream for your own custom processing, Get Session Events returns the full, untransformed capture for any session.

2. If you’re feeding session context into an LLM or AI model that you control, Generate Context does the work of lightly structuring and transforming that data for optimal consumption—so you’re not passing raw event noise into a model and hoping for the best.

3. And if you want Fullstory’s AI to generate the summary itself, that’s what the Generate Summary endpoint is for. You define a prompt profile—specifying what slice of the session to analyze, what context to include or exclude, and what format you want back (plain text or structured JSON conforming to a schema you define)—and Fullstory returns an AI-generated result built to your spec.

A support team might configure a profile that returns a plain-language description of what the user struggled with, automatically. A product team might define a JSON schema that structures session summaries into fields that pipe directly into a dashboard or alerting system. The prompt profiles are reusable and manageable via API, so once you’ve defined them, generating summaries at scale is straightforward.

If you're building AI-powered support or sales tooling, the Fullstory for Customer Agents MCP server addresses this same use case—surfacing session summaries and structured context to AI agents like support bots and sales copilots in real time.

Why the data flow in both directions matters

The conventional approach to behavioral data is retrospective: something goes wrong, someone pulls a report, someone watches a replay, someone files a ticket. That loop takes time—and by the time it closes, users have already churned or complained.

What this API enables is a different model, one where your systems can act on behavioral signals as they happen rather than after the fact. You’re not just giving your team visibility into what users did—you’re giving your infrastructure the ability to respond to it. The behavioral data that Fullstory captures can flow out through the Sessions API into whatever execution layer makes sense: a support platform, a personalization engine, a downstream alerting system.

Getting started

All of this is available today. Authentication uses an API key with Admin, Architect, or Standard permissions (though available endpoints vary by permission level), and all V2 endpoints are available at api.fullstory.com.pgm.c5nprx.cc/v2. For more information, explore our developer documentation.

Somebody just logged into your site. That’s good… right? Right?

Chris Haberle — Wed, 05 Aug 2026 11:30:00 GMT

Suspicious Activity signals are now generally available for Business and Enterprise plans. Tell agents, bots, and humans apart, and find the ones with ill intent.

Not long ago, you could look at the traffic on your site and assume a person was behind almost all of it. That's no longer true. In fact, earlier this year, Cloudflare announced that 57% of web traffic is now non-human. Guess what? We’re now outnumbered.

Some of this non-human traffic is AI agents acting for people: booking, shopping, and filling out forms on their behalf. And some are neither, just scripts running stolen credentials, bots probing your forms, automation built to take something that isn't theirs.

Telling these apart has become one of the more important questions companies need to answer. It’s the ability to see what's a human, what's an agent, and what's hostile, and to sort your traffic accordingly. We've been building toward that, and the first place it shows up in the product is the highest-stakes version of the question: which activity on your site is suspicious?

Move from blocking bots to understanding their intent

The old way of dealing with non-human traffic was blunt: separate bots from humans so you could block the bots. That's still useful, but a wall only tells you what to keep out. It doesn't tell you what's happening inside.

As AI agents and automated traffic become more sophisticated, the mandate for risk, digital, and analytics teams has evolved. Teams didn't just want to block traffic. They wanted fraud indicators: signals that something looks wrong, early enough to investigate before it turns into a loss. The old approach waited for problems to surface. This gives you an earlier read, so you can look closer and stop problems before they escalate.

What it actually catches

Suspicious Activity works because sophisticated fraud and automation rarely announce themselves in a single moment. They show up as patterns: the same credential pair tried across dozens of accounts, form fields filled faster than any human could type, a session with no cursor movement at all. Clues that only make sense when you see them together, across events, pages, and sometimes an entire session.

Instead of leaving those clues scattered, Fullstory surfaces them as one signal you can find and search, organized so it's useful at a glance:

By type: is this fraud, or is it automation?
By category: credential stuffing, autofill abuse, bot activity, and more

And it surfaces in real time, in the order things happened, because with fraud the sequence is the story. The tenth login attempt means something the first one didn't.

If you own fraud, risk, or forensics, this is aimed squarely at you. The earliest interest has come from the places with the most to lose: financial services, gaming and gambling, and retail, where gift-card abuse is a perennial headache. Anywhere the stakes are high and someone has a motive to game the system, an early and searchable suspicious signal changes what your team can catch, and when.

One thing to be clear about: this is not a full forensic investigation suite, and it isn't trying to be. Suspicious Activity alerts you to the signal. It's the early-warning layer that tells you where to point to a deeper investigation, whether that's a dedicated fraud tool or a hands-on forensic review by your team. Knowing where to look, and knowing it early, is most of the battle.

Get started with Suspicious Activity in Fullstory

In the Segments menu and Engagement sub-menu, you will find Suspicious Activity. Or you can just type in the global search bar "Suspicious Activity”. From there, select your event filter as AI Agent, Credential Pair, or the signal(s) that matter most to your team. After applying the search, you will be able to view the relevant sessions and trends.

Your traffic is more mixed than it has ever been. Your behavioral data should be what tells you who to trust. See how behavioral data separates humans, agents, and bad actors on your site. Book a demo →

Workflow Intelligence: See how work happens, find where AI fits

The Fullstory Team — Wed, 24 Jun 2026 06:00:00 GMT

Companies are building AI agents on top of documentation that describes how work is supposed to happen. The actual behavior, the tool-switching, the workarounds, the rework, lives nowhere. It doesn’t show up in Salesforce. It doesn’t show up in Zendesk. It happens in the browser, between systems, and it’s been invisible until now.

Workflow Intelligence captures that behavioral record and uses it to identify where AI automation will actually make a difference, not based on how a process was designed, but on how it’s being lived.

Workflow Intelligence is now available as part of Fullstory Workforce.

→ MCP was one of three products we announced at our Summer Release Event. See what else is new.

Three questions most teams can’t answer

The decision to invest in AI automation is rarely the hard part. Knowing where to focus and being confident enough to fund it is where most leaders get stuck. Workflow Intelligence is built to answer three questions that tend to block that decision:

Which workflows have the highest cost and human effort?

Without a reliable measure of where time is actually going, there’s no basis for prioritizing what to automate first.

How are those workflows actually being performed?

Documentation describes the ideal process. What employees actually do across their tools is usually different, and often significantly so.

Which parts of those workflows are suited for AI automation?

Not every step in a process is a good candidate. Identifying the right targets requires seeing the full picture of how work moves, not just what’s logged inside a single system.

What Workflow Intelligence captures

Fullstory deploys directly in the browser, which means it sees everything employees do across their tech stack: the tabs, the tool-switching, the time spent waiting, the repeated steps. Whether your teams are working in Salesforce, Zendesk, ServiceNow, or any other browser-based application, Workflow Intelligence captures the complete picture of how work moves through those systems and creates a digital twin of every workflow.

This is the behavioral data that traditional process mining tools miss. They see log data inside a single system. They don’t see what happens in between.

Total active time

One of the first things Workflow Intelligence surfaces is total active time: a true measure of the human effort associated with any given process, across every tool and every relevant employee. Think of it as the ceiling on what automation could give you back. It’s the starting point for making the right investment, and it’s a number most organizations have never had before.

From there, Workflow Intelligence aggregates and visualizes the workflows, quantifying the paths employees take through a process. Bottlenecks, rework loops, tool-switching, and deviations from the expected process all become visible and measurable.

Finding where to automate

That behavioral record can then be fed directly into an AI tool of your choice via Fullstory’s MCP connector. An automation analysis skill analyzes the data to find where employees are spending time on manual processes, quantifies the impact, and produces a ranked report of automation opportunities with implementation blueprints and success criteria.

Every recommendation is grounded in the behavioral data that produced it. Each opportunity links back to the metrics and session replays used to identify it, so teams can verify the findings before committing to an implementation.

Proving what AI is doing

The same view used to find automation opportunities becomes the scorecard after implementation. When an automation ships, you can see how the rework loops shrink and the handle time metrics drop throughout that workflow. Now, the ROI is visible and trackable over time.

For ops, support, and CX leaders tasked with proving the concrete returns of their AI investments, this delivers the ground-truth visibility they’ve been missing.

General availability

Workflow Intelligence is now available as part of Fullstory Workforce. Talk to your account team to learn more.

→ Learn more about Workflow Intelligence here.

Jaguar Land Rover + Fullstory: Why behavioral context is essential

Jay Dave — Wed, 29 Jul 2026 12:00:00 GMT

At ProductCon London, I recently had the opportunity to speak with Jim Kennedy from Jaguar Land Rover about how large organizations modernize digital product experiences.

One idea kept coming up in our conversation:

Metrics measure performance, but behavior reveals experience.

Most product teams today already have plenty of dashboards. They know when a metric moves. Conversion rates, engagement numbers, and funnel performance are all easy to track.

What is harder is understanding why the experience produced that outcome.

That is where behavioral context changes the conversation.

When teams can see how customers actually interact with their product — where they hesitate, retry, loop, or abandon a task — something important happens.

Prioritization gets clearer.

Debates turn into evidence.

When behavior becomes visible, decisions get easier

Jim shared a simple example from Jaguar Land Rover.

His team built a homepage carousel for their mobile app. It looked great in design reviews. The motion was smooth, the content hierarchy made sense, and everyone felt good about the design.

But when they looked at real customer behavior, something surprising appeared.

Customers were scrolling one tile.

Everything underneath, the content the team believed was critical, was effectively invisible.

That moment forced a choice: be design-led or be outcome-led.

The team rebuilt the experience around what customers were actually doing. They simplified the flow, prioritized the first tile differently, and personalized the content based on behavior.

Only once engagement improved — deeper scrolls, more interaction — did they scale the feature.

In Jim’s words, the shift was simple:

We moved from “we think” to “we know.”

Behavioral insight didn’t just add information. It removed debate.

Designing for ownership, not just interaction

Another example from Jaguar Land Rover came from their effort to consolidate their EV charging app and their vehicle care app.

The obvious move would have been to combine the apps and keep them as separate features.

Instead, the team asked a different question: What is the customer actually trying to accomplish in this ownership journey?

Charging is high-frequency and high-interaction. Care is lower-frequency but high-urgency.

And the two journeys intersect. If charging fails, the customer needs support immediately.

So instead of designing two separate features, the team built platform capabilities underneath the experience — charging, proactive care, and intelligence — and let the interface orchestrate them into a single ownership moment.

That shift sounds subtle, but it’s powerful.

Stop designing features in isolation. Start designing journeys.

Journeys are where friction becomes visible

Customers don’t experience products screen by screen. They experience them as journeys toward an outcome.

When teams start analyzing those journeys instead of individual pages, the most meaningful opportunities often appear between steps — the moments where intent turns into hesitation or abandonment.

Those transition points are difficult to see in traditional dashboards.

Behavioral context makes them visible.

AI will amplify the systems behind it

Of course, no product conversation today is complete without talking about AI.

Jim shared a perspective that resonated strongly with the audience:

AI doesn’t create value. It amplifies the systems you’ve built.

If the signals behind those systems are fragmented or incomplete, AI will simply scale that uncertainty.

But when behavioral signals are clear and connected across the customer journey, AI becomes far more useful. It can surface patterns faster, identify opportunities earlier, and help teams respond before small frictions become larger problems.

Strong foundations make intelligent systems far more effective.

Product leadership is becoming a decision discipline

Most product teams today can ship features faster than ever.

The real constraint isn’t speed.

It’s confidence in what to prioritize next.

Product leaders spend a lot of time aligning teams around what deserves attention. When customer behavior becomes visible across the journey, those conversations get easier.

Decisions become evidence-driven.

Alignment happens faster.

And over time, that clarity compounds.

Teams move from debating what might be happening to understanding what actually is.

Last year, I rode in a self-driving Jaguar for the first time.

If I’m honest, I was still a little petrified.

But the experience felt magical — not only because of the machine itself, but also because of the invisible software orchestrating everything behind the scenes.

That intersection between physical engineering and digital experience is where companies like Jaguar Land Rover are now innovating.

And after this conversation with Jim, I’ll admit something. I’m leaning toward Land Rover for my next car whereas my wife is still firmly Team Tesla.

We’ll see who wins.

Interested in hearing more? Watch the full conversation below:

Article originally posted on LinkedIn by Jay Dave, March 10, 2026

Find and fix has always been our bread and butter. AI makes it even better.

Irv Salisbury — Tue, 28 Jul 2026 12:00:00 GMT

The loop that catches broken experiences and repairs them is heading toward automation. How far it ultimately goes comes down to one thing: the ground truth data informing it.

My team does something specific for Fullstory's largest customers. When an experience breaks, a checkout that fails, a form silently rejects input, rage clicks spike on a page that looked fine yesterday, we find the cause and help fix it. We investigate incidents, build the analysis, and sometimes write remediative code ourselves. Find and fix is the loop we live in, and Fullstory has been good at it since the early days of session replay.

The cycle I see is similar across the customer base: a routine conversion analysis flags an error firing during online checkout far more often than it should. Session intelligence says it has to do with a third-party backend issue and surfaces what the numbers had hidden: your customers are often so loyal that they invent their own workarounds just to place an order. But workarounds are not solutions, so a fix gets shipped, funnel health is confirmed in Fullstory, and teams make this type of hygiene a quarterly habit. In the process, millions in annualized revenue can often be recovered.

That’s the story for the boardroom. What I want to talk about is the machinery behind it, because that's where AI is changing things fastest.

The loop has always looked the same

Every find-and-fix win fundamentally moves through the same steps:

Detect: An anomaly trips a threshold: a conversion drop in Adobe, a spike in rage clicks, or an error in Fullstory.
Diagnose: Someone moves from knowing a problem exists to knowing its cause, using triage dashboards, session replay, and funnel analysis.
Prioritize: Product and engineering weigh business impact against effort and write it into the backlog.
Fix: Engineers write and test the change, QA verifies it, and the patch ships.
Refine: The team retunes the alert so the next occurrence is caught earlier, with fewer false positives.

Only the first step runs on its own. The rest still require human work. That’s the part AI is starting to change, and it is worth being precise about where, because the honest answer is it’s not moving at the same pace.

The finding half is already faster and cheaper

Diagnosis is where AI is earning its place today. The old version of this had one of my analysts jumping across disconnected platforms, building custom charts, filtering error logs, cross-referencing funnels, and watching replay after replay to spot a single pattern. Hours, sometimes days.

What used to take that long, an AI agent now does in seconds by querying Fullstory directly through MCP. It runs analyses across the full dataset in parallel, evaluates funnel permutations simultaneously, and catches subtle behavioral patterns the human eye routinely misses. This is running against real behavioral data today, not sitting on a roadmap.

Ground truth data is why it works

Here's what decides whether any of this holds up: an AI investigator is only as good as the data beneath it. Feed it incomplete data, and it does not hand you incomplete answers. It hands you confident, wrong ones.

That failure takes two shapes.

Tagged systems only capture events that someone thought to define in advance, so when a bug hits an untagged element or an invisible overlay blocking a button, the AI sees a user stop and blames intent rather than the defect it failed to see.
Sampled systems keep a fraction of traffic, so a low-frequency, high-impact bug on a specific device and browser either disappears or gets inflated into a false enterprise-wide trend. And when an agent hits a gap in either, it fills the blank with probability, blaming page-load speed for a drop-off that was really an un-instrumented API error.

Fullcapture is the system that removes the gap. Every scroll, hover, rage click, dead click, and field change is captured at the DOM level, without manual tagging, giving the AI the full context leading up to the anomaly rather than just a sketch. Most AI guesses at why a user failed. Fullstory gives it the evidence.

Ground truth is not a data-quality nicety. It is the difference between an agent that diagnoses the real problem and one that hallucinates a plausible one. That is the ballgame.

That same fidelity is why the economics work. Better data is not only more accurate, but it’s also cheaper to reason over. When working from sparse or unstructured data, an agent burns reasoning tokens chasing missing context down dead ends. When fed exact, deterministic event streams straight from the source, the agent reaches the root cause in far fewer loops. Resolution shifts from a cost measured in analyst hours to one measured in compute.

Where this is headed

So where does that leave the fix itself? My honest answer: writing and shipping production code today is still a human-led function, and at the enterprise scale I work in, it will be for the foreseeable future. AI is genuinely strong at pinpointing a root cause and proposing a fix. Handing it the keys to deploy that fix unsupervised is a bar that our customers' security, compliance, and QA teams are not going to lower casually.

But the direction is not in question. One step at a time, every part of this loop that used to require a person is being handed to systems that can do it faster. The finding half is already there. The fixing half follows as trust is earned, and trust here is earned on data. A team hands more of the loop to AI only once it is confident the diagnosis is right every time, the guardrails catch a bad fix before it ships, and there is a track record to point to. Precision rests on complete, unsampled behavioral data.

From hunting for the problem to being handed it. From reports on what happened to systems that act on what is happening. Find and fix has been our craft for a decade, and AI is making it better, faster, and cheaper, starting now and widening from here. The ceiling on how far it goes is set by one thing: the quality of the behavioral data underneath it.

Behavioral intelligence: The foundation of the agentic enterprise

Adam Gunn — Tue, 28 Jul 2026 12:00:00 GMT

Every few years, Hollywood revisits Freaky Friday. The premise is simple, but it continues to resonate because it taps into something deeply human. We've all wondered what it would be like to experience the world through someone else's eyes—not just to see what they see, but to deeply understand what it feels like to be them.

That premise resonates at the core of every thoughtfully created and owned digital experience.

Every customer creates their own lived experience graph. They navigate moments of confidence and confusion, delight and frustration, momentum and hesitation. Yet by the time those experiences reach an organization, they've often been reduced to disconnected signals: a conversion event, a dashboard, a support ticket, or a CRM record. Each tells part of the story, but none captures the experience itself.

When Scott, Joel, and the founding team at Fullstory pioneered session replay almost twelve years ago, they fundamentally changed that. For the first time, teams could move beyond interpreting customer behavior through fuzzy fragments and instead could observe an authentic representation of what customers actually experienced. While early on, session replay was viewed primarily as a debugging tool, I believe its more important contribution was philosophical. It reminded us that understanding customers begins with preserving a precise record of their lived experience. This core tenet of session capture becomes even more important in the age of AI.

We've been asking the wrong question

Much of today's conversation focuses on whether AI will replace humans watching session replays. I don't believe that's the most interesting question.

The more important question is: What becomes possible when intelligent systems can learn from every customer's lived experience instead of just a tiny sample of it?

For decades, sampling wasn't a methodology; it was a constraint. Organizations collected enormous amounts of behavioral data, but human attention could only absorb a fraction of it. Researchers watched a handful of sessions. Product managers reviewed a few more. Teams drew thoughtful conclusions, but they were still conclusions drawn from samples because there was no practical alternative.

AI fundamentally changes the economics of session-based learnings. What once appeared to be an overwhelming amount of behavioral data can now become one of an organization's greatest strategic assets because it is finally possible to learn from all of it.

The future isn't one where humans stop learning from customers. It's one where humans no longer have to personally watch every customer interaction in order to learn from them.

Behavioral intelligence begins with complete behavioral data

The competitive advantage will belong to organizations that give models the richest, most complete, understanding of the people they serve.

That starts with comprehensive behavioral data.

Behavioral data is the closest thing we have to customer ground truth because it captures what customers actually did rather than what we expected them to do. Behavioral intelligence is what emerges when that ground truth is complete enough, contextual enough, and faithful enough that humans and intelligent systems can understand it, learn from it, and confidently act upon it.

This distinction matters because intelligence is only as good as the understanding beneath it. In fact, 83% of leaders say behavioral data meaningfully improves AI decision quality, yet just 44% actually feed it to AI as a primary input. That gap between what teams know they need and what they actually give their models is the gap this shift closes.

Event-based systems learn from the interactions someone decided to instrument. The only path forward for committed enterprises is comprehensive behavioral capture.

Capture, in this context, has a specific meaning. It is not sampling the interactions a team predicted would matter, nor is it a log of the events someone remembered to tag. It is the complete, structured record of what a customer actually did: every click, scroll, hesitation, and abandoned attempt, recorded whether or not anyone thought to look for it in advance. Comprehensive Capture (what we call Fullcapture™) allows people, AI agents, and systems to learn from the experience customers actually had, including the unexpected moments no one thought to measure or instrument in the tagging. Those moments often contain the most valuable lessons because they reveal the gap between how a product was intended to work and how it was actually experienced.

Unlike event-based systems, which require manual instrumentation—forcing teams to predict what to track and losing data for any interaction not pre-defined—Fullcapture functions as infrastructure, not an implementation detail. It utilizes server-side processing to create a high-fidelity, searchable replica of the full user experience, including DOM mutations and assets. This approach ensures data is always available retroactively, resilient to site updates, and private by default, decoupling the need for upfront engineering from the ability to derive future insights.

As intelligent systems become increasingly responsible for decisions and actions, comprehensive behavioral capture ceases to be an implementation detail. It becomes the foundation upon which richer behavioral intelligence is built.

That leads us to a broader conclusion: The future won't be won by the company with the most AI agents. It will be won by the company whose agents learn from the richest, most authentic representation of the lived experience. And this applies to an amplitude of lifecycles benefitting customers, users, AND employees!

Why this changes the enterprise

When agents can continuously learn from lived experience at enterprise scale, several things become possible that simply weren't before.

First, organizations are no longer forced to choose between scale and understanding. Every customer interaction becomes an opportunity to learn rather than another data point that disappears into an aggregated records schema.

Second, behavioral intelligence preserves context instead of isolated events. Funnels can tell us where customers abandoned a journey, but lived experience reveals why confidence disappeared in the first place. It preserves the sequence, intent, and nuance that transform information into understanding.

Third, intelligent systems are less constrained by the limitations of human observation. Product teams naturally bring assumptions to their work, and those assumptions are valuable. But they also create blind spots. Intelligent systems can continuously revisit conclusions against the complete behavioral history instead of relying on memory or a representative sample. The result isn't perfect objectivity; it's a broader and more complete understanding than any individual could realistically develop alone.

Finally, behavioral intelligence compounds. Every customer interaction enriches the understanding available to every future decision. As the behavioral history grows, the intelligence derived from it becomes increasingly valuable, creating an advantage that strengthens over time rather than remaining static.

The next generation of software won't just report. It will respond.

For most of the last two decades, enterprise software has been designed to help us understand what already happened. It recorded customer behavior, reported on trends, and gradually became better at explaining why outcomes occurred.

The next generation will be judged by something different.

It will understand customer behavior while experiences are still unfolding and respond before problems become permanent.

Imagine a digital experience that recognizes growing customer frustration in the moment, predicts the likely outcome, and intervenes before abandonment occurs. In other cases, that same experience heals itself: it identifies an issue, routes traffic around it, opens an engineering ticket, verifies the fix, and learns from the outcome, effectively eliminating the need for human intervention. The defining characteristic isn't automation for its own sake. It's the ability to transform understanding into action while the customer is still present.

Increasingly, those experiences won't serve only human users. They will also serve intelligent agents acting on behalf of people. Both require the same foundation: behavioral intelligence grounded in authentic customer experience and delivered in real time.

This is the shift from recording to responding, from reporting to resolving, and ultimately toward software that becomes more adaptive, more resilient, and increasingly self-improving.

The companies that win will learn the fastest

Every meaningful innovation begins with understanding. Every personalized experience, every AI recommendation, every autonomous workflow, and every product improvement depends on the quality of the understanding beneath it.

The organizations that thrive won't simply build more AI agents. They'll build agents that learn from the richest understanding of the people they serve.

That understanding begins with customer lived experience. It is faithfully preserved through comprehensive behavioral data. It becomes behavioral intelligence when that experience is understood in context. And it creates the foundation for intelligent digital experiences that are more adaptive, more personalized, more responsive, and increasingly capable of acting on behalf of both customers and businesses.

You don't build those experiences from partial understanding.

You build them with the full story.

Insider voices: Fixing travel booking and check-in friction

Nestor Barajas — Mon, 27 Jul 2026 11:00:00 GMT

Travel and hospitality teams can recite the booking conversion rate from memory. What that number rarely tells them is the why: why a traveler abandoned the funnel, bailed on check-in, or gave up on redeeming points.

We recently sat down with Nestor Barajas, Sales Engineer at Fullstory, to talk through the blind spots that quietly cost travel brands bookings and loyalty, and how behavioral data closes them.

Teams track the conversion rate, but the why lives in the behavior

Bookings dip. Check-ins stall. Most travel teams can see the conversion rate move, but the number alone doesn't tell them what to fix. Nestor Barajas describes the culprit as "digital potholes," the small breakdowns in the booking funnel that quietly send users elsewhere.

In one case, a travel brand had travelers rage-clicking on the "book now" button, a seemingly minor issue that would have gone unnoticed and deprioritized. Fullstory surfaced it, then quantified it as a $1.5 million annual conversion opportunity. The conversion rate said something was wrong. The behavioral data indicated what to fix and how much it was worth.

The cross-platform journey is the biggest booking blind spot

Travelers don't move in a straight line. They start a booking on mobile, hit a wall, and finish on desktop. They browse on their phone at night and book from a laptop the next morning.

The blind spot, Nestor says, is that most teams can't see that journey as one continuous story. Did the traveler hit an error modal ("don't worry, we're on it") mid-check-in? A dead click on "book now"? When the mobile and desktop experiences don't line up, travelers are forced to switch back and forth, and the friction reads as a sign that the brand doesn't have it together. If they don’t have it together online, would you get on their planes or sleep in their beds? Fullstory stitches those sessions into a unified view across devices, so teams can see where the handoff breaks and give travelers one streamlined experience, wherever they are. That consistency is where loyalty and trust get built.

A dollar value turns a bug into a business case

Every team has more to fix than time to fix it. What moves an issue up the list, Nestor notes, is a number attached to it. A conversion analysis in Fullstory ranks the booking friction, check-in drop-off, and loyalty redemption issues holding travelers back, and attaches a real dollar value to each one.That changes the conversation in the weekly product or engineering meeting. Instead of "this button feels off," it's "Fullstory found a dead click on 'book now' costing $1.5 million in annual revenue." You walk in with concrete data, not a hunch.

Digital frustration hides in plain sight

Frustration leaves fingerprints in behavioral data: a dead click on a CTA, a rage click on a button, a cursor thrashing across the screen. Nestor shared a telling example. Fullstory surfaced a dead click on a "check in with points" option that only applied to logged-in users, yet logged-out users could still see it. The label technically explained the rule, but travelers were confused about why the option was there at all. It's a classic case of design built around the interface instead of the person using it, and exactly the kind of gap that stays invisible until behavioral data brings it to the surface.

Customer loyalty is won in loops

The booking funnel earns the first conversion. Loyalty is earned in everything that comes after it. Nestor points to the experiences teams tend to neglect once the sale is done: the check-in flow, logging in on mobile or desktop to view points or status, and above all how easily travelers can actually redeem what they've earned. An intelligent digital experience platform gives travel and hospitality brands a unified view of these journeys, from the booking flow to check-in to the full loyalty program, so the post-booking experience gets the same scrutiny as the sale.

Personalization that travelers actually trust

Travelers expect a brand to remember they love a window seat or need a feather-free room. They're also more protective of their data than ever. The balance, Nestor says, comes down to transparency: give travelers a clear choice in what you collect, and personalize from there. Fullstory lets travel and hospitality brands take a privacy-centric approach to capturing behavioral data while still tailoring the experience to real, real-time preferences, so relevance never comes at the cost of trust.

Friction is fixable with Fullstory

Most travel teams aren't short on data. They have dashboards full of conversion, check-in, and abandonment metrics. What they're often missing is the behavioral layer underneath, the context that explains why those numbers look the way they do. That's the gap between knowing you have a problem and knowing how to fix it.

Fullstory closes that gap, surfacing the friction traditional analytics miss so travel and hospitality teams can see what's happening, understand why, and prioritize the fixes that actually move bookings and loyalty.

Ready to see it in action? Watch a product demo to see how Fullstory helps travel and hospitality brands like yours.

Shape the forest: why AI's payoff is in your processes, not the individual

Melissa Aery — Wed, 22 Jul 2026 12:00:00 GMT

AI hasn't delivered the productivity gains most companies have expected.

Tom Davenport recently laid out ten reasons why, and Bob Sutton, who wrote The Friction Project, endorsed the argument in a LinkedIn post that's been making the rounds. Their core point: companies keep chasing AI at the individual level, when the real value lies in redesigning end-to-end processes. I agree. And I'd push it one step further.

The individual was the crawl step

For the last couple of years, most companies have run some version of the same mandate: use AI in your daily work. And it delivered. Faster emails, quicker drafts, a better first pass. That was real, but it was just the crawl step, the thing that got people like me comfortable with what this technology can do. If you'd told me four years ago what we'd be doing with MCP today, I'd have said no way. Impossible.

But a pile of faster drafts isn't a transformation. Optimize person by person and you get side projects, not the unlock at scale companies actually hoped for. In life and in organizations, you have to shape the forest before the individual trees can thrive. The payoff from AI lives at that level, the end-to-end process, not the individual to-do list.

The forest has been hard to see for a reason

Here's why the forest-level view stayed out of reach: you couldn't get an accurate picture of how work actually happens. What people say they do in a process is rarely what they actually do, and documentation goes stale the moment someone finds a better workaround. So leaders interview a handful of people, sift through the available docs, and design against a stated process that doesn't match reality. Then it breaks.

The stated process gets circumvented in other ways too. Budgets get pulled. Companies develop a kind of institutional attention deficit and move on to the next thing. And the gap between what a company hoped to get from its tech and what it actually got quietly widens.

What just changed

What's different now is our ability to capture how a workforce actually works, at low lift, across every application it touches. Not what one person does in a single app, but how whole teams move across their stack to get something done for the business. Gather that at pace and scale, read the patterns in it, and you can see the shape of the work for the first time.

And it isn't only behavior. You can account for the technology you've already bought. We commit to tools, then reality drifts from the plan and we lose track of what we're actually getting from them. Map how work truly happens against what your tech can really do, and you find value sitting unclaimed inside what you already own.

Process slop is what happens when you skip this

This is where "work slop," and its more specific cousin "process slop," come from. A company builds an agent off its corporate documentation and a handful of interviews. All of it is anecdotal. Meanwhile the work keeps changing, people find better ways or quietly route around what's broken, and none of it gets written down. The agent still does something. It just isn't the thing you needed, because it was built on how someone described the work six months ago, not how the work happens today.

Think of it like planning a party. Tell an AI to plan a party and it will. But if you never mention that it's outdoors, that it might rain, and that it's for six-year-olds, you'll get something lovely for forty-five-year-olds indoors. It's a party. It just doesn't resonate. Agents lose resonance the same way when they run on generic inputs instead of the nuanced reality of how work actually happens.

The nuance is the point

I've watched this land with customers. A team is copying and pasting the same information six, seven, eight times a day. Nobody flagged it. It's just the job. Those repeated steps are the employee experience no one measures. But the second they see it laid out at scale, they start working out a better way on the spot. That's the nuance an interview never surfaces, not because anyone is hiding it, but because it's too ordinary to mention. One paper cut is nothing. At scale, it's a real tax on the business.

Then you remove the thousand paper cuts

Ground truth gives you two things: how long work actually takes, and the real path people take to get it done. From there, you design the target state, the people, process, and technology it needs, and you close the loop. What's left is the death by a thousand paper cuts, the small inefficiencies no one bothers fixing because none of them alone seems worth the effort. Remove those, ideally through automation.

The return shows up on both sides of the ledger. More from the people already on your team, and more from the technology you already pay for, because you're finally designing around what each can actually do rather than what you assumed when you bought it.

Agents aren't a "play." They're something you tend.

One caution. I keep hearing agents described as a "play." Call something a play and it turns superficial, something you run once and move past. Agents aren't that. Used well, at the end-to-end level, they can 10x what a company does. But only at that level, which is exactly Davenport's point.

And end to end doesn't stop at launch. A tree needs the right conditions and steady care, not a one-time planting. Agents are the same. You discover where one can deliver a better outcome, create it, watch what it actually delivers, and adapt it as the ground shifts. Feeding real behavior into the model through MCP is what lets you make that call with confidence, and keep checking whether the agent is still doing what you thought. Because reality will keep moving, and that constant feed of what's actually happening is what keeps an agent alive.

The bottom line

AI pays off when it runs on real behavioral data, not assumptions. The barrier was never ambition. It was visibility, an honest picture of how the work actually happens. That's what Fullstory Workforce is built to give you. Shape the forest, and the trees will thrive.

Your AI agent is brilliant. But it needs digital sight.

David DiGiammarino — Tue, 21 Jul 2026 13:00:00 GMT

Chat used to sit in the bottom-right corner of a website. It was the place you went when something broke. It often was a glorified phone tree. It usually was an afterthought. But AI is changing the way we think about digital experience.

We've spent the last few years learning a new habit: asking questions and reasoning with Claude, ChatGPT, and Gemini. That habit doesn’t switch off when we visit a retailer's site or a bank's app.

People now expect to ask their way through a digital experience, and AI agents are stepping into the spotlight. Fin's evolution from a support chatbot to a complex, reasoning customer-facing agent is a clear marker of where this is going.

But, for all their fluency, these agents open every conversation with some version of: "What can I help you with?" That question is the tell. The agent is brilliant, but contextually, it’s flying blind.

Your AI agent can think, but can it see?

Think of an AI agent as a brain. Fin has built one of the best in the business. It reasons, it decides, it holds a conversation that is genuinely helpful. But it has to open with a question, because it can't see what just happened. The agent can only see what the customer chooses to share.

It doesn't know the customer rage-clicked the same button four times. It doesn't know they've spent ten minutes flipping between two products, comparing. It doesn't know the payment stalled on the third attempt. So it asks.

And every unnecessary question adds friction at exactly the wrong moment. The agent is missing context.

That context is the behavioral data that Fullstory provides. Fullstory captures what the customer actually did. Our integration feeds every click, scroll, and hesitation as behavioral context into the agent's decision flow. Fin is the brain. We're the eyes. With digital sight, the agent doesn't have to ask why someone is frustrated. It already knows, and it can act.

From evidence to intervention: it has to be real-time

The key to all of this is real-time. For years, behavioral data was something you studied after the fact. You’d learn and fix the issues next quarter. Useful work, but it's evidence: it tells you what happened once the moment is already gone.

In a high-stakes journey (like a payment, a VIP interaction, a time-sensitive decision), you don't get a “next quarter.” You get the next few seconds. Our integration is real-time. And that turns after-the-fact analysis into a live intervention. It’s a nudge to the customer, or a handoff to a care team, while the person is still there to help.

What this looks like in practice

Think of it this way: our behavioral context layer plugs in anywhere an AI system has to make a decision about a live human being.

We watched this play out with GemBet, an iGaming operator that uses Fin. LeanConvert, a global optimization consultancy, helped GemBet build a more intelligent support ecosystem in which AI, behavioral data, and journey design work as one system.

In their recent announcement, LeanConvert shared that they embedded Fullstory's behavioral data directly into decision-making flows in Fin. The result? Real-time, contextual responses at the exact moment players need guidance. This dramatically reduces ticket volume in the moments that matter most: payments and VIP interactions.

It's this kind of result that led Fin to name Fullstory their 2026 Technology Partner of the Year.

A complete digital experience, not just a capable one

If you’re a CX or product leader: AI chat isn't a feature tucked in the corner anymore. It's becoming the main event. Soon, it may be the primary way customers move through your product. A capable agent is table stakes now. A capable agent that can see your customer’s digital behavior? That is what separates a good experience from a complete experience.

To the teams at Fin and LeanConvert: thank you for building with us. The best part of my job is moments like this one, where we impact clients together.

Celebrating you: Announcing the 2026 Customer and Partner Awards

Noah Garretson — Wed, 01 Jul 2026 18:00:00 GMT

Last year, we launched our Customer Awards to celebrate the innovators, visionaries, and changemakers using behavioral data to create unforgettable digital experiences. The response was incredible, and it confirmed what we already knew: our community is full of extraordinary people doing extraordinary work.

Now, we're back and bigger than ever. We're thrilled to announce 2026’s Fullstory Customer AND Partner Awards. See the full list of winners here.

The award categories

This year, we're honoring outstanding individuals and organizations across eight categories:

Digital Transformation Leader: Highlighting a customer who redefines operations and engagement through digital technologies.
Fastest Time to Value: Honoring a new customer who has achieved rapid ROI with Fullstory.
Data Strategist: Celebrating a customer who harnesses data to drive innovation and measurable business outcomes.
Analytics in Action: Applauding a customer who turns insights into action.
AI Innovator: Honoring a customer who integrates AI to enhance personalization, automate processes, and reimagine strategies.
Product-Led Growth Pioneer: Applauding a customer that uses behavioral data as its primary growth engine.
Commerce Visionary: Recognizing a customer who integrates data across every touchpoint to keep the customer experience elite throughout the entire lifecycle.
Digital Trust: Celebrating a customer that leverages behavioral data to simplify complex workflows while keeping security and compliance at the forefront.

Highlighting the Fullstory Partner Awards

Behind many of the most impressive Fullstory implementations are partners—solution providers, technology platforms, and consultancies—who bring expertise, integration, and vision to the table.

This year, we're excited to also recognize the partners who go above and beyond to help our shared customers succeed. We're honoring partners across five categories:

Solution Partner of the Year
Technology Partner of the Year
Rising Star Partner of the Year
Innovation Partner of the Year
Cloud Partner of the Year

What’s next

Members of the Fullstory team have nominated standout customers and partners who exemplify these award categories. We’re in the final stages of selecting the winners and can’t wait to celebrate their achievements and share their stories in July.

Here’s to celebrating you—the heart of the Fullstory community.

Stop reacting to past sessions: Closing the real-time insight-to-action gap

Kelsey Winiarski — Wed, 15 Jul 2026 16:00:00 GMT

We are opening up a limited number of spots for our early access program. Fill out our quick interest form to secure your spot and start guiding your users in the moment.

For digital experiences, timing isn't just everything; it is the only thing.

When a user encounters friction or shows a high intent to purchase, you have a matter of seconds to intervene. Yet, most digital teams are forced to operate on a delay. A user abandons their cart today, and your system sends an automated "win-back" email tomorrow. A user rage-clicks in frustration on Tuesday, and they see a helpful in-app guide the next time they log in on Thursday, if they ever log in again.

In both scenarios, your intervention arrived after the customer had already moved on mentally, physically, or financially, often purchasing from a competitor. You aren't optimizing the user experience; you're reacting to the past.

We call this the insight-to-action gap: the silent window of time between a user taking a meaningful action and your platform delivering a relevant response. Today, we want to share an early look at how Fullstory is closing that gap entirely with Real-time Behavioral Triggering.

The problem with asynchronous triggering

Most digital adoption platforms (DAPs) and messaging tools play a game of catch-up. Because they rely on batch data updates or historical session logic, they are fundamentally designed to trigger guides and offers in a future session.

But user frustration and user intent both happen in the present tense.

To actually influence user behavior, you have to meet them exactly where they are. We are actively developing the capability to trigger highly contextual, guiding content based on an end user’s real-time activity, all within their active session.

Hyper-contextual engagement in action

What does it look like to close the insight-to-action gap? Let’s look at how this plays out for an e-commerce experience using the Fullstory data layer.

Imagine a shopper browsing your catalog and adding items to their cart. With Real-time Behavioral Triggering, you can dynamically respond to their behavior the moment it happens:

Rewarding engagement: The moment the user's cart hits a specific threshold (e.g., five items), Fullstory instantly detects the data layer change and triggers a celebratory "Free Shipping!" message right on the page.
Preventing abandonment: What if that same user navigates to checkout, gets cold feet, and clicks back to the main catalog? Instead of waiting until tomorrow to send an email, Fullstory detects this exact sequence in real time. Instantly, a personalized discount code is triggered on their screen, providing the exact motivation they need to complete the purchase right then and there.

No delays. No, hoping they open an email. Just hyper-contextual engagement at the peak of user intent.

Proactively resolving hidden friction

While marketing and growth teams can use this to drive conversions, Real-time Behavioral Tafrgeting is equally transformative for Customer Success and Support.

Friction intensifies when a user is forced to leave their workflow to hunt for a help center or submit a ticket. With Fullstory, you don't have to wait for that ticket to be filed. You can intervene the second a behavioral frustration signal occurs, like a rage-click, excessive scrolling, or a long dwell time.

But friction isn't always a single, explosive event. The real power lies in tracking precise sequences of events to catch subtle, compounding hesitation before a user gives up entirely.

Consider a SaaS user trying to figure out which subscription tier they need:

Step 1: They hover near the "Upgrade Plan" button but hesitate to click.
Step 2: They navigate away to the help center.
Step 3: They highlight specific text inside a pricing plan comparison chart.

By mapping this exact behavioral pattern, you can trigger a highly targeted, proactive guide the moment the sequence completes:

"Thinking of upgrading? The Premium Plan would be a great fit for your team. Here is exactly what you will get compared to your current Standard features."

By launching a "Let me show you how" guide right where the user is struggling, you create a proactive first line of defense. Furthermore, for teams utilizing third-party AI customer agents (like Fin), intercepting these issues early with a targeted guide significantly reduces costly per-resolution agent fees and lowers overall support volume.

Closing the loop with Fullstory

The goal of your digital experience stack shouldn't just be to give you data; it should give you a system of immediate action.

By bridging the gap between identifying an in-session action and delivering an immediate response, we are giving digital teams a smarter, faster way to influence user decisions before they close the tab.

Because this feature relies on the powerful combination of our behavioral data engine and our engagement tools, Real-time Behavioral Triggering is available exclusively to customers who utilize both Fullstory Guides and Surveys as well as Anywhere: Activation.

We believe that reacting in the "next session" is no longer enough. When you remove the delay from your user guidance, you stop reacting to the past and start perfecting the present.

Want to be the first to experience it? Submit the interest form to save your spot and start guiding users the moment intent shows up.

User retention metrics 101: The most important metrics to use

Fullstory — Wed, 01 Mar 2023 07:00:00 GMT

User retention metrics are essential for any successful user retention strategy. They help you understand—and quantify—how well your users are engaging with your product or service, and show whether your efforts to increase user engagement are working.

However, there’s more to user retention metrics than simply tracking a few key numbers. To get the most value out of your user retention metrics, you need to understand which metrics are the most important and know how to interpret them.

Below, we look at what user retention metrics are, some of the key metrics to track, and why tracking the right ones is critical to your success.

Key takeaways:

User retention metrics provide insight into how customers interact with a product or service.
Important metrics include Net Promoter Score, Loyal Customer Rate, Repeat Purchase Ratio, and Engagement Rate by Channel, Segment, and Cohort.
Fullstory is a powerful tool that can help you track user behavior and maximize user retention.

What are user and customer retention metrics?

Customer retention metrics are any measurements of customer activity or engagement that help you understand how successful your user retention strategy is.

These metrics can include things like the number of clients who come back to use your product after a period of time, the average length of time it takes for those customers to return, and the percentage of users who become repeat buyers.

By tracking these numbers, you can get a better understanding of how your product is being used, which will help you make more informed decisions about user retention strategies.

Why is customer retention so important?

Customer retention is one of the most important metrics for any business. When customers, particularly your high-value customers, remain loyal to your brand and keep coming back over time, it’s an indicator that they are satisfied with their experiences and are likely to continue being repeat customers."

This is a great sign that your company has managed to build long-term relationships with customers, which can lead to increased profitability and success.

A high customer retention rate also helps you save on marketing and advertising costs since cultivating loyalty is cheaper than acquiring new customers. Harvard Business Review estimates that customer acquisition costs (CAC) can be 5x to 25x more than retaining an existing customer.

Additionally, loyal customers tend to purchase more from your business over time. Current customers spend an average of 67% more compared to those who are still new to your brand.

Therefore, it’s essential that you track your customer retention rate in order to understand how successful your customer loyalty efforts are. It allows you to measure the effectiveness of your current strategies and adjust them as needed to ensure better long-term results.

Ultimately, this will help you build trust among customers, strengthen relationships, and increase revenue.

Customer retention vs. customer loyalty

Customer retention and customer loyalty are two important concepts that are often used interchangeably but have distinct meanings. Customer retention is the measure of how many customers remain active with a business over time.

This metric focuses on the percentage of your customer base that continues to purchase products or services from a company. Customer loyalty, however, looks at the emotional connection and affinity between customers and businesses.

Conversely, this metric measures how likely customers are to promote a company’s product or service to other people—or even how likely they are to come back to a company as repeat customers.

In addition, loyal customers are more likely to spread positive word of mouth about a company’s product or service, which leads to more referrals, new customers, and ultimately more sales.

Unlike customer retention, customer loyalty is not limited to just looking at the percentage of customers that return for additional purchases. Loyalty metrics, like Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT), can provide helpful insights into customer feedback and how likely they are to recommend a business to others.

Additionally, loyalty metrics can help businesses better understand the overall happiness of their customers so that they can make improvements where necessary.

By understanding both customer retention and customer loyalty, companies can gain valuable insights into the health of their customer base and website well-being, and develop strategies for improving both.

Most important user retention rates

Retention rates are a key indicator of user engagement. While there are a variety of metrics that can be used to measure user retention, some are more important than others.

Here are the three most essential user retention rates you should track:

1. User/customer retention rate

This metric is the most fundamental and straightforward of user retention rates. It measures the percentage of customers who return to your product or service over a given time period.

Moreover, customer retention rates can be used to track long-term user engagement and loyalty. With this information, you can better determine if your current strategy is effective or if you need to make changes.

How to calculate customer retention

In order to calculate your customer retention, you first need to collect some basic data. Begin by adding up the total number of customers at the beginning of a given period.

Next, add up how many customers were onboarded during that period. Finally, add up how many customers were at the end of that period.

Then, subtract onboarded customers from those at the end and divide the total number of returning customers by the total customer number at the start of that period and multiply by 100 to get a percentage. This percentage is your customer retention rate.

Customer retention = (customers at end - customers onboarded) / (customers at start) * 100

2. Customer Churn

Whereas retention rate measures customers who are staying with your brand, churn rate measures the percentage of customers who are lost over a given time period.

This metric can be used to better understand why customers may not be sticking with your product or service and can help identify areas that need improvement.

It’s important to track this metric over time, as it will help you determine user loyalty and understand when customers may be at risk of leaving.

Calculating customer churn

Customer churn can be calculated by dividing the number of lost customers at the beginning of a period and dividing that by the total customer number at the start of that same period. Multiply the number you get by 100 to calculate your customer churn.

This calculation will provide you with an idea of how many customers have left your product or service during a given time frame.

It's important to predict customer churn before it compounds, since early signals give you time to identify the patterns driving disengagement before users leave.

Churn rate formula

Customer churn = (customers lost / customers retained) * 100

Example of customer churn

Let’s say you are a subscription-based business, and you start off with 100 subscribers in June 2023. By October 2023, you’ve lost five subscribers. This means you have a 5% customer churn rate for that period.

To calculate this, divide the number of customers who left (five) by the total customers at the beginning of the period (100) and multiply it by 100. You can then see how your retention rate (the percentage of customers who remain after a certain time frame) has changed over time.

3. Revenue churn rate

The third most important metric is the revenue churn rate. This metric measures customer lifetime value and helps you understand how much money your business is making from each customer over time.

Understanding the relationship between customer spending and time with your product can help inform decisions about marketing campaigns or changes in product pricing that could increase long-term customer loyalty and overall profitability.

Keep in mind that a number of factors can influence revenue churn rate, including:

Customer order cancellations
Canceled or downgraded customer subscriptions
Leaving your business altogether
Customer satisfaction
Pricing changes
Competitive landscape

It’s important to measure this metric periodically over time to get an accurate understanding of your business status and where you stand with your revenue churn rate.

Calculating revenue churn rate

Revenue churn rate is calculated by taking the total amount of revenue lost over a specific period of time (usually one month) and dividing it by the average monthly recurring revenue during that same period.

For example, if your business had an average monthly recurring revenue (MRR) of $1000 in January and you lost $300 in revenue due to customer churn, your revenue churn rate would be 30%: ($300 divided by $1000 = 0.3 or 30%)

Once you calculate the rate, track it over time to identify trends and areas of improvement in order to boost customer loyalty and maximize profits.

It’s important to calculate your revenue churn rate at the end or the beginning of the month so that you can quickly identify changes and react accordingly.

4. Existing customer revenue growth rate

Existing customer revenue growth rate is an important user retention metric that measures the year-over-year increase in your existing customer base’s spending.

This metric helps to indicate whether or not you’re successfully retaining customers and increasing engagement, which can help guide future marketing campaigns.

Calculating existing customer revenue growth rate

In order to successfully calculate your existing customer revenue growth rate, you’ll need to know the monthly recurring revenue (MRR) amount associated with each of your customers.

Once you have this data, you can plug it into the following formula:

Existing Customer Revenue Growth Rate = (current month MRR - previous month MRR) / previous month MRR * 100

This metric helps to show how effectively you’ve been retaining customers and increasing their spending. It also provides an easy-to-understand number that can be used to measure your success over time.

5. Daily, weekly, and monthly active users (DAU, WAU, MAU)

These are the most crucial user retention metrics for tracking engagement, as they provide an overall measure of how many users are returning to your product or service over a certain period of time and can help identify areas of growth.

DAU/WAU/MAU can also be broken down further into cohorts, which give more detailed insights like the average number of days between visits, the most active days of the week for users, and differences between user groups.

6. Customer lifetime value (CLV)

Understanding the value of individual customers over their lifetime is key to optimizing user retention and improving your product or service. CLV helps you understand how much money a customer brings in over time and allows you to identify which users are taking up more resources and which ones are driving higher revenue.

As a business, it’s best to strive for a CLV that either improves or stays the same—a CLV that is decreasing is a sign of user churn.

Calculating customer lifetime value

Calculating CLV is relatively straightforward and can be done by taking the average value of a customer over their lifetime (total revenue/number of customers) and multiplying it by the average lifespan of customers.

For example, if your gross annual sales are $12 million and you have 10,000 unique clients for the year, then your CLV would be calculated as follows: ($12 million/10,000) x (average customer lifespan) = Customer Lifetime Value

By tracking changes in CLV over time, you can better measure the long-term impact of user retention strategies on your business.

7. Product return rate

Product return rate measures the percentage of products that are returned or refunded after a purchase. It’s important to note that this metric only applies to businesses that sell physical products.

If your business sells products, it’s best to aim for a product return rate of 0% or as close to 0% as possible, as this indicates that customers are satisfied with their purchases and unlikely to return them.

Calculating product return rate

First, take the total number of products sold in a specific time frame. Then divide the number of returned or refunded products from that same time frame. Finally, multiply the result by 100 to get your product return rate.

8. Net Promoter Score

Net Promoter Score, or NPS, measures customer loyalty and ranges from -100 to 100. It’s important to be aware that NPS can’t guarantee customer retention and growth, but it does indicate the likelihood of your customers recommending you to others.

Higher scores mean higher satisfaction with the product or service, so a high NPS is an important metric for maintaining user loyalty. A good NPS score will always lead to higher customer retention.

Calculating Net Promoter Score

NPS is calculated by asking customers to rate their likelihood of recommending products or services from 0 to 10. Customers who give 9s and 10s are considered Promoters, 7s and 8s Neutral, and 6s and below Detractors.

The NPS score is then determined by subtracting the percentage of Detractors from the percentage of Promoters.

9. Loyal customer rate

This metric measures how many customers keep coming back to your product or service. It’s important to track this to see if customers are returning and help you understand why they keep coming back.

You can determine how many customers are loyal to your product by looking at the number of clients who have purchased from you more than once in a given time period.

Calculating loyal customer rate

To calculate your loyal customer rate, you first need to determine which customers have bought from your more than four times in a single year. Next, divide that number by the total number of total customers in the same period. This will give you your loyal customer rate.

10. Repeat purchase rate

The Repeat Purchase Rate is a measure of the percentage of customers that make repeat purchases. It’s a great metric for understanding how customers are coming back to purchase again and whether or not they are overall satisfied with their first purchase.

A high RPR indicates that there is an effective customer retention strategy in place and that users are satisfied with their initial purchase. A low RPR suggests that there is a need for improvement in how customers are retained and encouraged to return.

Calculating repeat purchase rate

The Repeat Purchase Ratio is calculated by taking the number of clients who have made more than one purchase and dividing it by the total customer number. This ratio can then be multiplied by 100 to convert it into percentage form.

Engagement rate by channel, segment, and cohort

Analyzing the engagement rate by channel and segment will help you understand which channels, segments, and cohorts are more successful in terms of retention. This analysis is especially useful when making decisions regarding product development or marketing campaigns.

Traditional digital surveys are dead—here’s why that's a good thing

Jason Wolf — Wed, 18 Feb 2026 13:00:00 GMT

When was the last time you actually completed a survey? Not just clicked through it, but took the time to finish it thoughtfully, believing your answers would make a difference. If you asked a room of people that question a decade ago, most hands would have gone up. Today, you’d be lucky to see even one.

This shift didn’t happen overnight or because customer feedback became less critical. It came from a fundamental change in how we understand digital experiences.

The shift from stated to observed reality

Surveys earned their place by pulling teams out of the boardroom and into their customers’ world. They were an early step in removing the invisible wall between what teams believed was happening and what customers actually experienced. For the first time, decisions could be informed by customer voices instead of assumptions.

At that time, without AI or session replay, there was no reliable way to understand the full context of digital experiences. Collecting user descriptions was the best method available.

Today, the landscape looks very different.

Teams can now capture every visit and observe real-time behavior across all users, not just those who respond to surveys. This marks a shift from stated reality to observed reality.

While stated reality is based on individual accounts, often shared after the fact and influenced by memory and emotion, observed reality captures the experience itself in real time, preserving full context.

Behavioral data enables teams to track how thousands of users navigate a journey, identify where paths diverge, and pinpoint recurring issues. This approach provides direct insight into product experiences and clarifies both causes and necessary changes.

The cost of decontextualized customer feedback

Here’s what nobody wants to admit: traditional surveys capture opinions from a minority of users, and those users are predominantly frustrated. When things are working well, most people simply move on. It’s only when something breaks that they stop to respond.

Over time, that dynamic distorts how teams understand their product. Attention gravitates toward the loudest complaints, even when those issues don’t exist at scale. Meanwhile, quieter friction accumulates across thousands of users who never felt compelled to say anything at all.

And even when feedback does come in, it often raises more questions than it answers. Without knowing what device someone was using, what screen size they were on, or what happened just moments before, the picture remains incomplete. The survey signals that something went wrong, but stops short of explaining what actually happened.

By the time teams reconstruct the experience through additional analysis and investigation, the moment has usually passed. What remains is frustration without the context needed to respond clearly or decisively.

The next evolution of customer surveys

For a long time, customer surveys worked because they met the needs of the moment. As digital experiences became more complex, however, that gap shifted. Understanding product feedback now requires understanding user behavior.

In the next evolution of surveys, feedback cannot stand alone. It must be directly connected to behavioral data so responses can be interpreted in the context of the experience that produced them. When feedback and behavior are captured together, teams no longer have to infer intent or reconstruct what happened after the fact.

That’s the foundation behind how Fullstory approaches surveys.

By placing short, personalized surveys at specific moments in the experience, such as confirmation screens or error states, teams often see response rates above 30 percent, compared to the 5 to 7 percent typical of traditional surveys. Feedback is captured while context is intact, making it more reliable and actionable.

With richer context behind every response, teams can quickly identify what’s actually affecting conversions, retention, and customer satisfaction. That means less time chasing low-impact fixes and more confidence that the work they prioritize will drive real financial results.

When responses are linked directly to session-level data, teams can see what a customer experienced before and after they shared feedback. They can determine whether an issue is isolated or systemic, and measure impact using the same behavioral signals tied to conversions, retention, and support outcomes. This context provides a near real time opportunity to engage the customer in app, on site, or via another engagement platform.

Instead of treating feedback as a separate input, user surveys become part of the same workflow teams use to analyze behavior and prioritize improvements. That makes it easier to focus on the changes that will have the most significant impact on the business.

The result is a closed loop where understanding leads directly to action, without delay or interpretation gaps. That is what future surveys need to remain relevant.

Stop guessing

Relying on traditional surveys to understand customers means working with partial information and delayed signals. In a world where user behavior can be observed as it happens, that is no longer enough.

The era of stated reality is giving way to something more reliable. Observed reality doesn’t ask users to explain their experience after the fact. It shows what actually happened, while it still matters.

Fullstory Guides and Surveys was built for this shift. By capturing product feedback in the moment and pairing it with real behavioral context, teams can move beyond assumptions and focus on the improvements that truly drive impact.

Stop debugging shadows in your Flutter app

The Fullstory Team — Fri, 15 May 2026 06:00:00 GMT

In digital experience, if your insights arrive after the customer has already left, you aren’t optimizing. You’re performing an autopsy. We call this the gap of uncertainty—that silent, frustrating window of time between a user hitting a snag and a developer realizing why it happened. For teams building on Flutter, this gap has historically been a canyon. Today, that gap closes. Fullstory now supports Flutter. Capture behavioral data and session replay from your Flutter apps and combine it with your web and other mobile sessions for a full view of your digital experience.

Fullstory now supports Flutter. Capture behavioral data and session replay from your Flutter apps.

→ If you'd like to learn more about the expanded EA program, schedule a demo here.

Why traditional tools fall short

Most mobile monitoring is a game of telephone. Because Flutter renders its own UI, traditional tools struggle to keep up. They rely on shadows—heavyweight screenshots or brittle HTML conversions that attempt to translate the UI into something they can understand. These methods are always a step removed, creating significant performance overhead and providing only a guess at the truth.

We did not want to give Flutter developers another tool that guesses. To solve this, we partnered directly with Google to build deep into the Flutter rendering pipeline itself. This collaboration ensures that Fullstory isn't just "watching" your app; it is integrated into the very engine that powers it.

Matt Wear, Senior Director of Mobile Engineering at Fullstory, explained:

"The old approaches were always a step removed—screenshots, HTML conversions, a few updates per minute. You were debugging a shadow of your mobile app. Building on Flutter's rendering pipeline, with Google, means we're not guessing anymore, and you can understand fully what your users are doing. That matters."

This direct integration is powered by Fullcapture. Unlike legacy tools that require you to pick and choose what to track, Fullcapture automatically logs every interaction, state change, and UI element within the Flutter framework without a single line of manual tracking code. This eliminates the "tagging lag"; when a new friction point emerges, you already have the data to fix it. You can ask new questions of old data and get answers instantly.

Truth without trade-offs

By building directly into the Flutter rendering pipeline, we’ve eliminated the compromises that usually haunt mobile engineering teams:

Privacy by default: Because we aren't capturing screenshots or video, sensitive data is masked at the source. You don’t have to wait for a privacy scrub to see the truth; it’s secure the moment it’s captured.
Performance without the tax: High-fidelity video capture drains batteries and eats data. Our wireframe approach is lightweight, ensuring that the act of observing the user doesn't degrade the user experience.
The why behind the what: When a user abandons a registration flow in your Flutter app, you don’t just see a drop-off stat. You see the complex pop-up that didn't close or the bottom sheet that obscured the "Submit" button.

Closing the loop

The goal of any digital experience platform shouldn't be to give you more data; it should be to give you the truth, faster.

With Fullstory for Flutter, the why behind user behavior is no longer a mystery to be solved days later. It’s an immediate, actionable reality. When you remove the delay from your insights, you stop reacting to the past and start perfecting the present.

Ready to see what's happening inside your Flutter app? Schedule a demo or contact your CSM to get started with Fullstory for Flutter.

Or, for additional information, review our session replay setup guide and Fullstory for Flutter mobile apps implementation steps.

Your site looks great to humans. AI agents see something very different.

Adam Gunn — Fri, 26 Jun 2026 06:00:00 GMT

I have the privilege of being the executive sponsor of Fullstory.com. We’ve seen the volume of bot traffic to our website increase significantly in the past six months, which correlates directly with the rollout of tools like Claude Code and Cursor. These tools have led to a greater number of agents visiting, consuming, scraping, and interacting with our digital experiences.

We’ve historically just lumped all bot traffic in a category of bad or utilitarian, but that is changing now as helpful, non-malicious agents start visiting, interacting, and in some cases purchasing on behalf of customers and consumers. We’ve all built processing, measurements, and guardrails around this traffic, but it’s also fundamentally changing the measures of site health that may go beyond a traditional cursory check of Lighthouse scores.

Your teams probably check load time and page speed regularly, validate and test mobile and responsive layouts, and maybe run an accessibility scan. You check to make sure the experience looks solid, navigation is intuitive, and the forms work.

The problem is that traditional tests and measures are about human user experience. Few people are considering an increasingly crucial question during website audits: Can an AI agent actually “see” this experience? Does it know what each element is, what each page means, and what action to take next?

That gap between what human visitors experience and what machines content actually parse is growing more consequential by the day.

Check your agent experience score →

Why AI agent experience is crucial now

AI agents aren't hypothetical anymore. They're navigating websites, filing support tickets, completing checkout flows, and interacting with customer-facing products on behalf of real users. LLM crawlers are indexing content at scale, building structured representations of the web that shape how models understand your brand, your products, and your value proposition they are informing and biasing critical LLM searches more so now than traditional SEO ever did. And behavioral analytics platforms are capturing interactions, triggering automations, and powering personalization based on what they can observe and label in real time.

All of these systems rely on the same underlying infrastructure. And for most websites, that infrastructure is surprisingly incomplete.

Most AI moves fast. But without the right structural signals to orient itself, it still gets things wrong—it misidentifies elements, misreads page intent, and misses the context that would let it act or interpret correctly or more deeply.

The problem isn't the AI. It's the site data.

The fundamentals of agent digital experience

Agent experience isn't about whether a bot can load your page. It's about whether the page communicates meaningfully once it does. There are five distinct signal types that determine this.

Element identity

Interactive elements—buttons, links, form fields—need stable, machine-readable identifiers to be actionable. Without them, an AI agent navigating your site is working without labels. It can see the button. It can't know what it does. aria-label attributes and custom data-* identifiers are the foundation here. They're also what behavioral analytics tools use to differentiate a "Get demo" click from a "Log in" click. No label means no signal—and no signal means no meaningful data.

Semantic structure

HTML gives you structural tools that most pages underuse: <main>, <nav>, <header>, <footer>, <article>. These landmarks tell AI where content starts and stops, what's navigation versus body content, and how the page is organized. A single, well-placed H1 gives AI an anchor for the page topic. A page without semantic structure is a page without road signs. Agents can traverse it; they just can't navigate it with intent.

Structured data

JSON-LD schema markup tells crawlers and AI models what your content is, not just what it says. An article without structured data is a wall of text. The same article with schema is a typed object: author, date, topic, and content type all attached before a model reads a single word. Open Graph tags play a parallel role, shaping how your content gets previewed and summarized across AI systems and social platforms.

Navigation legibility

AI agents navigate by intent. They follow links because labels tell them where those links lead. Generic anchor text—"click here," "learn more," "read more"—erases that signal entirely. Descriptive link text and properly labeled navigation regions let agents move through a site with purpose. Without them, they're guessing.

Page identity

Every page should announce what it is before a machine parses the body. A well-formed title tag, a substantive meta description, and a lang attribute on the root element are small signals with outsized impact. They're the briefing an AI reads before entering the room. Missing them doesn't break anything visibly. It just means every session starts without context.

The behavioral data connection

AI agent experience and behavioral data quality are inherently a connected problem space.

When Fullstory captures user behavior through Fullcapture, the richness of what it can surface depends directly on how well your site's elements are identified and structured. A button with no name is a data point with no meaning. A navigation region with no label is a segment that can't be isolated. A page with no schema is a session that starts as a blank slate.

The teams getting the most from their behavioral data are the same teams whose sites provide clear, stable structural signals. That's not a coincidence—it's infrastructure. Better decoration means cleaner segments, more precise automation triggers, and AI that can act on your site with confidence rather than approximation.

Most AI waits for problems to escalate before acting. Fullstory catches them in real time, but only when it can see behavior clearly enough to understand it. And the beauty of today’s Claude/Cursor-enabled workflows is that improvements in this area do not have to be a massive, resource-intensive undertaking. Fullstory has the tools and guidance as part of our onboarding to ensure your site and products are ideally decorated to provide the depth you (and AI) want and need.

Where to start

If you're not sure where your site stands, we built a free tool to surface it: the Agent Experience Index. Submit your URL and get a scored breakdown across all five dimensions, with specific findings and prioritized recommendations.

→ Try it now: Check your agent experience score.

A few patterns tend to have the highest return regardless of where a site starts:

Add aria-label to interactive elements. Buttons, links, and navigation regions. This single change improves both AI legibility and accessibility coverage simultaneously.
Implement JSON-LD on your highest-traffic content types. Even basic Organization and WebPage schema moves the needle for how AI systems understand your content.
Replace generic anchor text. "Click here" and "learn more" cost nothing to fix and degrade every machine that tries to navigate by intent.

The sites that perform best in an AI-native world are the ones that communicate most clearly to machines while still delivering the experience humans expect.

Both are achievable. Most sites just aren't doing both yet.

Prompts and use cases to get the most out of Fullstory MCP

The Fullstory Team — Thu, 21 May 2026 06:00:00 GMT

AI coding agents help engineering teams move faster than ever before, but that speed is useless if they are sprinting in the wrong direction. An AI agent investigating a bug with only a stack trace is like using a map to navigate a busy city street. It shows where the roads are, but it can’t see the pedestrian entering the crosswalk. AI models are smart, but they are blind to the human context of how users interact with the digital experiences you create.

We recently announced the Fullstory MCP, which connects behavioral context directly into your integrated development environment (IDE). Since launching, we’ve seen a massive wave of demand and usage of this solution.

By working closely with early customers, we’ve learned the exact conversational frameworks that collapse hours of manual investigation into seconds of automated triage. We’ve compiled the most effective prompts to help you get the most value out of your data using the Fullstory MCP.

→ Already using Fullstory MCP? Check out our Prompt Library for a full collection of prompts designed to enhance your investigations, streamline your workflows, and harness the full power of behavioral context.

How to structure an effective question

To ask an effective question, use as many of these four ingredients as possible:

What you want to know: "Show me drop-off on checkout"
Who you care about: "among first-time visitors"
The time window: "in the last 30 days"
What to do with it: "tell me the most common last action"

When you put it all together, a highly effective prompt looks something like this: "Show me drop-off on checkout among first-time visitors in the last 30 days, and tell me the most common last action before they left."

Use case 1: Monitoring releases and explaining metric drops

When a core metric drops or a deployment goes out, SRE and DevOps teams need to explain the impact instantly. Standard error monitoring tools trigger alerts, but they lack the behavioral context to show actual user impact. Instead of manually cross-referencing deploy timestamps with server logs, you can prompt your IDE to react to behavioral signals in real time.

Prompt for a release check: "Compare console errors and API failures on [page] in the last 7 days versus the 7 days before [release date]. Are there new errors that appeared, and how many sessions are affected?"

Prompt for a conversion drop: "Conversion on [goal event] dropped this week versus last. Show me what changed. Look for new frontend errors, new drop-off patterns, or pages that lost traffic. Walk me through the most likely causes."

Your AI agent acts on what is happening right now, pulling raw session events to tell you exactly how the deployment altered user behavior. You fix issues before they escalate, rather than waiting for a slow trickle of user complaints.

Use case 2: Uncovering frustration and tracking validation

Codebase refactors can silently break automated tracking scripts, and UI updates often introduce friction that does not trigger a traditional error log. Because Fullstory captures ground truth automatically, you can ask the MCP to scan for the biggest friction points or validate broken UI elements without relying on manual inspection.

Prompt for finding broken UI: "Show me the top rage-clicked elements across our site in the last 30 days. Are they concentrated on any specific page, and do those sessions convert at a lower rate?"

Prompt for tracking validation: "Is our defined event for [event name] still capturing correctly? Compare click volume over the last 7 days versus the prior week and flag any drop that might mean a broken selector."

Prompt to quantify a specific bug: "Show me sessions in the last 30 days where [error or API failure] occurred during [flow]. What is the abandonment rate for affected sessions versus unaffected, and how many conversions per month does it represent?"

By quantifying the exact cost of a failure, the agent provides a concise, data-backed report that justifies moving a fix to the top of your sprint.

Use case 3: Agentic root cause analysis for specific users

When a Jira or Linear ticket arrives, reproducing the bug requires understanding the exact actions the user took leading up to the failure. Rather than asking the customer for reproduction steps or manually scrubbing through a session replay, you pass the prompt directly into a profile configured for your coding agent or support assistant.

Prompt to understand the specific bug:

Pre-prompt: "You are a senior debugging assistant. Analyze the following session."
Post-prompt: "Identify the user's primary goal, extract any console errors or failed network requests, and detail the exact DOM interactions they took immediately before the failure."

Once the coding agent extracts the stack trace and the exact UI state, the most important next step is sizing the blast radius across your entire user base.

Prompt to size the pattern: "How many users in the last 30 days hit [the exact API failure or console error from the session summary]? Is this a flagged Opportunity in our org, and what is the estimated conversion impact?"

Your agent cross-references the session evidence, deduplicates similar reported issues, and pinpoints the underlying root cause in your codebase. You respond to struggle in the moment, arming your engineering team with the exact code snippet required to resolve the ticket.

Bridge the gap between speed and accuracy

Without live behavioral context, even the most advanced AI agents risk sprinting in the wrong direction. You get speed without precision.

The winners in the AI era will combine speed, context, and reflexes to steer experiences with confidence. The Fullstory MCP server is currently in private beta. Already a customer? You can get started with MCP now, or learn what it can do today to give your AI stack the digital sight it needs to act on what is happening right now. Or, if you're one of the customers already using Fullstory MCP, browse our Prompt Library for even more prompts to copy, customize, and put to work.

The semantic DOM: Why your AI needs a map, not a flashlight

Lane Greer — Thu, 14 May 2026 06:00:00 GMT

I don’t know if you’re anything like me, but my brain goes to weird places when I’m stuck in traffic. Lately, I’ve been obsessed with the impact of live maps on urban planning.

Out in West Atlanta, the roads have been under construction for what feels like a decade. As I’m sitting there, I find myself wondering who makes the call on which roads to widen. Is a civil engineer sitting in an office looking at Waze data? Are they accounting for the fact that, because everyone has a live map in their pocket, we all collectively "reroute" around congestion, creating entirely new traffic patterns on roads that were never designed for that volume?

It’s a massive shift in the profession. When I started college, I was still on a flip phone. Exploring Dallas meant a lot of trial and error (mostly error). It wasn't until my senior year that I got a smartphone with real maps. Suddenly, the resolution of the world changed. The unknown was replaced by a digital twin of the city that told me exactly where I was and where I could go.

Today, we’re at a similar “senior year" moment with AI. It’s that tipping point where a technology moves from a novelty you play with to infrastructure you rely on. But right now, we’re asking AI agents and automated tests to navigate our digital experiences in low-resolution. Sure, they can find an "Add to Cart" button—usually. But they stumble when the user wants the [blue] [button-down] [long-sleeved] shirt in size [M] that’s [in-stock] and [on-sale] for at least [25% off] with [free shipping]. Without a semantic layer, we're forcing these agents to find their way with a flashlight in a world that desperately needs a live map.

The blindness of the raw web

Most websites are built for human eyes, not machine logic. We use React, Vue, or SwiftUI to create beautiful interfaces, but the underlying code is often a graveyard of generic tags. To an AI agent—or a Computer Use Agent (CUA)—a checkout button might look identical to a newsletter signup if they’re both just <button> elements.

This is what I call the AI sight problem.

Without a semantic layer, your AI is essentially blind. It’s guessing intent based on proximity or text labels that might change during a localized A/B test. When the guessing stops, the automation breaks.

The map is not the territory (the problem of state)

But here’s the thing: Finding the button is the easy part. Any decent "digital twin" can usually stumble its way to a primary CTA.

Where the wheels fall off is the state of the page.

An AI agent looks at a grid of color swatches and size dropdowns and sees a maze. Is the [Navy] swatch [selected], or just [hovered]? Is the [Size Large] actually [in-stock], or is that subtle strikethrough only visible to a human? Is the price a [Sale] price, or is that a strike-through of the MSRP? Does the [Free Shipping] badge only apply if they add another $10 to the cart?

When we don't provide a semantic layer, we’re forcing AI to "hallucinate" the logic of our business. It's trying to infer inventory status from a CSS class like .swatch-disabled-opaque. That’s not a map; that’s a riddle.

Notes from the lab: the industry agrees

If you think this is just a niche engineering problem, look at what the major AI labs are saying. We are currently seeing a massive shift in how AI companies expect us to build for the web.

Anthropic: In their documentation for Computer Use, they explicitly point to the "Accessibility Tree" as the primary interface for agents. They argue that raw DOM is too noisy and that agents need a "semantically meaningful" view of the page to be reliable. And CUA Navigation can actually be enhanced to take into account all this additional semantic decoration.
OpenAI: With the launch of Operator, the focus has shifted to "context engineering." The consensus is that AI agent optimization (AIO) is the new SEO. If your site doesn't provide explicit, machine-readable context, the agent will simply move on to a competitor that does.
Community Standards: We are seeing the rise of the llms.txt standard—a way for websites to provide a "map" for agents. But for dynamic retail, a static text file isn't enough. You need that map baked into the live experience.

The semantic symphony: FS-Skills and Fullcapture

At Fullstory, we’ve been obsessed with this "mapping" problem long before the advent of the LLM. We recently open-sourced fs-skills, a framework for "decorating" your digital experience with semantic meaning.

But here is the truth: A semantic layer is only as good as the foundation it sits on. Traditional analytics can decorate elements, but decoration without capture is just labeling the events you’ve already remembered to configure. Only Fullstory Fullcapture is optimized to capture data in a way that can power this semantic symphony.

When you combine a decorated DOM with Fullcapture, you create a high-fidelity record that serves every system in your stack. You can enrich this record using custom properties to track specific user states, or leverage setPageProperties to define the metadata of the experience itself. Even server-side custom properties can be piped back in to ensure the "map" includes backend reality like inventory levels.

This approach changes the game for five key areas:

Agentic site navigation: When a CUA hits your site, it doesn't have to reason about whether a shirt is available. The semantic attribute tells it. This is the difference between an AI prompted to guess and an AI enabled to observe.
Analytics & session intelligence: You aren't just seeing a click; you're seeing a "stock-out interaction" on a "promotional item." The context is baked into the event, retroactively queryable without re-instrumentation.
Test automation: Your tests don't break when the UI team changes the "Out of Stock" styling from gray text to a red "X." The semantic state remains the same. No brittle CSS class hunting.
Data science & ML: This is where the warehouse comes alive. When you pipe structured behavioral data into your dbt pipelines, your propensity models finally have teeth. You aren't just modeling "users who visited a PDP"; you're modeling users who specifically interacted with [on-sale] items but were deterred by [out-of-stock] size selections.
Observability (component health): This moves us from generic site-wide errors to granular observability. By defining the component boundary semantically, you can monitor latency, error rates, and rage click frequency at the source.

The civil engineer of the digital experience

There is a recurring fear in the industry: If AI can navigate the site and analytics are automated, do we still need developers and analysts?

Think back to that civil engineer in Atlanta. Their job didn't disappear when Google Maps launched; it got more interesting. They moved from guessing where cars might go to architecting systems that respond to real-time behavioral data.

AI is a growth lever, not a human replacement. Adding a semantic layer doesn't make the developer obsolete; it makes them an architect of intent. Instead of spending 40% of your week fixing broken CSS selectors in Selenium or manually tagging events, you are building the source of truth for the entire enterprise.

Teams using behavioral data as a primary input are 9 points more confident their AI understands real user behavior than teams that aren't, according to our Digital Experiences Survey.

You’re moving from tagging janitor to data strategist.

Precision over hype

Not every problem is a generative AI problem. If you’re trying to detect if a user is struggling on your site because they can't find their size, you don't need an LLM to chat about it—you need classical data science looking at behavioral signals.

But for that data science to work, it needs to know what it’s looking at. It needs to know the difference between a user browsing colors and a user frustrated by a ghost swatch that looks clickable but isn't.

The call to action for retail tech leaders

If your digital experience is just a utility, AI will automate it away. If you want it to be a destination, it has to be built on a foundation of high-fidelity behavioral data.

Ditch the wand. Stop looking for the magic AI fix. Embrace the lab coat. Start decorating your DOM with the intent, state, and meaning your systems—and your customers—deserve.

→ Curious how we're doing this at scale? Check out our FS-Skills repository or explore the documentation for custom properties to see how we're mapping the future of digital experience.

Closing the insight-to-action gap in digital experiences

Kyle Lindsay — Wed, 20 May 2026 06:00:00 GMT

Imagine it's the Thursday before Thanksgiving. You’re in charge of a global airline’s online booking system.

On the giant monitors lining the wall, the technical dashboards are a sea of deceptively soothing green. Server uptime is 99.9%. API response times are under 200ms. There are zero spiked error logs and no alerts firing in the backend.

Suddenly, you get an alert that your call center volume spiked by 500% in 10 minutes. Apparently, customers are clicking the “Confirm Booking” button on the mobile app, but nothing happens.

You notify your on-call engineer and tell them to look into this issue fast. They say that the data is hitting the server. The database is recording the transaction. Their monitoring says the booking system is working perfectly. It must be a user error or a carrier signal issue.

This is the insight-to-action gap that you dread.

Revenue and reputation are hemorrhaging by the second, but your team is stuck, unable to do anything about it. Without a way to bridge the gap between the server saying, “Everything looks fine,” and the humans saying, “No, it doesn’t,” the team will spend the next six hours fruitlessly chasing ghosts.

Why data doesn't equal decisiveness

Most digital teams aren't suffering from a lack of data; they’re suffering from a lack of assuredness. Even with a sophisticated tech stack, two primary friction points prevent teams from moving fast enough:

1. The what vs. why disconnect

Traditional analytics are world-class at telling you what happened—conversion dropped by 12% on the checkout page—but often only highlight symptoms of deeper issues. It’s a "that’s interesting" moment that triggers a lengthy cycle: data request → analysis → hypothesis → test.

Teams end up in endless meetings, whiteboarding theories about why users are leaving. Action is delayed because no one wants to ship a fix based on a gut feeling.

31% of teams now say reviewing and validating AI outputs is their single biggest obstacle, according to our 2026 Digital Experiences Survey.

2. Needle-in-the-haystack fatigue

Many tools attempt to solve the why with session replays. Seeing exactly what happened can be helpful, but if you have 10,000 sessions, watching them to find the friction is a full-time job. Teams eventually turn away from replays because the time to insight is simply too high. They lose the ground truth and start making decisions based on aggregated averages that hide the actual human struggle.

The ideal state: a system of action

In a perfect world, your digital experience isn't just recorded; it’s understood. The ideal state for a high-velocity team isn't just better dashboards—it’s a system of action defined by four pillars:

Verifiable truth: You have access to every interaction without having to manually tag events beforehand. Every click, swipe, and scroll is verified with real, observable data. If a question is asked today about a trend from three weeks ago, the data is already there.
Automated prioritization: Instead of hunting for needles in a haystack, the system lays the needles out for you. It identifies friction clusters and ranks them by business impact. You don't fix the bug five people hit; you fix the one causing $35,000 in lost revenue.
Instant contextual clarity: When a chart shows a dip, you don't guess. You click the data point and immediately see the exact user experience that created it. The transition from trend to true story takes seconds.
A shared language: Product, engineering, and support all look at the same screen. They move from 'I think' to 'I see' instantly.

Fullstory bridges the gap between finding and fixing

Fullstory is more than a system of record; it’s a system of action. While competitors focus on storing data points, Fullstory is built to eliminate the time between spotting a trend and shipping a fix.

From assumptions to clarity in a few clicks

Imagine your analytics dashboard shows a 15% drop-off at the "Invite Your Team" step of your onboarding flow. In a traditional setup, the product team might spend a week hypothesizing—Is the copy too aggressive? Are there too many fields?—and often ends up with more questions than answers.

Fullstory’s agentic system curates a prioritized list of opportunities with the biggest impact. This means your product manager isn’t looking at a spreadsheet; they can directly identify issues affecting revenue. For example, they might discover that certain mobile users can’t click the “Confirm Booking” button due to a CSS conflict with the “Save Trip” option, which they can verify with session replay summaries and examples.

Closing the engineering loop

Friction usually intensifies when the product manager hands off insights to engineering teams. Usually, this involves a JIRA ticket with vague steps to reproduce the issue.

But with Fullstory, the product manager embeds the direct session link inside the ticket with one click. The engineer doesn't just see the video; they use integrated DevTools within the replay to see the console logs, network requests, and CSS state at the exact moment of failure. There is no "I can’t reproduce this." The bug is caught red-handed.

Take confident action with Fullstory

Data is just a record of the past. Action is what shapes the future.

If your current stack leaves you standing in a "flooded living room" while your dashboards stay green, you aren't lacking data; you’re lacking context. Fullstory provides the verifiable truth that turns a war room standoff into a “fixed that” Slack notification.

Stop guessing why your customers are leaving. See for yourself.

→ Request a demo to see Fullstory in action.

Why mobile session intelligence is table stakes

The Fullstory Team — Mon, 01 Jun 2026 06:00:00 GMT

Mobile apps have become essential for everyday activities like shopping, banking, traveling, and learning, which means product teams can't afford to have blind spots in mobile user behavior.

Mobile session replays show how real users move through an app: every tap, swipe, scroll, and screen change. They add the missing why to quantitative data, crash reports, funnels, user feedback, and real user monitoring. Without mobile session intelligence, teams are flying blind in a platform that may drive their most important revenue and retention moments.

Only 44% of digital experience teams use session recordings or behavioral data as a primary AI input, according to our 2026 Digital Experiences Survey.

In this article, we'll explain what mobile session replay is, how it works, what it means for app performance and privacy, and why Fullstory sets the gold standard across web and mobile.

What is mobile session replay?

Mobile session replay visually captures and reconstructs the user journey within native mobile apps. It allows teams to see exactly how users interact with an app by recording actions like taps, swipes, and scrolls, as well as the context and sentiment behind those actions.

Unlike conventional screen recordings, mobile session replay provides a detailed, interactive experience that highlights not just what users do but also how they navigate through various screens. By logging user interactions, session data, and UI changes, it creates an in-depth visual representation of user behavior, helping teams better understand user experiences.

A typical mobile session replay may include:

Gestures: taps, swipes, scrolls, long presses, and repeated attempts
UI context: screen states, transitions, menus, modals, and layout changes
Technical context: device, operating system, network requests, errors, and performance data
Behavioral context: where users engage, hesitate, go back, or drop off

While web applications often rely on the Document Object Model (DOM) to dynamically represent and interact with the structure of a webpage, mobile applications require purpose-built capture methods because there is no standard web DOM in native mobile interfaces.

How mobile session replay works under the hood

Most tools use screenshots, simplified wireframes, or native rendering data. Those choices affect fidelity, storage, privacy, and minimal performance impact.

Screenshot-based tools capture images at intervals and stitch them together. This can create large files, miss key moments, and increase the chance of exposing sensitive data. Different methods for capturing mobile session data include creating replays from screenshots, which can lead to large file sizes and incomplete experiences, and using wireframes, which simplifies the capture but may miss key moments.

Wireframe or view hierarchy approaches map the app UI into boxes, labels, and element positions. This is lighter than screenshots, but it can miss animations, custom components, and complex layouts.

A modern system usually works like this: A lightweight SDK sits in the mobile app, logs interaction and UI changes, batches data, supports offline mode when needed, uploads securely, and renders the replay in a browser-based viewer.

Fullstory for Mobile Apps takes a different path. Fullstory uses draw instructions, never screenshots, to reconstruct what the device rendered. That means higher-fidelity mobile replays, better privacy, and more efficient storage. Fullstory also supports minimal-config auto-instrumentation for SwiftUI and Jetpack Compose, plus support for stacks like React Native.

In short:

Screenshots: visually literal, but heavier and riskier
Wireframes: efficient, but often incomplete
Fullstory draw instructions: high-fidelity, screenshot-free, private by design

Key benefits of mobile session replay for product and engineering teams

Mobile session replay tools offer several advantages, including:

Enhanced debugging: Observe exact user actions that led to errors rather than relying solely on logs, allowing developers to filter sessions with crashes, rage taps, or performance defects.
Holistic team collaboration: Enable product, design, growth, support, and engineering teams to identify usability issues and optimize user experiences based on real user behavior.
Conversion rate improvements: Analyze session replays to pinpoint where users drop off in conversion funnels, facilitating targeted improvements that can increase conversion rates.
Visual user insights: Gain a visual representation of user sessions to understand behavior and identify friction points that lead to abandonment or dissatisfaction.
Comprehensive understanding: Move beyond simply replaying sessions to combine qualitative insights with quantitative data and app performance context, creating a complete picture of customer behavior.

Privacy, security, and performance considerations in mobile session replay

Session replay for mobile applications does raise a few important questions: How do you protect user data? What happens to sensitive information? Will capture slow the app?

First and foremost, user privacy is a big deal. Consumers today are more concerned than ever about their personal information getting into the wrong hands. That's why it's crucial to mask sensitive data such as passwords or personal identifiers when using these tools. This isn't just a good practice; it’s essential for compliance with regulations like GDPR and CCPA.

To address privacy concerns, look for tools that offer features like custom data masking, field exclusions, and blocklists for sensitive views. On-device redaction ensures that sensitive information doesn't even leave the user’s device. Plus, clear data retention policies will help you manage how long you keep user data, aligning with legal requirements and internal standards.

Performance is non-negotiable; if a replay tool slows down your app, your users will get frustrated and leave. That’s where lightweight SDKs come into play. These are designed to minimize resource usage, ensuring the recording process runs smoothly without disrupting users’ experiences.

Why Fullstory is the gold standard for mobile session replay

Not all mobile session replay solutions are built the same. Here’s how Fullstory sets the gold standard:

Privacy by design: Fullstory captures draw instructions, never screenshots. The visual replay you see is reconstructed from the UI signals the device itself rendered, not a literal recording of the screen. Sensitive text is processed in-device and discarded before it ever leaves the user's device, while visual attributes like color, size, and position are preserved.
Lightweight SDK: Most mobile teams have been burned by analytics SDKs that bloat app size, drain battery, or slow down launches. Fullstory's typical payload is less than 100 KB per minute. Lightweight enough that users never feel it, rich enough to give product teams the full picture of how mobile users behave.
One platform for web and mobile: Fullstory unifies behavioral data across both web and mobile apps, so you can trace a single user from a web touchpoint to an in-app conversion, or from a frustration signal in the app to where it spilled into support tickets and churn.

Ship mobile experiences that convert with Fullstory

Every tap, scroll, and frustration signal is behavioral context you can use to build better mobile experiences. Fullstory captures these signals automatically, turns them into instant understanding, and gives product and engineering teams the insights they need to fix friction and lift conversion in the moment.

Ready to see it in action? Request a demo today to learn more about Fullstory for Mobile Apps.

How to use customer feedback to build a product roadmap

The Fullstory Team — Tue, 30 Jun 2026 06:00:00 GMT

A feedback-driven product roadmap will beat intuition-only planning every time because users expose the friction your team has stopped seeing. When you build a product, you instinctively navigate the "happy path" because you know how it's supposed to work. Customers don't have that bias—they use your product in unexpected ways, revealing the UX blind spots you didn't even know existed.

But there’s a catch: the best roadmaps are not customer wishlists. If you blindly build exactly what customers ask for, you end up with a bloated, reactive product. Picture a team shipping a complex reporting module because three enterprise customers demanded it. Six months later, adoption is flat. The real pain wasn't more reports; it was "I need to share a clean weekly summary with my CFO."

An effective product roadmap uses customer input as evidence for prioritization, connecting that evidence to product strategy and measurable outcomes. Here is how to systematically turn raw comments, support tickets, and feature requests into a roadmap that drives business impact.

Start with a vision and strategic outcomes

Customer feedback only makes sense when it's anchored to a clear product vision. Without a strategic direction, your roadmap becomes a chaotic reaction to whoever complained the loudest last week.

Think of your product vision as your ultimate guide. For example, if your goal is to build the fastest, most frictionless mobile checkout experience, start by translating that high-level vision into 12- to 18-month strategic themes with specific, measurable outcomes:

Reduce mobile checkout friction: Increase completion by 3% by Q4.
Streamline the guest flow: Lift guest-to-registered account conversion by 15%.
Speed up payment error resolution: Cut issue diagnosis time by 20%.

Once those outcomes are set, they become your strategic filter. Every piece of incoming feedback must map back to at least one of them. If a feature request doesn't fit the plan, it belongs in a parking lot, not the release plan.

However, remember that this is a two-way street. While your vision filters day-to-day feature requests, aggregate feedback should continuously shape your long-term strategy. If your parking lot starts overflowing with deep, systemic user pain points that sit outside your current goals, that is your signal to zoom out and evolve the product vision for the next planning cycle.

Systematically centralize your feedback

Feedback is fragmented by default. It lives in Zendesk tickets, sales win/loss notes, quarterly business reviews, NPS surveys, and community forums.

When Voice of the Customer (VoC) data is this scattered, it breeds recency bias—you end up prioritizing whatever issue you heard about most recently. The fix is to aggregate everything into one centralized system and apply a simple taxonomy:

Problem area: onboarding, billing, analytics
Feature area: exports, dashboards, admin controls
Customer segment: SMB, mid-market, enterprise

A clear taxonomy prevents your backlog from turning into a junk drawer. For example, 200 export complaints shouldn't become 200 backlog items. Consolidate them into one “data export reliability” opportunity. The payoff is real; companies with structured feedback triage resolve 40% more customer issues and ship requested features significantly faster.

Translate requests into underlying jobs to be done

Customers ask for solutions, but it is a product manager's job to uncover the problem. Raw requests need translation.

To strip away the "what" and find the "why," use a framework like the 5 Whys. For instance, if an enterprise customer demands "Single Sign-On (SSO)," the chain of Whys might look like this:

Why do you need SSO? Because logging in manually is a bottleneck.
Why is it a bottleneck? Because our IT team has to manually provision and audit every new account.
Why do they do it manually? Because we lack automated user lifecycle management.

Suddenly, what sounded like a simple login feature is actually an administrative and security overhead problem.

The same rule applies to a request for "dashboard customization." That rarely means your users want prettier charts; it usually means they need role-based reporting so they can quickly share specific metrics with leadership. Translating raw requests into these underlying "jobs-to-be-done" ensures you are solving core business pain points rather than just treating symptoms.

Prioritize using an outcome-based framework

Deciding what not to build matters as much as feature development. Once you have translated the feedback, prioritize it using a structured framework like RICE (Reach, Impact, Confidence, Effort).

By scoring feedback, your decisions become objective. For example, mobile checkout fix that reaches 45% of users and takes two sprints to build will mathematically beat a niche admin setting requested by a single account.

Next, map these prioritized problems into an agile Now, Next, Later framework:

Now: Fix the payment error at checkout (High reach, high impact).
Next: Streamline the guest-to-registered account flow (Medium effort, aligns with strategic vision).
Later: Explore role-based reporting for enterprise dashboards (High effort, placed in the parking lot for now).

This agile roadmap style gives key stakeholders clarity without pretending every dependency is known months in advance. It supports experimentation and continuous learning over rigid Gantt charts.

Close the loop with your customers

Closing the feedback loop builds trust. It proves to your users that their insights don’t just disappear into a product backlog black hole.

When an item moves from "Next" to "Now", and especially when it finally ships, notify the customers who originally raised the issue.

Crucially, frame your release notes around the problem they helped you solve, not just the feature you shipped. Sticking with our e-commerce example, instead of writing, “We updated the payment gateway API,” you should say: “You told us the payment screen was timing out during checkout. Here’s how we fixed it.”

Your product marketing team can then take those updates and turn them into launch materials that speak directly to customer value, rather than just sounding like internal engineering announcements.

How Fullstory Guides and Surveys build better roadmaps

The biggest challenge in building a feedback-driven roadmap is the context chasm: traditional feedback is retrospective. By the time a user completes an email survey or submits a support ticket, the context for why they were frustrated is lost.

Fullstory bridges this gap by connecting what customers say directly with what they experience.

With Fullstory Guides and Surveys, you can capture contextual feedback at the exact moment friction appears. If a user repeatedly struggles with that mobile checkout flow, abandons a complex filter, or encounters an error, you can trigger a targeted survey to ask them why, right there in the UI.

Instead of an aggregated, generic complaint filed days later, you capture highly specific, in-the-moment feedback tied directly to user behavior. You can then link those behavioral insights straight back to your "Now, Next, Later" roadmap, ensuring every product decision is backed by undeniable evidence.

Stop guessing what your users want and start asking them in the moment. Get a demo of Guides and Surveys today to see how behavioral data can power your next product roadmap.

Heatmaps: How to create, use & analyze them

Fullstory — Tue, 04 Mar 2025 07:00:00 GMT

Key takeaways

Website heatmaps are a powerful way to understand what your users do on your website—where they click, how far they scroll, what they engage with, and more.
Click maps and scroll maps are the most popular types of heatmaps, but there are many other types.
UX designers, marketers, analysts, and product teams can all benefit from utilizing heatmaps.

What is a heatmap?

Heatmaps are color-coded visual maps that overlay a page to show where visitors or viewers click, tap, hover, and scroll, instantly revealing high- and low-engagement areas you can optimize for usability and conversions.

Heatmaps can be created manually, but most are generated by heatmapping software.

Heatmaps date back to the late 1800s when Toussaint Loua used a shading map to show social demographic changes across Paris.

Computer heatmapping technology was first trademarked in the early 1990s by software designer Cormac Kinney, who created a tool to visually display real-time financial market information.

Heatmaps can use different color schemes, like grayscale or rainbow. In grayscale maps, darker areas show more activity, while lighter areas show less.

Rainbow maps are often preferred because people can see more color variations than shades of gray. In rainbow maps, warmer colors like red and orange highlight popular areas, while cooler colors like blue and purple show less-used sections

What do heatmaps show?

Heatmaps show how users interact with your website. They provide visitor behavior insights that can help you improve conversion funnels, increase conversion rates, reduce bounce rates, and even boost sales.

For example, a map may reveal that nobody is tapping one of your most important page elements, leading you to consider a new design to increase engagement.

Different types of heatmaps provide insights into different aspects of user behavior, such as engagement, customer sentiment, and interaction patterns on your website.

How do heatmaps work?

Heatmaps generally fall into two categories: interaction and attention.

Interaction heatmaps measure different types of engagements and use tracking codes to record interactions between a user and a website, like clicks, scrolls, and mouse movements.

Attention heatmaps monitor how users look at your website content by monitoring or predicting their eye movements.

Types of website heatmaps

Several types of website heatmaps are designed to reveal specific aspects of user intent and behavior. Below is an overview of some of the most common types.

Click maps

Click maps are one of the most widely used types of website heatmaps. They show where users click on your page, offering valuable insights into how people navigate your site.

Click maps help you:

Identify and monitor the effectiveness of CTA buttons.
Pinpoint and remove areas causing user friction or increasing bounce rates.
Uncover the most clicked-on sections of your site.
Track conversion rates for both new and returning visitors.

However, click maps aren’t perfect. Be mindful of potential issues like accidental logins, frustrated clicks, or device compatibility problems that can skew data analysis.

Like any heatmap, click maps work best when combined with other data sources. Pair them with insights from product analytics tools, user analytics platforms, Google Analytics, UX surveys, and more to understand the full story behind user behavior.

Further reading: Behavioral data doesn’t just show what users do—it reveals why they do it. Learn what behavioral data is and why it’s important.

Scroll maps

Scroll maps are a visual representation of visitors’ scrolling behavior on a web page. They tell you:

How many visitors scrolledto the bottom of a page.
How many visitors scrolled but stopped short of the bottom of a page
How many visitors abandoned a page.
What percentage of users scrolled to certain depths on a web page (for example, 34% of visitors scrolled 50% of the way down a page, 13% of visitors scrolled all the way to the bottom, etc.).

Scroll maps help you see how far visitors scroll before leaving a page, revealing whether important content is being ignored and helping you understand how to maximize a page’s impact. If, for example, you find that only 25% of visitors scroll below the fold (below where your screen cuts off the rest of the page), you know that you need to put the most important content at the top of the page.

Scroll maps can also reveal false floors (or false bottoms), where visitors mistakenly believe they’ve reached the bottom of a page when they haven’t.

When using scroll maps, always look at metrics for both desktop, tablet, and mobile users, as behavior can vary across devices.

Mouse-tracking heatmaps

Mouse-tracking maps show general mouse movement across a web page, helping you understand customer sentiment. They can reveal where visitors are hesitating, hovering, or thrashing their cursor.

Mouse-tracking can help:

Spot hover patterns that indicate friction or frustration.
Optimize pages with complex or dynamic elements.
Evaluate the relevance of search results based on click patterns.

It’s important to remember that cursor movement doesn’t always align perfectly with where users are looking, which can lead to misleading insights.

Eye-tracking heatmaps

Eye-tracking heat maps user sensor technology to track users’ eye movements, blinking, and even pupil dilation as they interact with a web page. Eye-tracking heatmaps can help you:

Identify where users' attention is naturally drawn.
Position key elements in areas that receive the most visual focus.
Improve layout usability by understanding gaze patterns.

However, eye-tracking tools are expensive, often resulting in data based on a small user sample. Additionally, some users are wary of eye tracking and use camera covers to avoid being surveyed.

Error click heatmaps

Error clicks occur when a user clicks on an element of a web page that triggers an error, like a client-side JavaScript issue or a console error.

Error-click maps help you:

Quickly uncover bugs and glitches.
Investigate recurring console errors or exceptions.
Understand which errors are impacting users the most.

By identifying and addressing these issues, error-click maps help ensure your website runs smoothly and meets user expectations.

Rage Click™ heatmaps

Rage Click™ heatmaps highlight areas of user frustration by tracking when users rapidly click or tap on an element of your site.

Rage click maps help you:

Identify elements causing user frustration.
Spot malfunctioning buttons or static elements that are misinterpreted as interactive.
Prioritize fixes to reduce friction and improve user satisfaction.

Dead click heatmaps

Sometimes, users mistake unclickable elements on a website or app as buttons and tap them, expecting something to happen—this is called a dead click.

Dead click heatmaps can help you:

Identify elements users mistakenly think are interactive.
Spot opportunities to clarify design and reduce confusion.
Track behavioral trends over time to uncover new optimization opportunities.

Addressing dead clicks helps create a more intuitive user experience and improves conversion rates.

AI-generated heatmaps

Instead of showing actual user behavior, AI-generated heatmaps use artificial intelligence to predict user behavior.

AI-generated heatmaps can help you:

Predict where users will look when they first land on your site.
Determine the best placement for critical elements like CTAs.
Cut costs, as they are generally more affordable than eye-tracking heatmaps.

These heatmaps are up to 95% accurate, but they may not perform well on sites with low traffic or engagement.

You don’t have to choose just one type of heatmap. Leveraging various types can give you a well-rounded understanding of user behavior and help you make more informed decisions for your website.

Why (and when) to use a heatmap

Website heatmaps help you track visitor behavior visually so you can make your improve your site around your goals. They highlight which site areas people are engaging with, which areas are working, which aren’t, and which areas your users are avoiding.

These insights help you make data-driven changes — you're acting on data, not just guessing.

At many organizations, website heatmaps are part of larger conversion rate optimization (CRO) efforts, since they’re mainly used to improve conversion rates.

Website heatmaps can help you determine if:

There is important content on a page that visitors aren’t getting to
Users are having trouble finding or seeing certain CTAs
Users are experiencing issues based on device type or browser
Non-clickable elements are creating distractions that harm conversion (as shown in the video below)

Further reading: How to use session replay for conversion rate optimization

How to create a heatmap of your own

There are plenty of heatmapping tools to choose from, so it’s important to compare your options. Start by identifying which pages you want to analyze and what type of heatmap will provide the insights you need.

Look for a tool that offers multiple types of heatmaps, not just scroll or click maps. Having access to a variety of maps allows you to combine insights and make more informed decisions for your site.

Heatmap tools

Some popular heatmapping tools include:

Fullstory
Smartlook
EyeQuant
Attention Insight
Dragonfly AI
Mouseflow

If you're evaluating heatmap tools as part of a broader platform switch, the Contentsquare alternatives guide breaks down how the leading options compare.

Who can benefit from using heatmaps?

Heatmapping software provides a wide range of valuable data. Here’s how different departments can use that data to their advantage.

UX designers

Heatmaps help UX designers understand how users interact with their site or app. They’re great for usability testing, showing if content encourages action, revealing behavior patterns, and confirming if CTAs are placed effectively.

Heatmaps also assist with A/B testing. If something doesn’t perform as expected, heatmaps can help explain why. Even when tests don’t go as planned, the right data turns failures into valuable learning opportunities.

Marketers

For digital marketers, the competition for attention is fierce and traffic acquisition costs (TAC) are rising, so maximizing the value of site traffic is essential.

Heatmaps help marketers see which parts of a page or ad grab attention and which are overlooked. With this insight, they can place key elements—like special offers or CTAs—where they’re most likely to be seen.

Digital analysts

For digital analysts, heatmaps bridge the gap between data and actionable insights. They combine qualitative and quantitative data, adding depth and context to your analysis.

While other tools might only allow you to count clicks on a button, heat mapping tools can help you understand what happened before, after, and in between those clicks—critical information for understanding your user’s behavior.

The visual nature of heatmaps also makes it easier to interpret complex insights by turning raw data into a clear story.

Product teams

Heatmaps give product teams valuable insights into how users interact with features, helping to identify what’s working and what isn’t. For example, they can reveal whether users are engaging with a new feature or struggling to find it, allowing teams to refine designs and priortitize updates.

By understanding user behavior through heatmaps, product teams can make data-driven decisions, build intuitive products, and delight customers.

Learn more: See how Fullstory helps product teams.

The pros and cons of heatmaps

Like any website analytics tool, heatmaps have both strengths and limitations. Here’s a quick overview:

Pros of heatmaps:

Quickly visualize large amounts of data in a digestible format. Identify and optimize areas where users aren’t engaging with your site as expected.
Gain a clearer understanding of user intent and behavior over time and identify useful patterns.
Use insights from current landing pages to create more effective ones in the future. Discover which areas of your site naturally attract attention and place important content there.

Cons of heatmaps:

Variations in device sizes and browsers can sometimes make heatmap data unreliable.
A heatmap tool that doesn't account for common dynamic pages or elements.
It can be difficult to convert heatmaps into actionable business intelligence without corresponding quantitative or qualitative data.
Certain types of heatmaps, like eye-trackers, can be expensive.
Some types of heatmaps, like AI-generated heatmaps, need large volumes of traffic to create accurate predictions.

Further reading: Learn more about the pros and cons of heatmaps.

Fullstory vs. Pendo: Digital adoption powered by behavioral data

Fullstory — Wed, 18 Mar 2026 12:00:00 GMT

Fullstory and Pendo both help teams understand users and improve the digital experience, but they approach this from different starting points.

Pendo's primary focus is on in-app guidance and adoption, while Fullstory’s core is built around all-in-one behavioral intelligence. Deciding which digital adoption solution is best for you is less about features and more about how quickly insight turns into action.

Here's a side-by-side comparison of Fullstory and Pendo across key categories.

Fullstory vs. Pendo at a glance

Category	Fullstory	Pendo
Overview	Fullstory is a behavioral data platform that uses automated capture and agentic AI to provide high-fidelity session replays, visual diagnostics, and in-app guides and surveys.	Pendo is a software experience management (SXM) platform that provides product analytics, in-app guidance, roadmapping, and user feedback collection.
Primary focus	Capture and analyze everything. Deep behavior analytics, proactive UX optimization, frictionless data collection, and resolving user friction in near real-time.	Product management, structured feature adoption, employee onboarding, and top-down user engagement.
Best for	Enterprise teams prioritizing qualitative UX research, bug resolution, workflow intelligence, and conversion rate optimization, especially those with minimal dedicated engineering resources.	Product-led SaaS companies that need a centralized platform for feature usage tracking, targeted user communication, and product validation.
Data collection	Fullcapture™: Automatic, tagless collection of all client-side interactions from the moment of script installation, no manual tagging required.	Event-based tagging: Requires users to manually tag pages and features within the UI or via CSS selectors to track specific interactions.
Speed to insight	Immediate. Data is instantly populated and fully retroactive. Users can query behaviors that occurred before a specific dashboard or funnel was created.	Dependent on setup. Requires establishing a taxonomy, tagging elements, and waiting for data to accumulate post-tagging.
Ease of implementation	Simple. Requires a single script installation; data is structured automatically without ongoing engineering maintenance. Alternatively, an easy-to-deploy browser extension is available for internal employees.	Mixed. Initial snippet setup is reportedly simple, but configuring dynamic filtering, maintaining feature tags, and mapping workflows often require ongoing effort.
Cross-team collaboration	Unifies Support, Engineering, UX, Product, and Operations teams around a shared source of truth via easily shareable session replays.	Aligns Product, Marketing, and Customer Success teams through shared roadmaps, feature adoption metrics, and centralized communication tools.
Data privacy & compliance	Strong. Private by Default, Automatic PII masking, SOC2 compliance, GDPR, CCPA, and self-serve Header Privacy Rules.	Strong. SOC2, GDPR, CCPA, and HIPAA compliant. Includes secure data encryption and role-based access controls.
Cost & scalability	Custom pricing (Free, Business, Advanced, and Enterprise tiers). Scales on session volume. Core features are largely unified across paid tiers.	Custom pricing (Free, Base, Core, and Ultimate tiers). Based on MAU and plan functionality. Advanced features are offered as paid add-ons.
Integrations	Jira, Slack, Salesforce, Databricks, Segment, Optimizely, Zendesk, Qualtrics, and more.	Salesforce, HubSpot, Jira, Figma, Microsoft Teams, Segment, Snowflake, TrustRadius, and more.
Strengths	High-fidelity session replay, automatic frustration signals, tagless retroactive data, AI querying (StoryAI), and behavioral-triggered guides.	Extensive multi-step in-app guides, integrated NPS/CSAT surveys, and comprehensive quantitative feature adoption tracking

Fullstory Workforce vs. Pendo for Employees

Category	Fullstory Workforce	Pendo for Employees
Primary focus	Capture, analyze, and diagnose the entire digital employee experience through deep behavioral and process insights.	Onboarding new hires, change management for software rollouts, and in-app training.
Data collection & installation	Browser Extension: Captures data across all web-based tools without needing individual snippets.	Tagging/SDK: Typically requires a snippet or browser extension (Pendo Launcher), with a focus on specific "tagged" features/pages.
Qualitative insights	Strong. Rich qualitative insights with session replays and heatmaps, allowing teams to visually see how employees are interacting with the app	Limited. Limited session replay (no mobile support). No automatic detection of frustration signals
Cross-app analysis	Provides both single and multi-app analysis to provide a holistic view as employees move between applications	Provides insights within the context of a single app or product (cross-app analysis often requires additional integrations)

Workforce gives us empowerment through transparency.
"It gives me the footprint of what applications are out there. How many are out there? Who’s using them? And the frequency in which they’re using them? Now, I have compelling, objective data to demonstrate the business case behind spend and roadmapping decisions.”
Justin Hauschildt - Director, Enterprise Applications & Delivery Enablement,

Features

Feature	Fullstory	Pendo
Session replay	Strong. High-fidelity playback with DOM mapping, dev-tools visibility, and automatic AI-generated session summaries.	Limited. Available as an add-on (Core plan+). Offers contextual insights, but may require technical dev-tool integration for deeper insights.
Heatmaps	Strong. Includes advanced click maps, scroll maps, and engagement tracking generated automatically across all pages.	Limited. Supported, but reliant on accurate manual page and feature tagging to function optimally.
Event & process tracking	Strong. Tracks all standard and custom events/processes without manual instrumentation, ensuring total data coverage.	Limited. Requires manual definition and tagging of specific elements. As with any tag-based system, changes in underlying code may necessitate tag adjustments
AI & automation	Strong. StoryAI: Natural language querying ("Ask StoryAI"), automatic session summaries, and AI-suggested elements for rapid analysis.	Strong. Agent Analytics: Tracks user interactions with AI bots. AI-assisted guide creation and NLP-based NPS theme summarization.
User journey mapping	Strong. "Journeys" feature dynamically maps the most common entry/exit paths and visualizes the exact, unstructured steps users organically take. Workflow intelligence tracks tasks and process steps across applications	Strong. "Paths" feature maps sequences of events leading up to or following a specific, predefined user action.
Dashboards	Strong. Customizable, no-code dashboards that integrate quantitative charts with qualitative session links. Highly rated for self-serve ease of use.	Strong. Extensive reporting dashboards tailored for product metrics (stickiness, retention, feature adoption).
Mobile app analytics	Strong. Strong native support for iOS, Android, and Flutter. Captures mobile-specific behaviors without sacrificing app performance.	Strong. Robust mobile SDKs. Recently added multiple-track events for mobile guides and image blocking for mobile replays.
Segmentation & cohort analysis	Strong. Deep behavioral segmentation. Can group users by granular actions, errors experienced, or device specifications effortlessly.	Strong. Granular audience segmentation based on metadata, role, or usage history to target communications effectively.
In-app guidance	Strong. Triggers precise, personalized guidance based on real-time behavioral friction and intent with Guides and Surveys.	Strong. Industry-leading tooltips, walkthroughs, and banners. Requires no coding, though multi-step guides can be complex for admins to maintain.
NPS surveys	Strong. Embedded, contextual surveys triggered by specific user behaviors or friction to maximize response rates without interrupting flow.	Strong. Comprehensive "Sentiment" module featuring NPS, CSAT, PMF, and the newly launched UX-Lite usability surveys.

Outcomes & use cases

Use Case	Fullstory Workforce	Pendo for Employees
Workflow intelligence	Strong. Provides a complete, cross-application view of how employees complete tasks. Automatically captures every step to pinpoint internal friction, streamline complex workflows, and diagnose process inefficiencies without manual tagging.	Limited. Focuses on tracking user paths within predefined, structured workflows, typically within a single application. Mapping cross-application journeys often requires additional integrations and manual setup.
Acting on behavioral insights	Strong. Trigger real-time, behavior-based in-app interventions directly from observed friction.	Strong. Deploy targeted in-app messages and product tours directly from analytics dashboards based on user segments.
Understand why users abandon a funnel	Strong. Combines quantitative drop-off analysis with session replay to reveal the exact experience causing abandonment.	Strong. Clearly identifies abandonment steps and enables follow-up through targeted polling and engagement.
Debug UI/UX issues	Strong. Integrates developer diagnostics, such as console logs and network requests, directly into session replay.	Limited. More focused on tracking standard UI interactions than surfacing underlying technical console errors or front-end bugs.
Analyze employee journeys to improve digital experience	Strong. Reveals organic, unstructured user paths and unexpected behaviors influencing successful processes.	Strong. Excels at analyzing structured journeys built around predefined workflows and product milestones.
Reproduce bugs or support issues	Strong. Eliminates manual reproduction by allowing teams to watch the exact user session tied to an issue.	Limited. Suitable for tracking macro usage trends and guiding users around issues, can assist with bug reproduction, though focus may differ from tools used for in-depth technical debugging.
Measure feature adoption over time	Strong. Automatically establishes behavioral baselines without requiring upfront tagging or configuration.	Strong. Purpose-built adoption analytics with deep retention, stickiness, and feature usage tracking.
Accelerate user onboarding	Strong. Identifies exactly where users get confused organically during onboarding to inform permanent, systemic product redesigns.	Strong. Facilitates interactive, multi-step walkthroughs to manually guide users through the initial onboarding process via overlays

What Fullstory and Pendo both do well

Fullstory and Pendo share a core mission: helping organizations reduce digital friction and improve adoption through in-application guidance and analytics.

Both platforms excel at:

Democratizing data: Both move beyond "vanity metrics" (like pageviews) and give non-technical teams the power to see how users actually behave.
Scalable security: Both maintain the highest standards of SOC2, GDPR, and CCPA compliance, ensuring that "seeing everything" never means "compromising privacy."
Contextual engagement: Both platforms deploy in-app guides and surveys to further close the loop between insights and action, delivering the right message to users.

But the divergence isn’t about capability coverage; it’s about architecture and philosophy.

Why teams choose Fullstory

When organizations evaluate digital adoption platforms, decisions tend to come down to operational realities and not features.

Teams consistently cite these reasons for choosing Fullstory as their go-to digital analytics platform (DAP):

1. Fullstory reduces engineering burden

Many SaaS teams find manual event tracking to be a resource-intensive task.

Fullstory enables product and UX teams to easily create and refine events themselves, dramatically reducing reliance on the backlog and accelerating experimentation. This changes who could participate in discovery.

2. Fullstory's session replay becomes a shared source of truth

Feedback from some users indicates that certain session replay solutions can be challenging to use. Session replay had an even greater impact than expected, but only when it was easy enough for everyone to use.

Teams chose Fullstory because replay became central to collaboration. Instead of debating dashboard interpretations, teams could instantly align on a shared user experience.

3. Fullstory provides diagnostics that shorten time-to-resolution

Teams often cite Fullstory’s ability to surface browser console errors directly alongside session replay timelines. So instead of attempting to reproduce customer-reported issues, support teams can immediately identify technical failures in context.

4. Fullstory's support and partnership during evaluation

Platform decisions are shaped by how vendors operate during an evaluation process.

Companies going through time-sensitive evaluations often say responsiveness becomes a deciding factor. Teams have described choosing Fullstory after experiencing quicker communication, collaborative working channels, and easier access to experts during the evaluation stage, which was a positive signal of what the long-term partnership would look like.

5. Fullstory Workforce + Anywhere

Beyond customer adoption, Fullstory supports employee-facing workflows and warehouse-native analytics, enabling behavioral insights to inform CRM, support, and operational systems, extending beyond traditional overlay-only DAP models.

Fullstory Workforce

Instead of just guiding employees through predefined workflows, Workforce gives you a complete, unvarnished view of how your teams actually use their suite of internal SaaS tools.

By automatically capturing every interaction across web-based applications, Fullstory helps you pinpoint and eliminate internal friction, streamline cross-functional workflows, and accelerate IT support. It moves beyond simple adoption metrics to reveal the why behind employee struggle, boosting productivity and satisfaction.

Fullstory Anywhere

Fullstory’s warehouse-native solution lets you connect Fullstory's rich behavioral data with your existing business intelligence tools (such as Databricks, BigQuery, or Snowflake).

This enables you to build a comprehensive view of the digital experience that incorporates behavioral signals into your broader CRM, support, and operational systems.

So, which engine drives your product?

As we mentioned at the start of this post, the decision isn’t about choosing analytics or adoption. It’s about choosing how quickly your team can move from insight to action.

Choose Fullstory for complete behavioral visibility without manual tagging and a collaborative platform that helps teams understand friction and resolve issues in real time. With Guides and Surveys built directly into Analytics, you'll be able to fix problems as they arise.

Choose Pendo if your priority is structured adoption programs, multi-channel feedback collection, or AI-assisted guide creation across predefined user segments.

Both platforms help you guide users, but one waits for you to define what matters while the other reveals it automatically. If you're still comparing options, these Amplitude alternatives break down how the leading platforms stack up across data capture, privacy, and team accessibility.

If you want to check out other options, our Contentsquare alternatives guide compares the leading digital experience platforms.

Let’s get in touch. Request a personalized demo today.

Fullstory vs. Contentsquare: From confusing clicks to revenue clarity

The Fullstory Team — Mon, 18 May 2026 15:00:00 GMT

At a glance, Fullstory and Contentsquare might look similar. Both promise to show you what users are doing on your site. But for a busy E-commerce Manager or Retail Merchandiser, the real question is: which platform helps me fix friction faster and connect behavioral data directly to revenue??

If you're tired of spending hours trying to connect clicks to conversions and struggling to prove the ROI of your work. This breakdown cuts straight to what actually matters for teams trying to connect behavior to business outcomes. We show you exactly how they differ in terms of ease of use, business impact, and long-term value.

At a glance: Quick comparison

Below is a head-to-head comparison of the most critical evaluation criteria: Need more detail? We break it down below.

Category	Fullstory	Contentsquare
Data capture & depth	DOM-level capture works out of the box. No tagging required—even for SPAs. Full drill-down into every interaction.	Manual setup is allegedly required for SPAs, modals, and dynamic elements. Limited no-code analysis. Drill-down capabilities can be shallow.
Setup & maintenance	One snippet for full functionality. Updates automatically with site changes. Minimal engineering support needed.	Built from multiple tools. Site changes might break tracking and require manual fixes. Ongoing engineering effort.
Platform integrity	Unified platform with native features built on a single data model.	Created through acquisitions (Heap, Hotjar, Clicktale). Tools and data models can feel disconnected.
Privacy & security	Best-in-class masking across devices. Real-time PII detection with Detections. Privacy-first approach.	Limited masking on mobile. Masking rules can rely on fragile selectors. Lacks real-time PII detection.
Connecting behavior to business impact	Auto-zone configuration generates heatmap zones automatically. Enhanced Heatmaps overlay revenue and conversion data directly on the page, showing which elements drive sales and which don't.	Revenue and conversion data are tied to manually-defined 'zones.' Requires significant setup and can be difficult to interpret.
AI & future-proofing	AI works retroactively across all behavioral data.	AI is tied to predefined metrics. May not apply to retroactive data.
Support & governance	Streamlined onboarding with step-by-step SDK documentation. Support built for speed and clarity	Often relies heavily on services. Global presence, but onboarding can be fragmented.

One platform vs. many parts

Some platforms were built to work together. Others were built by acquisition and never fully brought into sync. That difference shapes every part of the user experience.

Fullstory was built as a single platform. Every feature shares one data model, one script, and one interface, so teams can move quickly and stay aligned.

Contentsquare was put together from acquired tools like Clicktale, Hotjar, and Heap. The result is a patchwork of modules with different data models, inconsistent UX, and workflows that often require help from experts to navigate. If you're weighing other options too, our Amplitude alternatives guide breaks down how the leading platforms stack up.

Having used Contentsquare and coming here and really missing it on the first couple days, I think Fullstory is a far more powerful tool. [Y]ou guys are easily my favorite vendor I’ve ever worked with and the support you guys have provided has been amazing. - Digital Analyst, Fortune 500 Restaurant Chain

Here's what that means in practice:

Setup takes minutes with Fullstory. You deploy one script and start capturing retroactive, DOM-level data immediately.

Setup with Contentsquare can take weeks or more. You might need custom tagging, separate scripts for different tools, and support to configure core features.

Fullstory adapts automatically. When your site changes, your data stays intact—there is no need to remap zones or re-tag elements.

Contentsquare can require ongoing maintenance. Structural changes to your site risk breaking data collection or invalidating reports.

Fullstory works out of the box, and Contentsquare can come with baggage.

What both platforms handle well

Both platforms help teams understand what users are doing—and where they’re running into trouble. If your goal is to improve digital experiences, both platforms support key use cases:

Session replay

Each tool captures 100% of user sessions across web and mobile. You can watch real journeys, skip to key moments, and share recordings with teammates.

Journey mapping

Want to understand drop-offs or weird user paths? Both platforms visualize customer journeys and let you filter them by behavior, page type, or cohort.

Fullstory shows real DOM interactions. Contentsquare uses sunburst visuals with advanced journey layers, but limited analysis

Conversion & funnel reporting

Whether you care about cart drop-offs, lead form abandonment, or onboarding flow issues, both tools help you build and measure funnels across key touchpoints.

Fullstory’s funnels tie directly to replays and DOM events. Contentsquare supports top-down analysis with side-by-side journeys.

Error monitoring

Both include JavaScript error tracking, API failure detection, crash insights, and the ability to measure how errors affect business outcomes. Integrations with tools like Jira and Slack make it easy to act fast.

Speed analysis

Both platforms include real-user monitoring (RUM) to pinpoint slow-loading pages and understand how load times affect user behavior.

Contentsquare includes synthetic monitoring as an add-on; Fullstory focuses on in-session impact.

Why Fullstory heatmaps beat Contentsquare’s

Both platforms offer heatmaps, but their philosophies are fundamentally different. Contentsquare's "Zone-Based Heatmaps" are powerful but require a slow, manual setup process and ongoing manual configuration post-launch; for example, any changes to a web page require a manual update to the Heatmap zone configuration to ensure accurate data.

Fullstory is built for speed and business clarity. The newly enhanced Heatmaps are automatically configured into a logical zone hierarchy and provide a complete view of user behavior from day one, powered by autocapture. And with new Conversion and Revenue maps, you can instantly overlay revenue and business KPIs on any page, giving non-technical teams the power to see what's driving the bottom line without needing a data scientist.

Questions to ask your team

Your decision isn’t just about features. It's about how the platform fits your team, your tech stack, and your long-term goals.

Will it work with my site structure?

Modern websites rely on SPAs, dynamic content, and custom components. Fullstory captures it all automatically. Contentsquare often needs manual tagging or help from consultants.

How quickly can I get value?

Fullstory delivers insights as soon as it's installed. With Contentsquare, you may spend weeks just getting the basics configured.

What happens when my site changes?

Fullstory keeps your data intact, no matter how your structure evolves. Contentsquare can break and require retagging or reconfiguration.

Is my user data protected?

Fullstory detects and masks sensitive information in real time. Contentsquare masking depends on brittle selectors and lacks real-time detection.

Will this scale across teams?

Fullstory is intuitive enough for product, engineering, and marketing to use without friction. Contentsquare often relies on specialists or external support.

What kind of support will I need?

Fullstory provides responsive support and clear docs. Contentsquare may require ongoing paid services to get full value.

Can I trust the data?

Fullstory captures everything retroactively—no sampling, no blind spots. Contentsquare relies on tagging and sampling, which can leave gaps in understanding.

How future-ready is this?

Fullstory's AI works across all behavioral data, retroactively. Contentsquare’s AI is limited to predefined metrics and use cases.

The verdict

Fullstory is built for teams that value speed, clarity, and autonomy. From setup to insight, it delivers value quickly—without the extra work, manual tagging, or reliance on outside help.

Contentsquare may be a fit for teams with deep budgets, complex service contracts, and a preference for visually heavy dashboards. But if you want a platform that just works—for every team, on every site change, without delay—Fullstory is the better choice.

If you're weighing a broader set of options, the Contentsquare alternatives guide covers how the leading digital experience platforms compare.

Ready to see what a unified behavioral data platform can do for you? Explore Fullstory’s expanded portfolio—including StoryAI, Workforce, and Anywhere—or book a demo.

Fullstory vs. Amplitude

Fullstory — Wed, 28 Jan 2026 12:00:00 GMT

Most teams don’t struggle because they lack data. They struggle because they can’t always trust what they’re seeing or move fast enough when something breaks. When conversion dips, tickets spike, or a feature underperforms, getting from “something’s off” to “here’s what actually happened” often takes longer than it should.

Amplitude is built to measure what teams plan to track. Fullstory is built to show what actually happened.

That difference matters.

Fullstory captures real, exhaustive user behavior by default, so teams can investigate problems retroactively, see friction in context, and act while the moment still matters. Instead of waiting for new instrumentation or hoping the right events exist, teams already have the answers in front of them.

Fullstory vs. Amplitude at a glance

Most analytics platforms tell you what you planned to measure. Fullstory shows you what actually happened.

That difference shapes everything below.

What teams need	Fullstory	Amplitude
Data capture & depth	Fullcapture automatically captures interactions, so teams can analyze retroactively, see frustration signals, and understand context.	Primarily event-driven. Optimally performs with a stable tracking plan. Autocapture, session replay, and heatmaps are also available.
Analysis	Complete: Deep product analytics. Replay, heatmaps, Rage Clicks, frustration signals, and error detection are built in.	Complete: Charts, funnels, and cohorts are powerful, with qualitative context. Replay and heatmaps are available.
Speed to insight	Often within minutes. Retroactive queries and AI summaries surface answers quickly.	Achieving rapid insight may involve careful consideration of new questions, instrumentation, and data-collection processes.
Cost & scalability	Session-based plans with unlimited seats. Scales across teams and use cases.	MTU and event pricing can scale and be optimized to align with analytical needs.
AI assistance	StoryAI surfaces funnel drop-offs, session summaries, and hidden opportunities proactively. With Ask StoryAI, teams can use a conversational interface to ask immediate, data-backed questions about user behavior.	AI Agents monitor metrics and recommend next steps, like experiments or guides. Strong for growth loops, experimentation, and ongoing optimization.
Privacy & security	Private-by-default capture, auto-masking, and robust compliance (SOC 2, ISO 27001, ISO 42001, HIPAA, PCI).	Enterprise-ready, with data privacy and governance effectively supported through implementation and tagging practices.
Setup & maintenance	One snippet. Data flows immediately. Designed for minimal ongoing engineering; teams can layer in custom events as needed.	Typically involves an upfront tagging strategy and configuration, complemented by event management to optimize data accuracy.
Integrations	Connects behavioral data to warehouses (Snowflake, BigQuery, Redshift, Databricks), support tools, bug trackers, and personalization platforms	Broad martech ecosystem. Native Activation module syncs cohorts to ad and messaging platforms for campaigns.

Feature comparison

Amplitude is an event-first analytics platform. It works well when teams know exactly what they want to track and can maintain that structure over time. It’s like shining a flashlight in the dark–great but inherently limited.

Fullstory is built for reality. It’s like turning the lights on in a dark room–it captures everything.

It captures real user behavior by default, so teams can ask new questions later, investigate unexpected problems, and understand friction in context, without rebuilding their tracking every time something changes.

More comprehensive data sets enable teams to find the needle in the haystack, often leading to decreased user friction, shorter time to resolution, improved funnel performance, and more.

Why teams choose Fullstory

Teams don’t switch for more charts. They switch because something feels broken and they need answers fast.

With Fullstory, teams stop dealing with:

Re-instrumentation cycles just to investigate new questions
Event mismatches that require hours of debugging
Insight bottlenecks that only analysts can unblock
Guessing what broke and hoping the right data exists

And start operating with:

Answers in minutes
Full visibility into real user behavior
Fewer stitched-together tools
Confidence that the data is already there when something goes wrong

Support, product, and marketing teams can work from the same source of truth, see friction in context, and move from investigation to action without waiting for new tags or new data to catch up.

Data capture: Fullcapture vs. manual tagging

Event-first tools assume teams can predict what they’ll need later. Most teams can’t.

With Fullstory, every interaction is already captured. Teams can investigate issues retroactively, explore unexpected patterns, and see friction in context without waiting for new tags.

With Amplitude, new insight usually requires new instrumentation and time to accumulate data. Teams wait for answers instead of acting on them.

Learn more about Fullcapture ➜

AI capabilities

Fullstory: StoryAI

StoryAI solves the critical problem of information overload by acting as a personal analyst for every user. Instead of watching long session replays or manually analyzing data across dashboards, it instantly synthesizes complex behavioral data into clear insights paired with recommended actions. Teams can pinpoint critical user moments, understand why they’re happening, and get guidance on what to do next, turning insight into confident, in-product decisions in seconds, not hours.

Amplitude: AI Agents

Amplitude AI monitors predefined metrics and recommends optimizations. It’s effective for known growth loops, but limited when something unexpected happens.

In-app guidance

A guide is only as good as the insight that triggers it.

Fullstory’s Guides and Surveys are built into Analytics, allowing teams to act on insights within a single, unified platform. The critical difference is that every survey response is automatically linked to the user’s complete, high-fidelity session replay. This eliminates the gap between what users say and what they actually did, providing the full story behind every piece of feedback.

Amplitude offers an integrated solution for in-app guides and surveys, allowing teams to target users based on behavioral cohorts defined in their analytics. However, it lacks the seamless, built-in connection to session replay. Understanding the "why" behind a user's feedback still requires a separate, disconnected step: finding and watching a session replay to manually piece together the user's experience.

Learn more about Fullstory Guides and Surveys ➜

Enterprise readiness

Fullstory captures data privately by default. Masking and compliance are built into the platform.

Amplitude meets enterprise standards, but privacy and data accuracy depend more heavily on how carefully teams govern and maintain tagging over time.

The verdict

When Fullstory becomes your system of record, teams stop operating in the dark.

Instead of debating what happened or rebuilding instrumentation for every new question, they have a complete view of real user behavior ready the moment something feels off.

Amplitude measures what teams planned to track. Fullstory shows what actually happened.

That difference is what turns reactive teams into confident ones.

Request a personalized demo ➜

If you're weighing a broader set of options, the Contentsquare alternatives guide covers how the leading digital experience platforms compare.

2025 Mobile app trends: The state of mobile experiences

The Fullstory Team — Mon, 15 Sep 2025 04:00:00 GMT

The data is in, and it tells a complicated story. Data from over 4 billion digital sessions across all types of industries shows that mobile session duration has skyrocketed by over 300%. People are spending more time in mobile apps than ever before, which sounds like a clear win.

But the more time they spend, the more frustration they seem to find. Metrics like error-driven exits and bounce rates are also climbing.

To put it simply, more engagement uncovers more problems.

Success in 2025 means looking past surface-level stats to understand the why behind user behavior. It’s time to explore the real-world challenges your users face, from spotty network connections to the quirks of modern development frameworks.

Let's look at the trends that are defining the mobile experience in 2025.

1. Users are spending more time in-app than ever before

Apps have become the primary way users interact with businesses, whether they’re browsing, buying, or booking. Session duration has jumped an astounding 332% in the last year, and 64% of consumers now say they prefer a brand’s mobile app over its website.

But increased engagement isn’t necessarily something to celebrate. Experts caution that longer sessions may not always indicate success, they may signal users struggling to complete tasks under less-than-ideal conditions.

2. Error rates are rising as users take apps "into the wild"

Mobile apps need to be able to handle less-than-ideal conditions because their users don’t always operate in perfect conditions. People launch apps while juggling tasks, dealing with unstable networks, or using a wide range of devices with different performance capabilities. These everyday scenarios often introduce issues that never surfaced in QA or internal testing environments.

The data reflects this. From 2024 to 2025, error-related session exits jumped by 254%. To catch and resolve these edge cases, teams need high-fidelity visibility into how their app performs in the real world.

3. Bounce rates are climbing as user patience hits an all-time low

Mobile users are showing less tolerance than ever for friction; bounce rates are up 54% year-over-year. If they encounter a confusing flow, sluggish screen, or tap something that doesn’t respond, they’re gone.

Polished, responsive digital products have set a new baseline, and anything less feels broken by comparison. That makes the first experience a make-or-break one. The moment something doesn’t work, users are gone, and the next alternative is already in their pocket.

4. Using AI to create personalized experiences

In 2025, the apps that succeed aren’t just fast or bug-free; they feel personal. They engage an audience, boost conversion rates, and increase retention.

True mobile personalization goes beyond basic user preferences or A/B testing. It’s about using machine learning to understand each user’s behavior in real time and adapt their experience accordingly. Whether that means suggesting content, optimizing a checkout flow, or rearranging an onboarding journey on the fly, AI helps apps feel like they’re tailored to the individual.

Although delivering highly personalized experiences sounds like a tedious process, tools like Fullstory’s StoryAI are making it increasingly accessible. StoryAI turns massive volumes of behavioral data into precise insights that help teams understand not just what users are doing, but why. It highlights patterns of friction, pinpoints moments of drop-off, and surfaces high-impact opportunities to tailor the user experience automatically—so you spend less time digging and more time making impactful moves.

But insights alone don’t drive results. That’s where Fullstory Anywhere: Activation comes in. As users interact with your app, Activation streams live signals into your existing tools from CX platforms like Intercom or Salesforce to personalization engines and marketing automation. That means your team (and your tech) can respond in real time to what each user is experiencing.

Marketing analytics tools that cannot explain why users leave after a campaign click are not giving marketers the full picture.

5. Integrated mobile tech is the future

Mobile isn’t just a channelm it’s the primary experience platform. As such, it’s important to make sure your tech ecosystem is optimized for mobile data exchange.

Fullstory is built to integrate with your stack. It supports the most widely used iOS, Android, and cross-platform frameworks, giving teams a unified view of user interactions across desktop and mobile. With behavioral data from every touchpoint in one place, teams can move faster, reduce context-switching, and inform decisions across product, CX, and marketing.

Learn how Fullstory’s tech stack integrations can help you make the most of your data.

6. Designing mobile applications for privacy and trust

Privacy isn’t just a legal requirement; it’s directly tied to whether users feel safe using your product. With people spending more and more time in apps, the responsibility to protect their personal data is only increasing.

But many traditional mobile analytics tools for capturing user behavior introduce unnecessary risk. Screen recordings or raw screenshots often collect more than intended, including passwords, credit card information, or other personal data. Fixing it later with redaction is unreliable. Once sensitive data has been captured, control is already lost.

The better approach is to design for privacy from the start.

Fullstory, for example, avoids capturing raw screen data altogether, focusing instead on rendering and behavioral signals that offer insight without compromising privacy. With this approach, you can capture what matters, skip what doesn’t, and give your users the confidence that their data is handled with care.

7. Embracing the mobile commerce revolution

Mobile commerce has become deeply embedded in daily life. According to Shopify, mobile commerce volume is expected to hit $710.4 billion this year. People scroll storefronts while commuting, restock essentials between meetings, and complete entire purchases from the couch. It’s fast, it’s convenient, and for many users, it’s the preferred way to shop.

But that flexibility introduces new variables, like inconsistent network conditions, device limitations, and distracted browsing, that can complicate the checkout journey. These real-world contexts often expose friction that’s tough to catch in a controlled QA environment. The challenge for brands isn’t just building a beautiful checkout flow, but ensuring that experience holds up in the wild.

The observer effect: How you see the problem changes the experience

So how do you diagnose these issues? Many teams default to traditional screen recording tools for session replay, but they often do more harm than good.

The old way is a performance killer

Constantly taking screenshots or recording the screen is a massive drain on your user's device. It kills their battery and eats up their data plan, creating the very performance lags and frustration you’re trying to fix. It’s like trying to diagnose a patient by making them run a marathon.

It’s also a privacy nightmare. When you capture the entire screen, you capture everything, including sensitive personal information. Trying to redact that data after capturing it is a fundamentally flawed approach. Once private data leaves the device, the damage is already done.

There’s a better way to see what’s happening

Instead of capturing pixels, modern solutions capture the underlying rendering instructions of an app.

Think of it like this: a traditional screen recorder is a photographer taking a huge, high-resolution photo of a scene. The modern approach is more like a courtroom sketch artist, capturing the essential details with a few precise, lightweight strokes. The result is a high-fidelity recreation of the user experience that is performant and lightweight.

This method also allows you to be private by default. By capturing only what’s needed to reconstruct the session, you can avoid collecting sensitive data from the very beginning.

Case study: CINC reduced churn in mobile app experiences with Fullstory

CINC, a platform that helps real estate agents generate, nurture, and close leads, turned to Fullstory to enhance the performance of its mobile experience and streamline customer support. By tapping into behavioral insights and custom iOS metrics, CINC quickly spotted friction, improved new feature performance, and accelerated support workflows.

The results?

25% faster resolution time for mobile-related issues
20% increase in customer satisfaction

→ Read the full story here.

See the full story across mobile and web

Great digital experiences don’t happen in silos. Users move between mobile and web without thinking twice, and your team needs clarity across every touchpoint.

With Fullstory, you get a complete view of the user journey, no matter where it happens. From friction in a mobile form to a stalled checkout on desktop, you’ll understand what’s really going on, why it’s happening, and what to do next.

→ Curious how the experiences you create for your users compare to others in your industry? Our 2025 Digital Benchmark Report breaks down trends from billions of sessions across Ecommerce, Financial Services, Travel & Hospitality, Food & Beverage, Gaming, and more. Access the Benchmark Report

8 website heatmap examples to better your bottom line

The Fullstory Team — Mon, 18 Oct 2021 04:00:00 GMT

Heatmaps are some of the most important data marketers can analyze to make their websites more effective.

And knowing the kinds of heatmaps at your disposal can help you understand where user behavior patterns are emerging and how you can make changes to meet their needs.

In this article, we cover all things heatmaps—what they are, how they work, and the different types that will benefit you most.

What is a heatmap?

Heatmaps are graphical representations of numerical data. Think of a heatmap as a way of understanding how popular certain elements of your website or mobile app are. It shows where people are scrolling, clicking and where they stick around a warm-to-cold color scheme.

In general, heatmaps allow you to:

Track user behavior
Evaluate user interests on your website
Understand what’s ignored on your website
Test call-to-actions
Find out what causes friction
Catch page abandonment in action
Conversion rate optimization and design

The key benefit of heatmaps is that they simplify complex numerical data sets into easily understood visuals.

Additionally, heatmaps are powerful tools with many use cases, and applications across many industries.

A few heatmap examples:

Streaming services use heatmaps to optimize their viewing experience by identifying target audiences’ interests, understanding which recommendations users look at and which they ignore.
The stock market uses heatmaps to identify trends in the market at a glance. It uses a cold to hot color scheme to analyze stock options.
Sports teams use heatmaps to plot players’ performance, identify patterns for improvement, and study rival strategies.

Website heatmaps

Website heatmaps provide insights into user behavior, making them a valuable tool for businesses. Specifically, a heatmap uses color-coding to visually represent how users browse your website.

A website heatmap is a true representation of all visitor actions—whether that be engagement, frustration, or friction—heatmaps showcase it all

Types of website heatmaps

1. Clickmap

A clickmap is a map of how people use a website or a specific landing page. Clickmaps show you an overall aggregate of where visitors click their mouse and can be referred to as “touch maps”.

Clickmaps can help identify navigational gaps on a webpage or mobile device. Are your users completely missing where to go next? Find out with a clickmap.

Clickmaps also show which parts of a webpage are clicked on the most (and the least) and help identify navigational and conversion problems.

One use case may be gathering data on CTAs. Or if users are clicking on areas that are not clickable—like an image or text that people expect to be linked. That gives you insight into what to change on that particular page.

Pros of clickmaps:

Improve ROI by placing effective CTA buttons and monitoring them with clickmaps.
Clickmaps allow you to identify and remove areas that cause friction and distract users.
Clickmaps visually represent which categories and areas are most popular and where the gaps are.
When running a clickmap on your website, it allows you to monitor ecommerce conversion rate optimization for new and returning visitors.

Cons of clickmaps:

Improper readings may occur from accidental (multiple) login use, where a user repeatedly clicks on the same element.
Frustrated clicks like rage clicks can skew data analysis.
There may be other issues caused by interactive elements, responsive design rules, browser/device incompatibilities, or differences in page content.

Pro tip: Track at least 2,000 page views per screen for accurate clickmap readings.

2. Scrollmap

A scrollmap is a type of heatmap that’s a visual representation of visitors' scrolling behavior on your website.

On a scrollmap, the top of the page is red, meaning that it’s the most viewed area on the page, and the color turns yellow to green to blue towards the bottom of the page, showing fewer and fewer visitors scrolling.

Specifically, scrollmaps tell you:

the number of visitors who scrolled through a page to the bottom.
the number of visitors who scrolled through a page but did not scroll to the bottom.
the number of visitors who abandoned a page.

Use scrollmaps to help calculate the ideal length of a webpage beyond which visitors don't scroll down, identify whether or not visitors reach the content below the fold, and spot false floor or false bottom.

Additionally, scrollmaps help identify important content being ignored—like the most actionable items you can use to be successful.

Pros of scrollmaps:

A scrollmap is beneficial when designing longer web pages by helping you understand where to prioritize important elements.
Use scrollmaps to develop strategies to entice users to scroll further down to the page.
Scrollmaps are an excellent tool for optimizing “above the fold”. Above the fold is the part of your website that visitors can see without needing to scroll down and is typically the most visited part of the page.

Cons of scrollmaps:

Scrollmaps don’t always provide accurate data visualization. Scrolling activity is not necessarily an indication of how they are engaging with the content.
Scrollmaps don’t measure clicks, which doesn’t allow you to measure the effectiveness of an element.
A scrollmap may not be beneficial for websites with dynamic elements. With web design becoming more dynamic, it can complicate scrollmap data.

Further viewing: Learn more about how Fullstory's utilizes scrollmaps in our platform

3. Error click heatmap

Error clicks are when a user click triggers a client-side JavaScript error or website error occurs. Error clicks may or may not result in a user noticing something is broken.

With a behavioral data platform like Fullstory, you can create an Error Click heatmap to pick up any unforeseen errors on your website.

You can use this heatmap to specifically investigate console errors or uncaught exceptions.

If you find something that seems wrong, you can run another search scoped to the specific click event that triggered the error and view all user sessions with Error Clicks around that event.

Pros of error clicked heatmaps:

With error clicks, you can reproduce bugs on the fly to proactively fix them before a user engages.
Error click heatmaps can drastically improve the online experience on your site or app.
Error clicks can be helpful in surfacing a functional sample of data sessions to research.

Cons of error clicked heatmaps:

Error clicks can be the result of many different factors (web design, hosting issues, etc) and it may take time to find the source.
Error clicks don’t provide the context for the error.
Error clicks are likely to be noticed by users, creating frustrating online experiences.

4. Rage click heatmap

Rage clicks occur when users rapid-fire click (or tap) on your site or app in frustration. Whether the button or area doesn’t function or is producing an error, the rage clicking showcases friction with the product.

A rage click heatmap will show you all the areas that a user has frustratedly clicked errors, with signature heat indexes. It's not easy to predict what might cause a user’s rage, which is why searching your user sessions in a best-in-class DXI solution for Rage Click events is so helpful—you'll discover unexpected opportunities.

Pros of rage click heatmaps:

Rage click heatmaps can help you reproduce unexpected bugs and errors to proactively fix them.
Rage click heatmaps can correct any confusion on CTAs for better conversions
Rage clicks are a strong signal and therefore lead to actionable insights
Overall, these heatmaps can increase your ROI due to clearing up product friction across the board.

Cons of rage click heatmaps:

Rage click heatmaps may (at first) surface false positives. For instance, users may be paging through a multi-page app and click repeatedly on the same element.
There are all kinds of web elements that can induce rage clicks, from graphics to text, so pairing them down can be tricky.

5. Dead click heatmap

Dead clicks are when users click an element on your website or app but the click has no effect. Dead click heatmaps will find any non-functioning element with heat signatures and data intelligence.

Pros of dead click heatmaps:

Understand behavioral trends with dead click heatmaps over time and create new opportunities.
Stop non-functioning CTAs before they hit mass-market product updates.

Cons of dead click heatmaps:

Dead clicks’ behavioral signals can be nuanced and subtle, which makes them hard to find.
There is a chance for false positives with dead click heatmaps—based on the different website functions and user behavior.

6. Mouse tracking heatmap

Mouse tracking heatmaps help identify areas on which visitors are spending the most (and the least) time on your website. It has many names: mouse movement heat map, move map, attention heatmaps, or hover maps.

Mouse heatmaps visually represent user mouse movement data using thermal imaging – it records where visitors are navigating, clicking, scrolling, and stopping on your website.

Research shows a correlation between where users are looking and where their mouse cursor is, so these can be helpful.

Pros of mouse tracking heatmaps:

Mouse heatmaps allow you to identify hover patterns that can help discover areas of high visitor frustration and friction.
Mouse tracking also helps to optimize complex web pages made up of content, images, and dynamic elements in any matrix.
Mouse tracking has the ability to estimate the relevance of search results by the volume of clicks.

Cons of mouse tracking heatmaps:

Users tend to be “over-clickers” and click on literally everything, which can impact mouse-tracking data.
With mouse heatmaps, you have to rely on the visitors’ own objectives, not a controlled testing environment.
Additionally, you can’t ask your users direct questions about their experiences with mouse tracking—so you ultimately don’t know what their intentions are.

7. Eye-tracking heatmap

Eye-tracking heatmaps provide information about where visitors are looking on a given page.

They can also be used to track the visitor's gaze pattern to place all-important content and images in the most attention-grabbing sections of a page.

Eye-tracking heatmaps can be used to ascertain how engaging certain images on a website are.

Pros of eye-tracking heatmaps:

Eye-tracking heatmaps provide incredibly accurate data. It will show you exactly what users are looking at on your web page.
These types of heatmaps go hand in hand with mouse tracking maps, which can validate user data results.
Eye-tracking is based on natural human reactions, so you can see user flow easily.

Cons of eye-tracking heatmaps:

Eye-tracking data results are typically based on a small sampling of unique visitors.
Eye-tracking heatmap tools are costly and that cost can increase the more users you’re trying to track.
Some advanced users know they are being tracked and have the ability to cover up their cameras.

8. AI-generated heatmaps

Artificially intelligent (AI) generated attention heatmaps are visual representations of user attention data created by a software algorithm. Heatmaps generated by AI show which areas of the website users are likely to view the most (and the least).

Typically, AI heatmaps focus on imitating the first 3-5 seconds of user attention on a website—in order to predict future behavior.

Pros of AI-generated heatmaps:

AI-generated heatmaps can predict where people will look when engaged with a website—which can be beneficial for future improvements.
Since eye-tracking heatmaps are costly, an AI-based alternative is less expensive.
AI-generated heatmaps have up to 95% accuracy.
These heatmaps can validate your concepts prelaunch and also analyze your potential client’s websites to gather valuable insights.

Cons of AI-generated heatmaps:

AI-generated heatmaps don’t work well with low-volume websites, as there are fewer actions to predict.
These heatmaps often come with restrictions on actionable usage and are often pay-to-play.

Heatmaps and DXI

Heatmaps are incredibly resourceful for any website looking for insights on users.

With a DXI solution like Fullstory, you can access your colorful new heatmaps—or Click Maps—with Page Insights from within any session replay. Get a high-level view of how your user is engaging with just about anything.

Use Fullstory’s heatmaps on your homepage, your products page, your purchase forms, or any new features.

Learn more about heatmaps in Fullstory — and how they can help you craft a more perfect digital experience.

A step-by-step guide to conversion funnel optimization

The Fullstory Team — Thu, 23 Feb 2023 05:00:00 GMT

Whether you’re selling a product or service, having an effective conversion funnel is essential for success. With the right strategy, you can convert more leads into customers and increase your profits.

The key to optimizing your conversion funnel is finding the best content to engage with potential users/customers and convert them into paying users/customers.

Key takeaways:

Visualizing the marketing funnel is an important tool when trying to increase ecommerce conversion rate optimization
Most conversion funnels follow the AIDA model
Understanding your audience and tailoring experiences to different segments is the most effective way to optimize your funnel
Monitor your marketing or sales funnel using digital experience analytics to track progress and make improvements when necessary

What is a marketing funnel?

A marketing funnel is a visualization of the path someone follows from initial contact with your product or service to final conversion. It’s the customer journey, and it helps you optimize each step along the way. Tools like customer journey maps can help you visualize and analyze how users engage with each stage of your funnel, highlighting pain points and opportunities for optimization.

From increasing website traffic to improved lead generation, understanding how to maximize every stage of a marketing funnel can help you generate more conversions and increase your ROI.

Marketing funnels consist of stages: such as Awareness, Consideration, and Conversion. Understanding the different stages of your funnel is key to successful optimization.

The 3 basic phases of a marketing funnel

Awareness

This is where prospects are exposed to your products or services for the first time. It’s important to capture their attention by creating engaging content that resonates with them. Content should be informative, relevant, and consistent.

Consideration

During this stage, prospects assess your products or services to determine if they’re a good fit for their needs. You should use content that helps them make an informed decision by answering their questions and addressing any concerns they may have.

Conversion

This is the point of sale where prospects become customers. Content should focus on providing value and encouraging prospects to take action.

Below, you will learn about various funnel optimization strategies to use during each stage. Implementing these strategies can help you optimize your funnel and maximize conversions.

What is the AIDA model?

In addition to the basic three-step form above, the AIDA model is an influential marketing concept with four specific stages that every customer, user, or buyer goes through. It stands for Attention, Interest, Desire, and Action. It’s used to understand how customers move through the various stages of the conversion funnel.

Awareness

In this stage of the funnel, you need to grab your visitors’ attention with an eye-catching headline or image. This could be anything from a catchy slogan to an engaging video.

The awareness stage is considered the top of the funnel.

Interest

Once you have someone’s attention, you need to create interest. This means providing valuable content that is relevant to the visitor and their needs. You can do this by answering questions or offering helpful advice in blog posts, videos, or infographics.

Desire

After building interest, the next step is to create desire. This is where you showcase the benefits of your product or service. You can do this by highlighting customer reviews, featuring deals and promotions, or providing demonstration videos.

Action

Finally, you need to encourage visitors to take action and buy your product or service. This could be anything from a compelling call to action or an easy-to-use checkout process.

The action stage is considered the bottom of the funnel.

By understanding the AIDA model and optimizing your content for each stage of the conversion funnel, you can increase your website’s sales and conversions. So don’t forget to spend some time looking at how you can maximize each step.

How to implement funnel optimization for CRO

Now that you’ve learned about the basics of conversion funnel optimization, it’s time to implement these strategies. Here are some tips and tricks you can use for conversion rate optimization, making your funnels more effective:

Create brand value

Your content should be focused on providing value to your readers and creating a sense of trust in your brand. Focus on creating content that is informative, educational, and provides clear solutions to customer problems.

When you successfully create brand loyalty among your viewers, you can greatly improve your chance of increasing conversions.

Know your audience

Understand what motivates your target users and how they interact with your product or service. Analyze user feedback, surveys, customer support notes, and other qualitative data to get a better idea of who you’re targeting.

This is one of the most important steps in optimizing your funnel, as it will help you better understand user intent and tailor content, visuals, and CTA placement to get the most out of each step.

Start with a wide audience

When creating a funnel, start with a wide audience and then segment it down as you go. Doing so will enable you to attract more leads initially and qualify them later in the process.

Another way of narrowing your target audience is using segmentation and targeting so that each audience receives relevant ads, content, or promotions based on their interests and needs.

Learn more: User segmentation using Fullstory

Create personas

Buyer personas are representations of your ideal customers and can help you fine-tune content, messaging, and other elements to increase conversion. Develop at least two buyer personas based on the data you collect from your customer research and further refine them as you go.

Once you know who your buyers are and what motivates them, it’s time to look for potential gaps in your funnel. Analyze each step of the conversion process and identify areas where you can improve the user experience or remove any confusion.

Personas are critical for any industry, from SaaS to ecommerce.

Choose the right medium

Choose the right medium to deliver your message according to the stage and type of funnel. Consider using visuals, videos, or audio to convey information quickly and clearly.

When designing content, keep in mind that less is more. Make sure to provide the right amount of information without overwhelming users with too much detail. Your goal should be to reduce the number of steps in the funnel and make it as easy as possible for the user or customer to complete the journey.

Analyze your landing pages

Your landing pages are the first step in your funnel, so it’s important to make sure they’re optimized. Analyze your visitor flow and look for any areas that may need improvement.

Consider adding new CTA buttons, breaking up content into smaller chunks, and improving visual elements like fonts and colors to draw more attention.

Headlines and copy

The headlines and copy on your pages should be tailored to fit the message you’re trying to convey. Make sure the language is simple, direct, and engaging. Try to avoid being overly promotional or generic—use clear and concise phrases that draw attention and inspire action.

Remember, your goal should be to capture the attention of your visitors and make them want to take action.

Color

Make sure the visuals on your pages, such as images and videos, are relevant to the message you’re trying to convey. Colors should be used strategically to draw attention to CTA buttons and other important elements.

Additionally, consider using custom illustrations or GIFs to make your page stand out from the crowd.

CTAs

Make sure your CTA buttons are visible and stand out from the rest of the page. Use action-oriented words to encourage visitors to take action. Furthermore, consider adding exit popups or scroll boxes with a special offer for those who don’t convert on their first visit.

You want to make sure that your visitors have enough information and incentives to take action and that there is no confusion about what they need to do.

Use social proof

Adding social proof to your funnels can be a great way to increase conversions. Consider including customer testimonials, customer reviews, and case studies in your funnel pages.

With so many different options for consumers to choose from these days, it’s essential that you stand out. People are more likely to trust a product if they see that other people have used it and had success with it.

Create convincing supporting content

Supporting content like blog posts, videos, and presentations should be persuasive and geared toward convincing your readers to take action. Your supporting content should focus on addressing user pain points, outlining the benefits of your product/service, or providing relevant information about the industry.

The following are examples of supporting content you can use to optimize your conversion funnels:

Reduce friction on checkouts & forms

Did you know that nearly 70% of all online shoppers abandon their carts due to long forms and tedious checkout processes?

Reducing the friction on your website forms and checkout pages can dramatically improve conversions. To minimize checkout friction, make sure that you’re using only essential fields, such as inquiry/order information or billing details. Optimizing mobile experiences is equally important, as many users shop on their smartphones. Ensure your forms and checkout process are mobile-friendly and easy to navigate on smaller screens.

In addition, consider providing different payment options like PayPal in order to provide users with a more secure payment experience. Today’s consumers have been burnt one too many times, and providing secure payment options can help build trust with your customers.

If you're going to focus anywhere, focus on shopping cart abandonment.

Create informative, educational content that answers your users’ actual questions

Educational content is a great way to engage with new and returning customers. By providing helpful and informative educational material, you can help your users better understand the industry and how your product/service fits into it.

You can create blog posts, whitepapers, or downloadable PDFs that provide useful information related to your business, such as case studies, industry insights, and helpful tips.

These actions will help build trust with your users and improve their experiences with your company. They will also help convert leads into customers, as they will see you as a source of authority on the matter.

Capture your prospect's email address

Capturing your prospects’ email addresses should be a top priority. You can do this by offering free content (PDFs, whitepapers, etc.) in exchange for an email address. Or, you can provide incentives, such as discounts on products.

Once you’ve captured their email addresses, you can use them to send targeted messages and nurture leads through the conversion funnel.

Tools to help with conversion funnel optimization

To aid you in your conversion funnel optimization journey, there are several tools and tactics available that can help ensure a successful launch.

A/B Testing

If you’re not familiar with A/B testing, it’s a tool used to test the performance of two versions of a web page in order to determine which performs better. The process involves creating two versions of a webpage that have similar features and layouts but with one visible difference.

This could be something as simple as changing the color of your CTA button or rearranging the elements on the page. By testing these two versions of a webpage, you can determine which one is more successful in achieving your desired goal or following a desired path—whether that be increasing conversions, improving the user experience, or driving more sales.

Tools like SiteSpect, Optimizely, and AB Tasty are great testing tools.

Heatmapping software

Heatmap software is used to track and visualize user interactions on a webpage. The software shows you where users are clicking, scrolling, and hovering, so you can gain insight into how your web pages are being used.

By understanding how visitors actually interact with your page, you can make changes to optimize the conversion funnel for maximum efficiency.

Analytics software

Using product analytics or digital experience analytics tools—not just simply Google Analytics—is important in understanding the performance of your website and optimizing it for conversions.

Analytics software allows you to track user behavior, monitor traffic sources, and measure conversion rates. This type of qualitative and quantitative data gives you insight into how users are interacting with your site, so you can make changes accordingly.

Fullstory is an excellent example of analytics software that can help you get the most out of your conversion funnel optimization efforts.

Blog posts

Blogging is one of the most powerful tools available for marketers. A blog post provides an opportunity to engage with your audience, build trust, and drive conversions.

When you create and share content that aligns with the interests and needs of your customers, you can strengthen relationships and encourage users to take action. Additionally, blogs help to boost SEO rankings, providing a long-term benefit for your business.

By regularly creating and sharing blog posts, you can improve the performance of your conversion funnel.

Newsletters

Newsletters are another great tool for driving conversions. A well-crafted newsletter should contain engaging content, relevant offers, valuable promotions, and concise copy.

This type of communication allows you to keep in touch with customers and prospects regularly so they remain engaged and informed of your products or services. Additionally, newsletters provide the opportunity to drive people to your website, where they can take action and make a purchase.

Landing page optimization

Another crucial tool for conversion funnel optimization is landing page optimization. Landing pages are designed to capture leads and drive conversions.

By optimizing the design, content, and user experience of your landing page, you can increase conversions and maximize the performance of your conversion funnel.

Conversion rate optimization

Conversion rate optimization is the process of increasing the percentage of website visitors who take a desired action. This could be making a purchase, signing up for an email newsletter, or filling out a contact form.

When you optimize your conversion rate, you are improving the performance of your conversion funnel and ensuring that you are getting the most out of your website traffic.

Marketing funnel automation

Marketing funnel automation is a powerful tool for optimizing your conversion funnel. This process allows you to automate certain tasks, such as email marketing campaigns and lead nurturing calls.

Automating your marketing funnel allows you to efficiently manage your campaigns and reach potential customers on a larger scale.

Social media

No matter what line of business you’re in, social media is an important tool for driving conversions. Social platforms are great ways to engage with potential customers, build your brand, and grow your audience, even when starting a business with low investment.

If you don't already have a presence on social media, now is the time to create accounts and start posting content. You can use social media to spread the word about your business, share offers and promotions, and generate leads. If you are looking for a head start, you may consider buying followers.

Leveraging the power of social media will help you better optimize your conversion funnel for maximum performance.

Finally, pay-per-click (PPC) and display ads are effective tools for driving conversions. Ads allow you to target specific audiences with tailored messaging and offers.

If used effectively, PPC and display ads can be powerful ways to drive conversions and optimize your conversion funnel.

Following these steps will help you better understand the process of conversion funnel optimization and ensure that you are getting the most out of your campaigns.

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The best analytics products to use in 2024

The Fullstory Team — Thu, 10 Aug 2023 04:00:00 GMT

As the digital world continues to expand, so does the need for top-notch analytics products. With the power of cutting-edge analytics, businesses can transform complex data into insightful information, delivering enhanced understanding, and resulting in more strategic and effective decision-making.

Below, we dig into the leading analytics platforms to provide an in-depth look into analytics products' dynamic world.

What are analytics products?

Analytics products are software tools or platforms that help businesses extract, process, and interpret data to generate actionable insights. They bridge the gap between raw data and decision-making, equipping businesses with the necessary tools to assess performance, track trends, understand customer behavior, and drive growth.

There are different types of analytics products, each offering a specific set of capabilities. The most common examples include:

Descriptive Analytics: These tools analyze historical data to understand what happened in the past. They deliver insights through reports and dashboards, providing a clear view of business performance over a specific period.
Predictive Analytics: Powered by machine learning algorithms, these tools use historical data to make future predictions. They help businesses forecast trends, anticipate customer behavior, and make proactive decisions.
Prescriptive Analytics: These are advanced tools that not only predict the future but also suggest actions to take for optimal results. They leverage artificial intelligence and machine learning to automate decision-making processes.
Real-time Analytics: These tools process data as it comes in, providing instant insights. They're essential for businesses that require immediate responses, such as those in the ecommerce or finance sectors.

Why use analytics products?

The modern business environment is data-driven, with vast amounts of data being generated every minute. Product analytics have become vital tools to make sense of this data, turning it into actionable insights that guide strategic decision-making and foster business growth. Here are some of the biggest reasons to leverage one or more analytics platforms for your website or app.

Data-driven decision-making

Gone are the days when decisions based on assumptions or gut feelings were enough. Analytics tools provide businesses with solid evidence backed by data, greatly improving the accuracy and effectiveness of their decisions. Real-time analytics are crucial in this process, allowing businesses to quickly respond to market trends and changes in customer behavior. This immediate response not only enhances operational efficiency but also strengthens a business's competitive position in the market.

Improved customer experience

In today's customer-centric world, customer experience is crucial for business success. Customer experience analytics tools uncover valuable insights by identifying patterns and trends in customer behavior. Armed with this knowledge, businesses can tailor their products, services, and marketing strategies to better meet customer needs. This personalized approach increases customer satisfaction, loyalty, and ultimately leads to higher revenue.

Efficiency and productivity

Productivity analytics tools streamline processes by uncovering trends and patterns that guide smarter resource allocation. This not only boosts efficiency but also drives cost savings and profitability.

With real-time capabilities, teams can identify and resolve issues quickly, keeping operations on track.

The top product analytics platforms also include collaboration features, ensuring teams can act on insights together and improve workflow performance.

Risk management

In the ever-changing business world, risks and challenges are a normal part of daily operations. Predictive analytics tools help businesses identify potential risks and challenges in advance, enabling them to develop strategies to mitigate these risks.

Competitive advantage

Analytics tools provide valuable insights about the market, competitors, and customer behavior. Leveraging these insights can give businesses a competitive edge, helping them identify opportunities, devise effective strategies, and make informed decisions that differentiate them from competitors. Additionally, predictive analytics enables businesses to anticipate future trends, putting them at the forefront of their industry.

Innovation

Data insights from analytics tools can spark innovation in products, services, and business models. By identifying unmet customer needs and emerging market trends, analytics can inspire businesses to differentiate themselves and innovate. This drives business growth and establishes a reputation as an industry leader known for innovation.

Ultimately, this attracts new customers and increases customer retention.

Who should use analytics products?

Analytics products cater to a broad range of industries and professions due to their versatility, and professionals in nearly any industry can reap significant benefits

Business owners and executives: Analytics tools provide a comprehensive overview of business performance, empowering decision-makers with real-time insights that drive strategic planning and growth initiatives.
Product managers: Product managers can use analytics to track usage, understand feature adoption, and gain insights into customer preferences, thereby improving product development and customer satisfaction.
Marketers: With analytics, marketers can measure campaign effectiveness, understand customer behavior, and tailor strategies for optimal engagement and conversion.
Data analysts and scientists: These professionals rely heavily on data analytics tools for data mining, predictive modeling, and creating data visualizations to inform business strategies.
Customer service teams: Analytics can reveal patterns in customer inquiries and feedback, helping service teams enhance customer experience and resolve issues more effectively.
IT professionals: IT teams can use analytics to monitor system performance, identify potential issues, and ensure optimal operation.

Best analytics products for product analysis

Product analysis is crucial for understanding customer behavior, improving product features, and driving growth. Various analytics tools specialize in this and provide detailed insights.

Fullstory

Fullstory is a powerful digital experience intelligence platform known for its impressive capabilities in product analysis. It offers comprehensive insights into user interactions on your website or app, helping you understand how your product is used and where improvements are needed.

Fullstory offers session replay, funnel analytics, and click maps. The platform provides a robust array of features for capturing and analyzing digital experience data in real time. It leverages AI and machine learning for advanced predictive analytics and data mining, offering a full story about user behavior.

Pros of Fullstory

Real-time analytics provide instant insights
AI-driven analytics deliver predictive insights
Exceptional data visualizations make it easy to understand big data

Cons of Fullstory

There's a learning curve for some of its advanced features

Plans

Fullstory offers a free plan with limited features and other tiers with expanded functionality. Their Enterprise plan offers full access to the platform's capabilities, including advanced analytics, unlimited data retention, and priority support. Pricing is custom and based on specific needs.

Google Analytics 4

Google Analytics 4, the latest iteration of the popular Google Analytics platform, offers valuable insights into user behavior, helping businesses optimize their products. Google Analytics 4 provides real-time data, tracking features, and metrics. It offers both web and app analytics.

Pros of Google Analytics 4

Free for basic use
Integration with other Google products enhances functionality

Cons of Google Analytics 4

Can be complex for beginners to navigate
Limited customer support

Plans

Google Analytics 4 is free for basic use. An advanced version, Google Analytics 360, is available for enterprises with custom pricing.

Heap

Heap is an analytics platform that helps businesses approach product analysis. This includes tracking clicks, form submissions, transactions, emails, and more. With Heap, businesses receive a view of the customer journey, facilitating decision-making. Its reporting further helps businesses stay abreast with the latest trends.

Pros of Heap

Real-time reporting offers up-to-date insights

Cons of Heap

Implementation can be technically challenging and time-consuming
The user interface may be overwhelming for non-technical users

Plans

Heap offers custom pricing based on data volume and number of users.

Best for Business Intelligence

Business intelligence is critical for driving strategic decisions based on data. Several analytics tools offer strong business intelligence features. This section will compare Microsoft Excel, Domo, and Microsoft Power BI, giving you three powerful options for your BI management.

Microsoft Excel

Microsoft Excel, a component of the Microsoft Office suite, has long served as a trusted tool in statistical analysis and business intelligence. Owing to its extensive use across industries and professions, it brings a level of familiarity and ease of use that few other tools can match. Excel's primary strength lies in its powerful spreadsheet functionality. However, its capabilities extend far beyond this, offering a wide array of statistical and financial functions, pivot tables, and data visualization capabilities.

Pros of Microsoft Excel

Widely used and familiar to many.
Customizable for various data analysis needs.

Cons of Microsoft Excel

Lacks real-time data capabilities.
Can become complex and unwieldy with large datasets.

Plans

Excel is part of the Microsoft Office Suite, available in both standalone purchases and Microsoft 365 subscriptions, starting at $7/month for personal, and $10/month for family.

Domo

Domo is a cloud-based platform designed to provide direct, simplified, and real-time access to business data for decision-makers at all levels. Domo pulls together data from diverse sources, facilitating a unified view of business operations. It features interactive dashboards for a visual overview, and its data visualization tools translate complex data into comprehensible visuals.

Pros of Domo

Integrates with a wide array of data sources
Strong mobile access capabilities

Cons of Domo

The platform can be difficult for non-technical users
Customization requires technical expertise

Plans

Domo provides custom pricing based on business needs.

Microsoft Power BI

Microsoft Power BI is a collection of software services, apps, and connectors that transform unrelated data sources into coherent, visually immersive insights. Power BI can bring all your data together and turn it into coherent, visually immersive, and interactive insights. It offers data preparation, data discovery, and interactive dashboards.

Pros of Microsoft Power BI

Seamless integration with other Microsoft products.
Offers a good range of data visualization options.

Cons of Microsoft Power BI

Can be complex for non-technical users.
Limited ability to manipulate data compared to other tools.

Plans

Power BI offers a free version with limited capabilities, as well as more powerful paid versions. Power BI Pro is available for $10 per user/month, and Power BI Premium provides more capacity and flexibility starting from $4,995/month.

Tableau

Tableau is a leading tool in the field of business intelligence and data visualization. Designed for businesses of all sizes, Tableau provides an intuitive platform that transforms raw data into easily understandable formats.

It offers an extensive array of interactive data visualization, dashboarding, and data blending capabilities, thereby turning complex data into insightful narratives. With Tableau, users can drill down into their data, conduct in-depth analyses, and generate reports that aid strategic decision-making.

Pros of Tableau

Intuitive data visualization and dashboarding tools
Good data handling capacity

Cons of Tableau

Can become expensive for large teams
It has a steep learning curve for advanced features

Plans

Tableau offers a range of plans, starting with Tableau Viewer at just $15/month, and including Tableau Explorer for $42/month and Tableau Creator for $70/month.

Best for Data Exploration

Data exploration is a preliminary step in data analysis, where businesses deploy a range of techniques from statistical summarization to visualization methods to understand the makeup of a dataset. It's a critical process that uncovers patterns, identifies anomalies, and confirms assumptions with data.

Several tools offer these capabilities, but their efficacy varies significantly.

Looker

Looker is a modern data platform that is part of Google Cloud and offers powerful data exploration and business intelligence capabilities. Looker provides a platform for data exploration, visualization, and operational analytics. It offers advanced features like customizable dashboards, scheduled data deliveries, and data collaboration.

Pros of Looker

Highly customizable for various data needs
Seamless integration with SQL databases

Cons of Looker

Requires technical expertise for effective use
Customization can be time-consuming

Plans

Individual Viewer Users can be had for $30/month, the Standard User level is $60/month, and the Developer User is $125/month.

What is customer analytics? Definition, types & examples

Fullstory — Wed, 27 Mar 2024 07:00:00 GMT

Say hello to customer analytics, offering a treasure trove of insights from various sources. But this is not just about collecting data; it's about unlocking the stories hidden within. By understanding your customers' journeys, you're equipped with the tools to refine your offerings, elevate their experiences, and, yes, tackle the inevitable challenges like data quality, privacy concerns, and the seamless integration of this knowledge into your strategies.

What is customer analytics?

Customer analytics describes the process of systematically examining and interpreting data related to customer behavior, preferences, and engagement patterns.

It involves leveraging various analytical techniques, including data mining, predictive modeling, and machine learning, to uncover insights that inform strategic decisions.

Analyzing customer interactions across multiple touchpoints and platforms allows businesses to optimize marketing strategies, enhance customer experiences, and drive sustainable growth.

Gather data from various sources, such as websites, mobile apps, social media, and customer feedback surveys. By analyzing this information, you can identify patterns and trends that reveal valuable information about your customers.

6 types of customer analytics

Customer analytics empower teams to make informed business decisions, personalize customer experiences, and drive loyalty and growth.

Let's examine six key types of customer analytics that can transform how you connect with your audience.

1. Customer acquisition analytics

Customer acquisition analytics, a crucial component of marketing analytics, focuses on understanding how new customers find and choose your business. These insights help in optimizing marketing strategies and increasing the effectiveness of acquisition campaigns.

For example, a business might analyze customer data from various marketing channels to identify which ones are most efficient in attracting new customers, enabling a more targeted allocation of resources.

2. Customer segmentation analytics

Segmentation analytics break down your customer base into distinct groups based on shared characteristics like demographics, purchasing behaviors, or product preferences. This type of customer data analysis allows for more personalized marketing efforts.

For example, a retail company might use segmentation analytics to identify a group of high-value customers who frequently purchase premium products and then target them with specialized offers.

3. Customer engagement analytics

Engagement analytics measure how actively involved your customers are with your brand across various touchpoints. This can include website visits, social media interactions, and email opens.

For instance, a software company might track how users interact with its application features to determine which ones are most engaging and thus prioritize future development efforts.

4. Customer satisfaction analytics

Satisfaction analytics assess how happy customers are with your products, services, and overall brand experience. Surveys, feedback forms, and review analyses are common methods used.

An e-commerce platform could analyze customer reviews and ratings to identify areas of improvement in product quality or customer service.

5. Customer retention analytics

Retention analytics focuses on understanding why customers continue to choose your brand and what factors contribute to loyalty. This includes analyzing repeat purchase rates and customer lifecycle behaviors.

A subscription-based service might use retention analytics to identify the key factors that lead to subscription renewals and focus on enhancing those aspects.

6. Customer lifetime value analytics

Customer Lifetime Value (CLV) analytics predict the total value a customer will bring to your business over the entirety of their relationship with you. This helps in identifying the most valuable customers and optimizing resource allocation accordingly.

A financial services company might use CLV analytics to prioritize relationship management efforts for clients projected to have the highest long-term value.

Applying customer analytics

To get started, you can use customer analytics to improve your business and:

Identify your target customers: By analyzing customer data, you can identify the demographics and preferences of your most valuable customers and focus your marketing efforts on them.
Optimize your marketing campaigns: Use the insights gained from customer analytics to tailor your campaigns, ensuring that they resonate with your customers and drive engagement.
Personalize your customer service: Analyzing your customers' preferences and behaviors will allow you to offer a more personalized experience, making them feel valued and boosting loyalty.
Track your social media campaigns: With customer analytics, you can track the performance of your social media campaigns and adjust your strategies accordingly.

Types of customer data

Every interaction with a customer, from logging into your website to the more detailed aspects of their profile, is a data point. This data is invaluable in understanding who your customers are and how they interact with your brand. Let’s simplify and look at the main types of customer data and why they matter.

Personal data

Personal data covers the essentials about your customers, encompassing information that identifies an individual or is directly associated with them. It's straightforward but critical for personalizing experiences and ensuring transactions are smooth.

Examples of personal data include:

Name, birth dates, street address
Email address
Payment details

Engagement data

Engagement data (or interaction data) tracks how customers interact with your brand and content across various platforms. It’s about seeing where and how customers engage most.

Examples of engagement data include:

Email open rates
Click-through rates
Social media likes and shares

Behavioral data

Behavioral data reveals the actions and patterns of customers as they interact with a company's products or services. It allows teams to predict customer behavior, understand preferences, and improve product offerings.

Examples of behavioral data include:

Website visits and browsing behavior
Purchase history
App usage statistics

Attitudinal data

Attitudinal data captures customers' feelings towards your brand, including satisfaction levels, sentiments, and their purchase criteria. It’s about getting into the minds of your customers to align your offerings with their expectations better.

Examples of attitudinal data include:

Purchase criteria
Customer satisfaction
Customer sentiment

Stages of collecting and storing customer data

Let's distill the customer data journey into four main stages to understand your audience's preferences and actions clearly.

Data collection

To start collecting customer data, you need to gather information from various touch points such as websites, apps, social media, and customer surveys. This will help you understand your customers' preferences, behaviors, and needs.

Some valuable sources of customer data include demographic details, purchase history, and website behavior metrics. Be sure to use various methods like online surveys, behavior tracking tools, customer experience analytics tools, and preference indicators to gather a comprehensive view of your customers.

Data sorting

Once you have collected the data, it's important to sort and filter it in order to focus on the most relevant information. You can segment your data based on factors like demographics, purchase history, and user preferences. This will help you target specific customer segments and tailor your marketing strategies

Predictive analytics can help you forecast future customer behaviors and preferences, making it easier for you to engage with your audience effectively.

Data storage

Properly storing the customer data you've collected ensures its security and accessibility. Various customer data platforms (CDPs) are available that are specifically designed to store and manage customer data securely. By using a CDP, you'll be able to access and consolidate data from various sources, making insights easier to uncover.

Be sure to always comply with data privacy regulations in your region, like GDPR (for Europe) or CCPA (for California), when handling customer information.

Data analysis

The final step is to analyze the collected and stored data, so you can make informed decisions that benefit both your customers and your business. Metrics such as Customer Lifetime Value (CLV), Net Promoter Score (NPS), and user engagement can provide valuable insights.

Tools like Google Analytics, Adobe Analytics, and Fullstory are useful for understanding customer behavior data on your site or app.

Implementing customer analytics

Getting started with customer analytics is about understanding your customers better and ensuring your strategies align with their needs.

Let's take a quick look at making the most out of customer analytics to improve those all-important customer relationships.

Best practices and strategies

When implementing customer analytics in your business, it's important to follow four simple best practices to ensure success.

Know your customers: Segmenting customers based on their behavior patterns, demographic data, or product preferences, you can tailor your campaign to suit their individual needs and preferences.

Keep your customers close: Customer retention strategies also play a crucial role in optimizing your data analytics efforts. By identifying trends, patterns, and triggers that lead to customer churn, you can take proactive steps to prevent it, increasing customer loyalty and reducing customer acquisition costs.

Visualize your data: When it comes to data visualization, don't underestimate the power of a well-designed dashboard. Clear and concise visuals will allow you to understand your customer data better, identify opportunities, and communicate your insights more effectively with your team members.

Valuable integrations and tools

Effective customer analytics strategies require seamless integration of various tools and technologies. A well-designed customer analytics ecosystem will help you collect, analyze, and report on customer data more efficiently.

Consider incorporating popular customer relationship management (CRM) solutions that can better manage customer data across various touchpoints.

Additionally, investing in tools specializing in predictive customer analytics, which is a core component of fintech analytics, can give you insights into future customer behavior, allowing you to make data-driven business decisions for marketing campaigns, customer retention, or product development.

Challenges of customer analytics

Implementing customer analytics comes with its challenges. Here, we'll outline three key obstacles and offer strategies to navigate them effectively, keeping your analytics efforts compliant and impactful.

1. Cookies and privacy concerns

With the increasing reliance on customer analytics, it's crucial to ensure that your organization is managing cookies and user data responsibly. This is because these tools often involve the collection of personal data, which can raise privacy concerns among customers.

Always be transparent about the data you're collecting and how it's being used. Furthermore, it's essential to comply with relevant data protection regulations, such as GDPR and CCPA, to avoid facing potential legal issues.

2. Security and ethical considerations

Another challenge in customer analytics strategies involves addressing security concerns. Ensuring that your customer's personal data is kept safe and secure should be a top priority.

Invest in robust cybersecurity measures and follow best practices for data management and storage. In addition to security, it's critical to consider the ethical implications of customer data usage, such as avoiding biased algorithms or discriminating against certain customer segments.

3. Cost and ROI analysis

The cost of customer analytics applications can vary, making it essential for businesses, particularly small ones, to assess the potential return on investment (ROI) before committing to these strategies.

Consider factors like the cost of employing data analysts, software, and infrastructure needed to support your analytics initiatives.

When assessing the profitability of your customer analytics efforts, keep in mind that it may take time to see tangible results. Factors that can contribute to a higher ROI include increased customer loyalty, improved marketing campaign targeting, and enhanced decision-making.

Here are some tips to help you better assess the ROI of your customer analytics investments:

Establish clear objectives and metrics for your analytics efforts.
Regularly monitor the impact on key performance indicators (KPIs), such as revenue, retention, and customer satisfaction.
Continuously evaluate and adjust your analytics strategy as needed.

Collaborating with a customer data analytics partner can also provide access to advanced capabilities and insights, allowing for more effective data-driven strategies.

Customer analytics holds the key to a personalized customer experience

Getting into customer analytics is pretty straightforward—simply put, it’s all about understanding your customers better to make smarter business decisions.

It's like having a direct line to what your customers are thinking and wanting. Sure, there's a bit of a learning curve with the tools and data, but the insights you gain are invaluable. They help you fine-tune your marketing, improve your products, and ultimately, keep your customers coming back for more.

Think of it this way: every bit of customer feedback, every purchase, and every click they make is a piece of the puzzle. And while piecing it all together might seem challenging, it’s definitely worth it. The better you understand your customers, the more you can tailor your offerings to meet their needs.

Customer experience analytics: how to use data to improve CX

The Fullstory Team — Fri, 10 Apr 2026 04:00:00 GMT

Customer experience analytics is the practice of collecting and analyzing data at every stage of the customer relationship: actions, preferences, and direct feedback. Technologies like machine learning surface patterns in that data so businesses can predict behavior, personalize the experience, and identify where things go wrong.

What data does customer analytics use?

Customer experience analytics draws on several data types to build a complete picture of customer interactions and satisfaction. That data comes from multiple channels: websites, mobile apps, social media platforms, customer service interactions, and in-store visits.

The most common data types include:

Direct feedback

Data solicited directly from customers: surveys, reviews, complaints, and suggestions. Key CX metrics include:

Net Promoter Score (NPS): This measures customers' willingness to recommend a company's products or services to others.
Customer Satisfaction (CSAT): CSAT assesses the degree to which a product or service has met or exceeded customer expectations.
Customer Effort Score (CES): This gauges the ease with which customers can get their issues resolved or needs met.
Voice of Customer (VOC): VOC programs aim to capture customers' expectations, preferences, and dislikes.
Behavioral data: Behavioral data refers to information about how customers interact and behave across different touchpoints. This data includes website browsing history, purchase history, social media engagement, customer service interactions, and customer sentiment analysis from these interactions.
Transactional data: Transactional data refers to information regarding customers' purchases and interactions with a company. It includes details like the frequency of purchases, timing, the amount spent, and the specific products or services bought.
Demographic data: This type of data includes basic information about the customer, such as age, gender, location, occupation, and income level.
Psychographic data: This involves more subjective characteristics of customers, such as their interests, attitudes, values, lifestyle, and personality traits.

These data types come together to form a complete view of the customer, which is critical for analyzing and improving the overall customer experience.

What is the role of analytics in customer experience?

Analytics shapes the customer experience by giving businesses the data they need to make decisions and fix what's not working. Customer experience analytics covers the full picture across channels and touchpoints, while customer journey analytics zooms in on specific interactions within that journey.

Understanding customer behavior: Analytics reveals how customers interact with products, services, and platforms, making patterns and trends visible.
Segmenting customers: It groups customers by behaviors, preferences, or demographics, making marketing more targeted and personal.
Predicting customer behavior: Predictive models let businesses anticipate what customers will do next, so teams can act before problems arise.
Optimizing the experience: Analytics identifies friction in the customer journey, giving teams a clear target for improvement.
Informing product development: Behavioral insights point product teams toward changes that match real customer needs rather than internal assumptions.

Analytics turns raw data into organized insights that drive satisfaction and loyalty.

Why is customer experience analytics important?

Customer experience analytics matters most to businesses that take customer relationships seriously. It gives teams the data to customize experiences to individual preferences, identify pain points before they compound, and forecast future behavior to support strategic planning.

When you can catch dissatisfaction signals early, you resolve issues before they drive churn. Predictive analytics extends this further: instead of reacting to problems after the fact, teams can anticipate them.

Customer experience analytics drives customer loyalty, optimizes conversion rates, and supports business growth.

How do you use customer experience analytics?

Teams apply customer experience analytics in a few high-impact ways:

Personalize experiences

Analytics surfaces individual customer preferences, behavior, and past interactions. That knowledge lets you tailor the product experience to each customer rather than treating everyone the same.

Reduce customer churn

Behavioral data flags dissatisfaction signals early. Catch them while there's still time and you can take steps to retain customers before they leave.

Increase repurchase rates

Purchase history and behavioral patterns reveal where upsell and cross-sell opportunities exist, and which offers are actually relevant to a given customer.

Apply predictive analytics

Predictive analytics forecasts customer behaviors and trends before they materialize. Teams that act on those signals adjust strategy proactively rather than scrambling after the fact.

How to perform a customer data analysis

Customer data analysis follows six steps, repeated in iterations as behavior and business goals evolve:

1. Define your objectives

Decide what you want to learn. Are you trying to reduce churn, improve a specific flow, or predict future behavior? The answer shapes which data to collect and how to interpret what you find.

2. Collect customer data

Gather data from relevant touchpoints: social media, customer feedback, transaction history, and website interactions. Follow applicable data privacy regulations throughout.

3. Organize the data

Clean and structure the data so it's ready to analyze. This often involves transformation processes to get everything into a consistent, workable format.

4. Analyze customer data

Use statistical methods, predictive models, or AI to find patterns and trends. The goal is insights that actually drive decisions, not more data.

5. Create a data-driven strategy

Turn your findings into action: personalized campaigns, improved service processes, better product recommendations. The insight is only as useful as what you do with it.

6. Iterate

Customer behavior changes. Repeat this process regularly so your strategies stay current rather than built on last year's data.

Customer data analytics tools

Here are the platforms teams commonly use for customer experience analytics.

Platform	Best for	Key strength
Fullstory	Enterprises needing a complete behavioral context	Intelligent digital experiences powered by human context: the behavioral data infrastructure that makes your AI strategy work
Google Analytics	Baseline traffic and conversion tracking	Free and widely used (GA4)
Contentsquare	Enterprise journey and in-page behavior analysis	Journey analysis, friction scoring, and revenue impact overlay
Mixpanel	Product teams focused on funnels and retention	Event-based analytics and self-serve segmentation

Fullstory

Fullstory is the Intelligent Digital Experience Platform. Most CX analytics tools tell you what customers reported: survey scores, CSAT, NPS. Fullstory captures what they actually did. Fullcapture records every click, scroll, form input, and frustration signal like Rage Clicks and Dead Clicks without manual event tagging, giving CX teams behavioral context that surveys alone can't surface.

StoryAI turns that behavioral data into action: it identifies where customers struggle, summarizes sessions so teams skip hours of replays, and surfaces friction patterns across thousands of users at once. Guides and Surveys closes the feedback loop, delivering in-app guidance at exactly the moment a customer needs it and collecting contextual feedback tied to real behavior.

Fullstory runs across three product lines: Fullstory Analytics for product and UX teams, Fullstory Workforce for support and CX operations, and Fullstory Anywhere for data teams feeding behavioral signals into the rest of the AI stack.

Because the behavioral data is captured without gaps or bias, it is also the ground truth for AI agents: the digital sight that lets agentic systems understand what customers are actually experiencing rather than what they reported.

Google Analytics

Google Analytics is the most widely used web analytics platform, available at no cost through Google. The current version, GA4, provides event-based tracking, audience segmentation, acquisition reporting, and conversion measurement. It works well for understanding traffic sources and broad user behavior, but relies on manual event configuration and doesn't capture session-level behavioral context.

Contentsquare

Contentsquare focuses on digital journey analysis and in-page behavior, turning customer interactions into friction scores and visual heatmaps. Its Sense AI layer surfaces experience problems before teams go looking and connects behavioral patterns to revenue impact. The platform now includes Heap (acquired 2023), extending its product analytics coverage.

Mixpanel

Mixpanel is a product analytics platform built around event-based tracking. It's strong for funnel analysis, retention charting, and user segmentation. It relies on manual event instrumentation, which means interactions must be tagged in advance to appear in your data. Teams that need automatic behavioral capture or session-level context will need a complementary tool.

Closing the gap

Surveys tell you someone was frustrated. Behavioral data shows you where, why, and how many others hit the same wall before anyone said anything.

That's the gap customer experience analytics is built to close. And in the AI era, good behavioral data does more than inform your team: it feeds the intelligent systems that see friction in real time, infer what customers need, and act before the relationship ends.

See how Fullstory works.

The mobile-shaped hole in your analytics

Kevin Margatan — Thu, 25 Jun 2026 06:00:00 GMT

Many organizations still treat mobile analytics as a nice-to-have or something to get to eventually. But more and more, customers depend on mobile, which means mobile data should play a key role in informing product decisions, conversion analysis, and AI initiatives.

If you’re only tracking web analytics, you’re missing out on crucial context.

A customer journey broken in half

Customer journeys rarely happen on only one device. A hotel guest browses hotels on the web while sitting at a laptop, then switches to the mobile app to book. A traveler researches flights on a laptop, but books, checks in, changes seats, and accesses boarding passes through the mobile app. A shopper browses a catalog in a desktop tab, then uses the mobile app to find the item in-store later that day.

Without a unified record across both web and mobile, teams see multiple sessions that look unrelated when really they were all touchpoints in a single journey. Every downstream analysis ends up operating on a partial input. A funnel that ends on the web looks like abandonment, but was actually a successful switch to the app. A drop in mobile engagement reads as a churn signal when it's really a customer who finished their task on the web.

The story you believe about your customers’ experience, the information you base your decisions on, is incomplete.

A customer journey not seen at all

The cross-surface case is the most visible version of the mobile gap. Another version is increasingly common: the customer journey doesn't begin on the web at all.

The trend really started in emerging markets. In Latin America, parts of Africa, and across much of Asia, the personal computer wasn't a common household device the same way it was in other parts of the world. Mobile networks expanded faster than home broadband infrastructure. Smartphones became the dominant on-ramp to the internet for these markets, who crossed the threshold of digital purchase on a screen the size of their palm.

The leading apps in those markets reflect that. Try booking a taxi with ComfortDelGro—the yellow cab of Singapore—on a laptop, for example, and you’ll simply be redirected to download the mobile app, because the business decided they don’t need a web booking flow.

That same trend is emerging in the U.S. and European markets, although with a bit of a lag. The high-intent moments that used to anchor on the web, like booking, planning, and transacting, are increasingly anchoring on the phone. Customers still research on the web, but the surface they commit on is increasingly the phone. If your analytics only sees the research, you're watching the half of the customer relationship that didn't end in revenue.

A big gap on a tiny screen

Here's the paradox: the mobile-shaped hole in your analytics is undeniably massive, but the device at the center of it is tiny.

The entire customer journey you're missing is happening on a screen barely larger than your palm. In that limited real estate, there’s no room for ambiguity. Every tap, scroll, and hesitation carries a signal. Because the interface is so compressed, friction that might go unnoticed on a sprawling desktop layout becomes an immediate obstacle on mobile.

And the constraints don't stop at the screen; the device running all of this draws less power than half of a single LED bulb in your home. It may be operating on a data plan that runs out or switching between wifi and a patchy cell signal mid-session. The phone is doing an extraordinary amount with remarkably little—a fraction of the energy, a fraction of the bandwidth, a fraction of the screen—and yet it's become the surface where your highest-intent customers make their most consequential decisions.

Which raises the question: how do you capture data that is rich enough to be meaningful from a device that has so little to spare?

The most intimate device you own

There's one more dimension to mobile that has no real equivalent on the web: proximity.

Your phone is the most intimate digital asset you possess. It's with you when you wake up and when you go to sleep. It knows your location, your routines, your contacts, and increasingly, your financial life. The apps on it tend to hold more sensitive information than anything sitting in a browser tab. And because of that, users bring a different set of expectations to mobile. There's an implicit understanding that what happens in an app stays closer to you than what happens on a website.

That’s a legitimate expectation, and mobile analytics tools should be designed with it in mind.

The goal of mobile analytics isn't to harvest everything. It's to capture what's meaningful while leaving what's sensitive alone. Done well, analytics runs through a fine sieve—preserving the quality of behavioral signal, the clarity of the journey, the richness of the interaction data—while filtering out anything that touches personal or sensitive information. The result is data that is useful without being invasive, rich enough to make confident decisions on, and responsible enough to deserve the trust your users extend when they open your app.

See the complete picture with Fullstory

Filling the mobile-shaped hole in your analytics means capturing mobile data in a way that lets it sit within the same analytics platform as your web data, so the cross-surface journey is visible end-to-end.

Fullstory's mobile capture is built for exactly that. The output is structured the same way the web output is structured, which means every analytics tool, warehouse pipeline, and dashboard you already use for web data works the same way for mobile data. The session you watch can be a web session replay, a mobile session replay, or both, and the customer is recognized as one customer across the two surfaces. The single pane of glass that most teams have been trying to build for years is the default.

→ Ready to get started with Fullstory for Mobile Apps? Learn more here.

What travelers want right now (and what keeps them from booking)

The Fullstory Team — Tue, 23 Jun 2026 05:00:00 GMT

Fullstory surveyed more than 1,000 U.S. consumers to understand how rising costs, digital friction, and changing expectations are reshaping the travel experience. Here’s what the data tells us, and what it means for travel and hospitality brands navigating a more demanding market.

Travel is still happening. The math just looks different.

Demand hasn’t collapsed. Seventy percent of respondents traveled in the past year or plan to travel this year. But consumers are making sharper tradeoffs: 33% are cutting budgets or shortening trips, 31% are booking earlier to lock in prices, and 20% are swapping flights for road trips or other lower-cost alternatives. Only 13% say they’re postponing travel altogether.

Baby Boomers (25%) and Gen X (18%) are the least likely to adjust plans due to rising costs. Millennials and Gen Z are more reactive: just 14% of Millennials and 8% of Gen Z report making no changes to their travel behavior.

What this signals for brands: the market isn’t shrinking, but it is bifurcating. Value-seeking consumers are still showing up—they just need more from the experience to justify the spend.

Search and OTAs still own early-stage planning. AI is gaining ground.

When consumers start planning a trip, they turn to search engines (53%), online travel agencies like Expedia or Kayak (51%), and direct airline or hotel websites (44%). These channels aren’t going anywhere. But generative AI is starting to enter the mix. Fifteen percent of respondents say they use tools like ChatGPT or Claude to begin the planning process—including 19% of Gen X and 17% of Millennials. That’s not a dominant share yet, but it’s a meaningful signal about where discovery is heading.

The takeaway here is that brands need to be findable through traditional channels while preparing for a world where AI-mediated recommendations increasingly shape where people consider going.

The three things that actually drive booking decisions

When asked what matters most in a travel experience, consumers ranked their priorities clearly:

Price and overall value—77%
Quality of customer service—51%
Convenience of booking—48%

Loyalty rewards and digital tools ranked lower. Value, service, and ease are the

fundamentals—and they’re being evaluated at every step, not just at checkout.

Hidden fees are the biggest trust-breaker in travel

Sixty-one percent of respondents cited hidden or unexpected fees as their top frustration when booking travel. That’s not a design problem or a UX problem in isolation—it’s a trust problem with downstream consequences.

Nearly 70% of respondents said they would be likely to switch travel brands after a single negative experience. That number should give any travel brand pause. Loyalty in this category is fragile, and friction doesn’t just lose a transaction—it loses the customer. Booking abandonment tells a similar story. Thirty-one percent of consumers say they abandon bookings when final pricing changes too late in the process. Another 24% leave to compare options and don’t return. These aren’t passive drop-offs—they’re active rejections of an experience that felt untrustworthy or too difficult to complete. Understanding why people leave, and at which step, is where behavioral data becomes operational. When brands can see exactly where users hit unexpected fees, slow down, or exit without completing a purchase, they can start fixing the right things. That’s the difference between assuming the checkout flow is fine and knowing it isn’t.

Why people still book through OTAs—and what it would take to change that

When asked what would make them more likely to book directly with an airline or hotel, consumers gave a clear hierarchy:

Lower or price-matched rates—44%
Faster and simpler booking experience—19%
Better loyalty rewards—19%

Price is the primary lever, but it’s not the only one. A fifth of consumers would shift their behavior for a meaningfully better booking experience alone—without a price incentive.

That’s a real opportunity for brands willing to invest in the quality of their direct channel.

Personalization works—but only when it’s useful, not decorative

Consumers are open to personalization, but they’re selective about what kind. The top preference (by a wide margin) is personalized pricing, discounts, or bundled offers (55%). Destination recommendations came in at 41%, and content-based suggestions trailed at 26%.

The pattern is consistent: travelers want personalization that reduces friction or saves them money, not personalization that just makes the experience feel curated. Knowing that a guest prefers a king bed is useful. Serving them an email about “dreamy destinations” when they’re mid-booking is not.

Digital tools that support the actual journey also ranked highly: mobile boarding passes and check-in (63%), real-time alerts and notifications (56%), and centralized itinerary management (49%). These aren’t flashy features—they’re operational ones. They reduce uncertainty, keep the traveler informed, and minimize the moments where something going wrong becomes a reason to never book again.

What this means for travel and hospitality teams

The survey points to a consistent underlying theme: consumer tolerance for friction,

surprise costs, and impersonal experiences are low—and the consequences of getting it wrong are higher than brands may realize.

A few implications worth sitting with: Transparency is a competitive advantage. Sixty-one percent of consumers naming hidden fees as their top frustration isn’t just a complaint—it’s an opening for brands that price and communicate clearly.

The booking experience is a retention tool. Nineteen percent of consumers would book direct for a better experience alone. The brands investing in faster, cleaner booking flows aren’t just reducing abandonment—they’re recapturing margin they’re currently handing to OTAs.

Personalization needs to earn its place. Value-based personalization—pricing, bundling, recognizing loyalty—resonates. Personalization as atmosphere doesn’t. That’s where to focus.

Behavioral data closes the gap between assumption and reality. When a team debates whether hidden fees are really the problem, or whether users are abandoning because of confusion on the confirmation step, behavioral data settles the question. Brands that can see the full picture of how users move through their digital experiences are better positioned to act on what consumers are actually saying, not just survey by survey, but continuously.

The gap between what travelers expect and what they experience is where revenue goes. See how leading travel brands close it. Take the tour →

The Fullstory 2026 Travel & Hospitality Survey was conducted in May 2026 by a third-party research provider. The study surveyed more than 1,000 U.S. consumers.

App performance metrics and the hidden tax of analytics tools

The Fullstory Team — Mon, 22 Jun 2026 06:00:00 GMT

Mobile teams obsess over app performance metrics—and for good reason. Load time, startup speed, crash rates, and frame drops are the difference between an app users open every day and an app they delete after the first session.

Unfortunately, the very tools teams use to measure mobile app performance sometimes make it worse.

This post breaks down why most analytics tools hurt app performance, which app performance metrics product and engineering teams should track, and how to choose a tool that captures the data you need without taxing the app it's measuring.

The hidden tax of heavy mobile analytics SDKs

A heavy analytics SDK can slow down your app's startup time, increase memory usage, drain battery life, and lead to more crashes. Individually, these issues may seem minor, but they compound quickly.

A bulky SDK can add 300-500ms to loading times, raise the app size by 1-2MB, and create extra data calls that slow things down, particularly on slower networks. If your app takes too long to load, users may uninstall it before they even get a chance to try it.

Additionally, aggressive tracking methods can cause lag and dropped frames. When the frame rate falls below 60 frames per second, users will notice stuttering, which can lead to application not responding (ANR) events and further frustrate them.

Core app performance metrics to track

App startup and load time

Load time refers to how long it takes for an app to launch or display a specific screen, including cold starts (when the app is fully closed), warm starts (when the app is in the background), and hot starts (when the app was recently active). Comparing load times before and after pushing app updates helps identify any performance regressions.

UI responsiveness and smoothness

Response time measures how quickly the app processes user inputs and loads pages. Tracking input delays, dropped frames, freezes, and user behaviors like rage taps or dead taps can reveal frustration points.

Crash rate and app not responding rate

Crash rate is the percentage of user sessions ending unexpectedly due to errors. Average crash-free session rates are very high—around 99.95% for Android and 99.87% for iOS. Falling below these can lead to lower app store ratings and increased user churn.

Network performance and bandwidth usage

Network metrics include API latency (time for a request to reach the server and get a response), network latency (delays in sending/receiving data), network throughput (amount of data transferred per second), and error rates (failed requests or system errors). If your app experiences slow load times or network delays, these metrics help identify bottlenecks that need investigation.

Memory, CPU, and battery impact

Monitoring how much device memory (RAM) your app uses, how heavily it taxes the CPU, and how much battery it consumes is vital, especially on devices with limited resources. High CPU usage can cause battery drain and overheating. Keeping these resource uses low ensures better overall app performance and user satisfaction.

Performance and engagement metrics that heavy SDKs distort

Active users and stickiness

Daily, weekly, and monthly active users show how many people use your app and how often they return. The stickiness ratio, which divides daily active users by monthly active users, helps you understand user loyalty. A ratio above 20% is generally considered good.

Session length, interval, and task completion

Understanding how long users spend in your app and how often they return helps you gauge user experience. Short sessions may indicate frustration, while longer ones might suggest users are struggling to complete tasks. By combining these metrics with task completion rates and time to first value (TTFV)—the duration it takes a new user to experience the app's core benefit—you can pinpoint areas where users succeed or face challenges.

Retention, churn, and uninstalls

Retention tracks the percentage of users who keep using the app after installation, often measured at Day 1, 7, and 30. High churn rates indicate users aren’t finding enough value. Understanding churn helps pinpoint issues with usability or satisfaction.

How to evaluate the performance impact of any mobile analytics SDK

Set a performance baseline

Start by measuring key app metrics like startup time, time to first interaction, crash rate, and resource use (network, CPU, memory, battery) on a range of devices—everything from new flagship models to older phones. Test under various network conditions like Wi-Fi and slower mobile connections to see how the app performs in real-world situations.

Compare versions with and without the SDK

Create nearly identical app builds, one with the analytics SDK and one without. Track differences in startup speed, screen load times, crashes, network traffic, and CPU use during critical flows like onboarding and checkout. This helps isolate the SDK’s impact.

Monitor ongoing performance

Use real-time monitoring to catch any regressions in app behavior as you release updates. Early alerts about crashes or slowdowns allow your team fix issues before a large number of users are impacted.

Define acceptable limits

Set clear thresholds for how much the SDK can affect your app, such as a small increase in startup time or maintaining a high crash-free rate. Use these limits to guide optimization efforts and keep your app running smoothly without wasting resources.

Fullstory captures rich behavioral data without the heavy tax

Stop watching users abandon your app without being able to do anything about it. With Fullstory for Mobile Apps, you can gather the behavioral insights you need without compromising on performance.

Lightweight SDK and performance-focused design

Fullstory for Mobile Apps is engineered to have minimal impact on your app’s performance. Its compact SDK (around 100kb/minute) efficiently logs user interactions like taps, swipes, and gestures without streaming heavy image files, reducing bandwidth and CPU usage.

No manual instrumentation overhead

Fullcapture logs and indexes user behavior without forcing teams to predefine every selector or scatter custom events through the codebase. Fewer tracking calls mean fewer bugs, simpler testing, and less risk around app quality.

Privacy-first session replay that respects app health

Fullstory is Private by Default; it evaluates and redacts sensitive data on-device. Screenshot-free session replay reconstructs views from draw instructions, which supports privacy and helps monitor performance without unnecessary payload.

→ Ready to see how Fullstory can help you ship mobile experiences that convert? Request a demo to learn more.

StoryAI Agents: Expert analysis & 24/7 monitoring, built on behavioral data

The Fullstory Team — Mon, 22 Jun 2026 05:00:00 GMT

Most teams using Fullstory have gotten good at finding problems. Session replay, opportunities, funnel analysis—these tools surface what’s going wrong. The work that follows, figuring out why, confirming the scope, routing the finding to the right person, doing it again after the next release, has always landed back on a human.

StoryAI Agents is built to take on that work. Where StoryAI gave you faster answers, Agents complete entire jobs. They investigate issues, monitor releases, analyze funnel performance, and watch for high-value behavioral moments across every session, without waiting to be asked.

→ StoryAI Agents was one of three products we announced at our Summer Release Event. See what else is new.

Built on Fullcapture

What makes agents reliable is the data underneath them. StoryAI Agents work from Fullstory’s complete behavioral record—every click, scroll, network call, DOM change, and error, captured automatically without manual tagging or sampling. Every output links back to session replays, so findings can be verified rather than taken on faith.

Expert Agents: on-demand investigation

Expert Agents are purpose-built for the jobs Fullstory users have always come to the product to do. The difference is they do the investigative work for you.

Issue Investigator takes a problem description—a reported bug, a pasted support email, anything—and digs in autonomously. It finds the affected sessions, identifies what happened, and returns a diagnosis with suggested next steps. What previously took a cycle of ticket logging, session hunting, and cross-referencing is compressed to minutes.

In practice: a product team noticed friction spikes on a spring homepage campaign. Issue Investigator traced the dead clicks to a surge in network errors on the product detail page, and identified the cause: images were pointing to the wrong URL rather than the CDN. The finding went straight to the team’s Slack channel.

Conversion Optimizer analyzes your most important user outcomes and uses behavioral evidence to identify where and why users are dropping off. It produces optimization recommendations grounded in what users actually did, not inferred from aggregate patterns.

Release Analyzer assesses the behavioral impact of a specific code release. Rather than manually monitoring dashboards after a deploy, you can ask Release Analyzer to evaluate what changed for users and flag any regressions.

Session Finder locates sessions matching a natural language description. Instead of building a segment from scratch, you describe what you’re looking for and the agent finds the relevant sessions.

24/7 Monitoring Agents: always-on coverage

Expert Agents run when you ask them to. Monitoring Agents run continuously, watching your data and alerting your team when something needs attention.

These agents are configured with a specific job to do and a delivery channel to report to. When the condition they’re watching for is met, they send the finding wherever your team works—Slack, email, or another connected tool.

A few of the agents available:

Release Health Monitor continuously watches for errors and behavioral regressions after each deploy, so issues surface immediately rather than when customers start reporting them.

Conversions Optimizer tracks drops in a configured funnel and flags anomalies as they develop.

VIP Frustration detects when a high-value user hits a defined level of friction, summarizes their session, and sends it to the support team to follow up. The support agent receives the behavioral context before the customer has to explain anything.

The monitoring agents cover both aggregate trends—conversion rates, error rates, engagement metrics—and individual session-level behavioral triggers. A spike in aggregate data might warrant attention. A VIP user rage-clicking through a broken flow almost certainly does.

StoryAI Home

StoryAI Agents live inside a new hub in the Fullstory UI called StoryAI Home. Expert Agents are available on demand from a central interface. Monitoring Agents are configured, tracked, and managed there as well, with a live dashboard showing their activity and the insights they’ve surfaced over time.

Early access

StoryAI Agents is now in early access. Talk to your account team to learn more.

→ StoryAI Agents was one of three products we announced at our Summer Release Event. See what else is new.

14 of the best marketing analytics tools to improve campaign ROI

The Fullstory Team — Wed, 17 Jun 2026 20:00:00 GMT

Users are less tolerant of friction than ever, with mobile bounce rates up 54% year-over-year. That puts more pressure on marketers than ever to prove ROI and create campaigns that connect with their audience.

Most teams have no shortage of data. The problem is that channel-level metrics only tell part of the story. They show you what happened, but not the why behind user actions.

This guide covers the different types of marketing analytics tools and the best options on the market. You will also find guidance on building a stack that covers every layer of your funnel and choosing the right tool for your team.

What are marketing analytics tools?

Marketing analytics tools are software platforms designed to monitor, analyze, and manage the performance of your marketing initiatives. They offer a view of your business's marketing efforts by integrating with various data sources and providing insight into how to optimize your efforts.

These tools provide digital marketers the ability to monitor and gain insights on key metrics like:

Click-through rates
Conversion rates
Customer lifetime value
ROI
Cost-per-click (CPC)
Customer acquisition cost (CAC)

Three layers of analytics

While most teams can measure clicks and impressions, the harder challenge is connecting that data to revenue and business outcomes. That's where the right tool makes the difference.

Web analytics tools track what happens on your site. Product analytics tools focus on product engagement. Marketing analytics tools go further, tying campaign performance to pipeline, revenue, and the full customer journey.

Not every tool covers every layer; choosing the right combination is what gives marketing teams a complete picture. The tools below are a good place to start.

Best marketing analytics tools overview

Navigating through the vast sea of marketing analytics tools can be overwhelming. To help you make an informed decision, we have listed down some of the top tools in the market.

Tool	Category	Best For
Fullstory	Digital experience analytics	Connecting campaign performance to on-site conversion
Google Analytics	Web analytics	Traffic, audience, and conversion tracking
Optimizely	A/B testing and CRO	Experimentation and personalization
Semrush	SEO and SEM analytics	Keyword research and competitor analysis
Ahrefs	SEO analytics	Backlink analysis and rank tracking
Mixpanel	Event-based analytics	Event tracking and user segmentation
Heap	Auto-capture event analytics	Retroactive event tracking without manual setup
Domo	Business intelligence	Cross-functional data visualization
Funnel.io	Marketing data aggregation	Centralizing data from 500+ ad platforms
BuzzSumo	Content analytics	Content discovery and competitor tracking
Sprout Social	Social media analytics	Social scheduling and reporting
HubSpot Marketing Hub	Marketing attribution	Full-funnel campaign and CRM attribution
Triple Whale	Marketing attribution	Ecommerce attribution and ROAS tracking
Hotjar	SMB website analytics	Heatmaps and lightweight UX feedback

1. Fullstory

Best for: Marketers connecting campaign performance to on-site behavior and conversion

G2 Rating: 4.5/5

Fullstory is a digital experience analytics platform that gives marketers a behavioral layer between acquisition and conversion. Where most tools stop at channel-level data, Fullstory gives marketers a behavioral analytics layer that sits between your acquisition tools and your CRM.

Fullcapture automatically indexes every user interaction without manual tagging or a tracking plan, so your team does not need engineering support to get the data you need. StoryAI surfaces friction points and campaign insights automatically, so marketers spend less time digging through dashboards and more time acting on what matters.

Key features:

Fullcapture: Automatic data capture with no manual tagging or tracking plan required
StoryAI: AI-powered insights that surface friction points and opportunities marketers would not otherwise see
Funnel analysis: Identify exactly where campaign traffic drops off between click and conversion
Heatmaps: Visualize where users engage and what they ignore on landing pages and key conversion pages

Pros

No manual tagging: Fullcapture automatically indexes every user interaction, so your team gets the data without waiting on engineering.
Built for your stack: Fullstory integrates with your existing marketing tools and pipes behavioral data where your team already works.
Fast time to insight: intuitive dashboards mean marketers can find friction, track campaigns, and pull reports without needing a data analyst.

Cons

Learning curve: Fullstory's depth of features takes time to explore fully. For teams that want a faster ramp, Fullstory's Onboarding Specialists can help you get up and running quickly and start seeing value sooner.
Pricing: Advanced plans are built for growth and enterprise teams, so costs scale accordingly. For smaller teams, the free plan includes 30,000 sessions per month and is a low-risk way to see what Fullstory can do.

Pricing

Free: 30,000 sessions per month, ideal for small teams and startups
Business: Advanced features including API access, data export, and priority support
Enterprise: Custom solutions with dedicated customer success management

Visit the Fullstory plans page or contact the team for Business and Enterprise pricing details.

Reviews

One of the things users particularly like is the intuitive user interface. FullStory is designed to be easy to navigate, which means even those without a technical background can quickly get to grips with it. This accessibility is a significant advantage, as it allows teams across an organization to delve into user behavior data without needing extensive training.
- Federico M., G2 Review

2. Google Analytics

Best for: Traffic, audience, and conversion tracking

G2 Rating: 4.5/5

Google Analytics is an industry-standard tool known for its robust web analytics capabilities. This freemium service, offered by Google, helps you analyze in-depth details about your website’s visitors. Google Analytics provides information on acquisition, behavior, and conversions, enabling you to understand how your audience interacts with your website.

It tracks key metrics like website traffic, user demographics, bounce rates, and conversion rates. Where it falls short is connecting that campaign data to on-site behavior and the why behind user actions.

Key features:

Traffic and acquisition reporting: see where users come from across organic, paid, social, and direct channels
Conversion tracking: measure goal completions and ecommerce transactions tied to campaigns
Audience segmentation: break down performance by demographics, device, and behavior
Google Ads and Search Console integration for a connected view of paid and organic performance
Customizable dashboards and reports

Pros

Comprehensive and customizable: Google Analytics provides a detailed view of your site performance and user behavior. You can customize your dashboard and reports according to your needs.
Integration: Google Analytics can integrate seamlessly with other Google products like AdWords and Search Console, as well as other platforms, providing a holistic view of your marketing efforts.
Price: Google Analytics offers a free version that provides a wide array of features, which is very beneficial for small businesses or startups.

Cons

Complex Interface: Google Analytics offers a vast range of data, which can be overwhelming for beginners. It may require some learning to navigate and use the tool effectively.
Limited historical data: In the free version, historical data is limited to 26 months.

Pricing

Free: Core analytics features for small to medium-sized businesses
Google Analytics 360: Premium enterprise tier with advanced features, higher data limits, and SLA guarantees, priced on request

Reviews

What I like most about Google Analytics is the depth of insight it provides into website traffic and user behavior. The dashboard is detailed but still manageable once you get familiar with it, and it makes it easy to track visitors, engagement, and overall performance in one place. I also appreciate how well it integrates with other Google tools, and the reports are genuinely useful when it comes to making marketing and business decisions.
- Muhammad O., G2 Review

3. Optimizely One

Best for: Experimentation and personalization

G2 Rating: 4.2/5

Optimizely is a user-friendly tool that lets marketers experiment with and personalize websites and apps to improve customer experience and engagement. Optimizely specializes in A/B testing and multivariate testing, helping businesses test different versions of their website or app to determine what works best for their audience. It allows businesses to experiment across the entire customer journey on a single platform.

Optimizely works best when paired with a behavioral analytics tool. Knowing what to test is half the battle, and session-level data from a tool like Fullstory can surface the friction points worth prioritizing before you start experimenting.

Key features:

A/B and multivariate testing across web and mobile
Personalization by audience segment, traffic source, or behavior
Real-time experiment results to accelerate decision-making
Full customer journey experimentation on a single platform
Integrations with popular marketing and analytics tools

Pros

Ease of use: Optimizely's interface is user-friendly, making it easy to design and implement experiments.
Robust testing: It allows for robust A/B testing, multivariate testing, and personalization, helping businesses improve customer experience and engagement.
Real-time results: Optimizely provides real-time results of tests, allowing businesses to quickly understand what's working and what's not.

Cons

Limited integrations: Compared to other website analytics tools, Optimizely offers fewer integrations, which could limit its effectiveness in a complex marketing stack.
Pricing: Optimizely can be expensive, especially for small businesses.

Pricing

Free: Core experimentation features for startups and small teams
Enterprise: Advanced features, including 1:1 personalization, advanced targeting, and dedicated support, priced on request

Reviews

If you're looking to try new things, I strongly recommend going through Optimizely. It features a powerful data engine, a suite for feature experiments and a lot of targeting and audience options. Also, there have been almost no problems or breaks in service throughout the years.
Further, we have been satisfied with the professional help and tools for such help; the tools at least make work on support and problem tickets easier. If anything, the company might have only done a better job with communication.
- Dianna G., G2 Review

4. Semrush

Best for: Keyword research and competitor analysis

G2 Rating: 4.5/5

Semrush is a comprehensive digital marketing suite with strong capabilities in SEO, PPC, keyword research, competitive analysis, social media, content, and PR. SEMrush assists with your SEO needs by providing solutions for keyword research, site audit, rank tracking, and backlink analysis. It also allows you to analyze your competitors' SEO strategies, providing you with actionable insights to improve your own.

It pairs well with GA4 for teams that want to connect organic and paid acquisition data to on-site traffic performance.

Key features:

Keyword research and rank tracking across organic and paid search
Competitive intelligence: see what keywords and ads competitors are running
Site audit tool to identify technical SEO issues affecting campaign performance
Content analyzer with real-time optimization suggestions
Backlink analysis to support link building and domain authority growth

Pros

Comprehensive SEO tool: SEMrush provides all the tools needed to carry out an effective SEO strategy, including keyword research, backlink analysis, and rank tracking.
Competitive Analysis: SEMrush excels in offering deep insights into your competitors' SEO and SEM strategies, helping you to stay ahead of the competition.
Content Analyzer: SEMrush’s content analyzer provides suggestions for content optimization based on real-time metrics.

Cons

Interface Complexity: There is a learning curve associated with SEMrush due to the vast amount of data and tools available.
Price: SEMrush can be expensive for smaller businesses or individuals.

Pricing

Pro: Core SEO and SEM features for freelancers, startups, and in-house marketers.
Guru: Expanded features for SMBs and growing marketing agencies
Business: Advanced capabilities for agencies, ecommerce projects, and businesses with extensive web presence

Reviews

What I like most about Semrush is that it pulls multiple areas of digital marketing into a single ecosystem without the workflow ever feeling disjointed. Rather than bouncing between separate tools for SEO, keyword research, competitive analysis, content optimization, and reporting, everything is connected in a way that makes it easier to turn strategy into action.
- Poulomi M., G2 Review

5. Ahrefs

Best for: Backlink analysis and rank tracking

G2 Rating: 4.5/5

Ahrefs is another well-regarded SEO tool, offering features to aid keyword research, competitor analysis, backlink checking, content research, rank tracking, and more. Ahrefs is an SEO tool suite designed to help digital marketers get better visibility in search results. It allows you to perform in-depth keyword research, analyze your competitors, track your rankings, and conduct a thorough SEO audit of your website.

Marketers often use it alongside Semrush or as a standalone SEO tool, depending on a team's needs.

Key features:

Keyword research with search volume, difficulty scores, and related keyword suggestions
Backlink analysis with one of the most extensive link databases on the market
Rank tracking to monitor organic campaign performance over time
Competitor content analysis to identify gaps and opportunities
Site audit tool to surface technical SEO issues affecting search visibility

Pros

Extensive backlink database: Ahrefs arguably has one of the most extensive backlink databases, making it an excellent tool for backlink analysis.
Keyword research: Ahrefs provides comprehensive keyword data, including search volume, keyword difficulty, and related keywords.
Intuitive interface: Ahrefs has a user-friendly interface, making it easier for beginners to navigate.

Cons

Limited PPC data: Unlike some competitors, Ahrefs has limited PPC data.
Pricing: Like SEMrush, Ahrefs can also be on the expensive side for small businesses.

Pricing

Lite: Core SEO features for freelancers and startups
Standard: The most popular plan, suitable for most businesses
Advanced: Expanded features and increased limits for growing teams
Agency: Highest tier with maximum limits for agencies managing multiple clients

Reviews

How easy it makes it to actually understand what’s going on with SEO without digging through a million different tools. The keyword research and competitor analysis are super helpful, especially for finding opportunities that are realistic to rank for.
- Ashley A, G2 Review

6. Mixpanel

Best for: Event tracking and user segmentation

G2 Rating: 4.5/5

Mixpanel is a user analytics platform that provides insights into user behavior across websites and through mobile app analytics. Mixpanel offers advanced segmentation and funnel analysis. It allows you to understand how users are interacting with your product, track events, and analyze user behavior across platforms. This can help you to refine your marketing strategies and improve user engagement.

That said, Mixpanel requires manual event tracking setup and doesn't offer session replay, making it better suited for product-led teams than campaign-focused marketers. Teams that need to understand what users do between the click and the conversion will likely need an additional behavioral analytics tool.

Key features:

Event-based tracking to monitor user actions across web and mobile
Funnel analysis to identify where users drop off in key flows
User segmentation by campaign source, behavior, and demographics
Retention analysis to measure long-term engagement by acquisition channel
Real-time data to track changes in user behavior as they happen

Pros

Detailed user analytics: Mixpanel provides a granular view of user behavior, allowing you to analyze individual users or segments.
Funnel analysis: Mixpanel's robust funnel analysis feature helps you to identify where users are dropping off in the conversion process.
Real-time data: Mixpanel provides real-time data, allowing you to instantly track changes in user behavior.

Cons

Complexity: Mixpanel offers a plethora of features, which can make the platform somewhat complex to navigate for beginners.
Limited integrations: Mixpanel offers fewer integrations than some other top marketing analytics platforms.

Pricing

Free: Limited features for small teams getting started
Growth: Full platform access, priced based on data points used
Enterprise: Advanced features and custom limits, priced based on data points used

Reviews

I use Mixpanel a lot at Typedesk. It offers a lot of functionality, and I can quickly move through funnels and user flows to understand what people are actually doing. The integrations are solid, and performance has been consistently good for us. I’ve never run into issues with speed or with things breaking.
- Diana H., G2 Review

7. Heap Analytics

Best for: Retroactive event tracking without manual setup

G2 Rating: 4.4/5

Heap Analytics is an analytics platform that captures and measures user actions on your website or app, enabling you to better understand your users' journey. Heap’s defining feature is its ability to automatically capture all user interactions, including clicks, submissions, transactions, and more.

This automated data collection eliminates the need for manual tracking setup and allows for retroactive analysis of data.

Heap was acquired by Contentsquare in 2023. Teams evaluating Heap today should factor in how the acquisition may affect the product roadmap, support, and pricing over time.

Key features:

Automatic event capture with no manual tracking setup required
Retroactive analysis: apply filters and answer new questions using historical data
Funnel analysis to identify drop-off points across key user journeys
User segmentation by campaign source, behavior, and acquisition channel
Integrations with popular marketing and analytics tools

Pros

Retroactive data: Heap allows you to apply filters and analyze past data
User-friendly: Heap's user interface is clean

Cons

Limited customizability: Heap offers less customizability than some other analytics tools.
Pricing: Heap does not publicly disclose its pricing, which can make it difficult for potential users to evaluate if it's the right tool for their budget.

Pricing

Free: basic features for startups and small businesses
Business: advanced features for growing teams, priced on request
Enterprise: fully customizable solutions, priced on request

Reviews

Heap’s automatic event tracking is its strongest feature. It significantly reduces the need for manual instrumentation, which saves time for product and design teams. The interface is intuitive, and building funnels, retention reports, and user journey analyses is straightforward even without heavy engineering support. Heap is particularly useful for understanding real user behavior across web applications.
- Deep G., G2 Review

8. Domo

Best for: Cross-functional data visualization

G2 Rating: 4.3/5

Domo is a robust business intelligence (BI) platform that integrates with multiple data sources to provide real-time data visualization and analysis. Domo stands out by offering cloud-based, mobile-first BI that connects people, data, and systems. It helps businesses make faster, better-informed decisions by bringing together data from across the organization and turning it into actionable insights.

It is worth noting that Domo is a visualization layer, not a standalone marketing analytics tool. It works best when paired with channel-specific tools that feed it the underlying data.

Key features:

Pre-built integrations with hundreds of data sources, including ad platforms, CRMs, and analytics tools

Real-time dashboards for cross-channel campaign performance reporting
Drag-and-drop interface for building and customizing reports without technical support
Mobile-first design for leadership reporting on the go
Collaboration features to share insights and align teams around campaign data

Pros

Integration capabilities: Domo integrates with a wide range of data sources, providing a comprehensive view of your business data.
Real-time data: Domo updates data in real time, enabling businesses to make decisions based on the most current data.
User-friendly: Domo’s intuitive interface and drag-and-drop feature make it easy for users to create and customize dashboards.

Cons

Cost: Domo can be pricey, especially for small businesses.
Learning curve: While Domo's interface is user-friendly, the platform's vast capabilities can be overwhelming for new users.

Pricing

Free trial: available to get started
Professional: full platform access, priced on request
Enterprise: advanced features and custom solutions, priced on request

Reviews

I find Domo super easy to use, especially for nontechnical people. It's really straightforward to learn the analytics side, which is great since the people creating visualizations in my team don't typically have a technical background. Using Domo's no-code platform makes it easy to train up the team, which is awesome for filtering and changing date ranges on cards. It's also been easy to train our salespeople on how to do what they need to do on the cards.
- Caitlin R., G2 Review

9. Funnel.io

Best for: Centralizing data from 500+ ad platforms

G2 Rating: 4.5/5

Funnel.io is a marketing analytics tool designed to collect, transform, and visualize data from all your marketing and advertising platforms. Funnel.io automates data collection and harmonization, enabling you to spend more time on analysis and less on data management.

It allows businesses to view their entire marketing and advertising data in one place, facilitating more efficient reporting and analysis.

Funnel.io is strong for data collection and aggregation, but does not offer the behavioral insights or on-site analysis that most marketing teams also need. It works best as the data layer that feeds your reporting tools, not as a standalone analytics solution.

Key features:

Pre-built integrations with over 500 marketing and advertising data sources
Automated data collection and harmonization across channels
Customizable dashboards and reports for cross-channel performance visibility
Data transformation to standardize metrics across platforms
Flexible data destinations, including BI tools, data warehouses, and spreadsheets

Pros

Automated data collection: Funnel.io automatically collects and harmonizes data from all your marketing intelligence platforms, saving time on data collection and preparation.
Pre-built integrations: Funnel.io offers pre-built integrations with over 500 data sources, providing a comprehensive view of your marketing data.
Customizable dashboards: Funnel.io lets you create custom dashboards and reports, so you can visualize your data however you want.

Cons

Limited advanced analysis: Funnel.io focuses more on data collection and visualization, offering fewer advanced analysis tools.
Pricing: Funnel.io may be expensive for smaller businesses.

Pricing

Starter: core features for smaller businesses with a limited number of data sources and users
Professional: expanded data sources, users, and update frequencies for medium-sized businesses
Enterprise: highest limits and custom solutions for larger businesses, priced on request

Reviews

Funnel makes it very easy to connect all our relevant marketing and analytics data sources in one place and keep them synced automatically. I really like how the connected fields work – once the mappings are set up, building [visualizations] and reports is straightforward and consistent across channels. It saves us a lot of manual work and gives us a much clearer, unified view of performance.
- Muhammad Abdullah S., G2 Review

10. BuzzSumo

Best for: Content discovery and competitor tracking

G2 Rating: 4.5/5

BuzzSumo is a powerful online tool that lets users find popular content by topic or on any website. BuzzSumo helps discover the most-shared links and key influencers for any topic. It's a vital tool for content marketing and for the performance of SEO campaigns.

Its social search engine surfaces the most shared content for any topic, author, or domain, giving marketers a data-backed starting point for content strategy and SEO campaign planning.

Key features:

Content discovery: find the most shared content by topic, domain, or author
Competitor content tracking: monitor what is performing well for competing brands
Influencer identification: find key voices in your industry for content amplification
Trending topic alerts to stay ahead of content opportunities
Content analysis by engagement, shares, and backlinks across social platforms

Pros

Content discovery: BuzzSumo helps you discover the most popular content in your industry, providing valuable insights for your content strategy.
Influencer identification: With BuzzSumo, you can identify key influencers in your industry who can help amplify your content.
Competitor analysis: You can track what content is performing well for your competitors, helping you to benchmark and strategize your content effectively.

Cons

Limited data: BuzzSumo does not provide as extensive data as some other tools.
Price: BuzzSumo can be expensive for small businesses or individuals.

Pricing

Pro: core features for small marketing teams and growing businesses
Plus: expanded features for medium-sized businesses
Large: advanced capabilities for big agencies and brands
Enterprise: custom solutions for teams with complex needs, priced on request

Reviews

BuzzSumo makes it incredibly easy to discover high-performing content across any topic or industry. I love how I can instantly see which articles are getting the most engagement across social platforms like Facebook, LinkedIn, and X (Twitter). The Content Analyzer and Trending Feeds are my favorite features — they save me hours of research and help me plan content that’s more likely to succeed. It also helps identify top influencers and competitors’ best-performing content, which is a huge bonus.
- Verified G2 Review

11. Sprout Social

Best for: Social scheduling and reporting

G2 Rating: 4.4/5

Sprout Social is a social media management platform that helps marketing teams grow their social presence and measure the impact of their efforts. It consolidates scheduling, monitoring, and performance reporting into one place.

That makes it easier to track social campaign ROI, measure engagement across channels, and report social performance to leadership without having to jump between platforms.

Key features:

Smart Inbox consolidates messages and mentions from all social channels into one unified view
Scheduling and publishing tools span multiple social accounts and platforms
Social analytics and reporting tracks campaign performance and audience engagement
Competitor analysis benchmarks social performance against industry peers
Team collaboration tools manage approvals, assignments, and workflows

Pros

Comprehensive social media management: Sprout Social offers a suite of tools for publishing, analytics (Facebook, Twitter, Instagram, etc.), engagement, and team collaboration.
Clean, intuitive interface: Sprout Social's interface is user-friendly, making it easy for businesses to manage their social media presence.
In-depth reporting: Sprout Social offers robust reporting capabilities that help businesses understand their social media performance.

Cons

Limited integration: Sprout Social does not integrate with as many platforms as some other social media management tools.
Cost: Sprout Social can be expensive for small businesses.

Pricing

Standard: core social media management features for smaller teams
Professional: expanded features for growing marketing teams
Advanced: full platform access for larger teams with more complex needs

Reviews

The feature that reports on what our competitors are doing on social media is particularly impressive, as well as the stats for our own posts at a glance. It's nice to know if we're beating our competitors on social media, what kinds of content we're posting, and what might be working for our competitors versus us. Additionally, connecting accounts was pretty easy, and the support team was extremely helpful and pleasant to work with.
- Robert H M., G2 Review

12. HubSpot Marketing Hub

Best for: Full-funnel campaign and CRM attribution

G2 Rating: 4.4/5

HubSpot Marketing Hub is a full-funnel marketing platform that gives marketers a clear line between campaign activity and closed revenue. It covers the attribution and CRM layer, giving marketers visibility into which campaigns generate leads, how they move through the funnel, and which touchpoints drive closed revenue.

Where HubSpot has limits is on the behavioral side. It tracks form fills, email opens, and campaign conversions, but does not show what users actually do on your site between the click and the conversion. Teams that need that depth benefit from pairing HubSpot with a behavioral analytics tool like Fullstory.

Key features:

Multi-touch attribution reporting to connect campaigns to pipeline and revenue
Email marketing and automation are tied directly to CRM data
Landing page and ad campaign management in one platform
Lead tracking and scoring across the full customer journey
Native CRM integration for a connected view of marketing and sales performance

Pros

Direct pipeline visibility: connects campaign performance to leads, deals, and closed revenue without leaving the platform
All-in-one reporting: built-in attribution and CRM data reduces time spent pulling reports across tools
Marketing automation: workflows and sequences free up time for strategy over manual campaign management

Cons

Pricing: costs scale quickly as your contact list and feature needs grow, making it expensive for smaller teams
Limited behavioral insights: HubSpot shows what converted, but not why users hesitated or dropped off before converting

Pricing

Free: basic CRM and marketing tools for small teams
Starter: core marketing features starting at $15 per month
Professional: advanced automation and reporting starting at $800 per month
Enterprise: full platform access with custom features, priced on request

Reviews

HubSpot seems built on the idea that, to make marketing effective, the tools you use to manage programs should be as simple as possible. When I started using HubSpot 15 years ago, it was just as simple as it is today. That simplicity of getting onboarded to the platform and building campaigns has only improved with the addition of perfectly timed tooltips (like those that help you optimize email copy for more clicks).
The new AI tools that help you learn new features faster are great, too, because they're not too pushy. It's a platform that invites you to improve as you get comfortable, and you'll see that your campaigns perform better the more comfortable you are with the toolset.
- Matt I., G2 Review

13. Triple Whale

Best for: ecommerce attribution and ROAS tracking

G2 Rating: 4.5/5

Triple Whale is an ecommerce analytics platform built specifically for DTC and ecommerce brands running paid social and search campaigns. It centralizes attribution, ROAS tracking, and blended CAC reporting in one place. That gives marketing teams a clear view of which channels and campaigns are actually driving revenue, not just clicks.

For teams running personalized ecommerce campaigns, Triple Whale fills the attribution gap that GA4 alone cannot cover. It pairs well with GA4 for traffic analysis and Fullstory for understanding what users do on the site after they click through from a paid campaign.

Key features:

Multi-touch attribution across paid social and search channels
ROAS and blended CAC reporting in real time
Creative analytics to measure ad performance by creative asset
Pixel-based tracking to fill gaps left by iOS privacy changes
Integrations with Shopify, Meta, Google, TikTok, and other ecommerce and ad platforms

Pros

Ecommerce-built attribution: ROAS and CAC reporting designed around the way DTC brands actually run campaigns
Creative analytics: Gives media buyers visibility into which ad assets are driving conversions
iOS attribution: Fills the tracking gap that leaves many ecommerce teams flying blind on paid social

Cons

Limited use case: Built for ecommerce, so less relevant and harder to justify for B2B or non-ecommerce marketers
Limited behavioral insights: Shows which campaigns drive revenue, but not what users do on site after they arrive

Pricing

Free: core tracking, AI visibility, and blended ROAS for founders and small teams
Starter: multi-touch attribution and Moby AI analysis starting at $149 per month
Advanced: full platform including creative analytics, cohort analysis, and SQL editor starting at $219 per month
Add-ons: conversion tracking, retention, and marketing mix modeling available separately, priced on request

Reviews

Triple Whale is our source of truth for marketing data. The integrations are solid, and we trust the data because we’ve verified its accuracy ourselves. It’s helpful to have everything centralized in one place, especially since we manage all our advertising in-house across multiple channels. The ability to analyze campaigns and get clear insights is a big plus.
I also like that the tool can notify us when specific thresholds are hit, which saves us from having to constantly monitor everything manually. With a small team, this kind of automation is really valuable.
- Verified G2 Review

14. Hotjar

Best for: Heatmaps and lightweight UX feedback

G2 Rating: 4.3/5

Hotjar is a lightweight website analytics tool that gives small marketing teams a starting point for understanding user behavior on their site. It offers heatmaps, session recordings, and basic feedback tools that make it easy to spot obvious friction points without a complex setup.

That said, Hotjar has a clear ceiling. As teams grow, the lack of depth, scale, and marketing stack integration makes it harder to connect campaign performance to on-site behavior and revenue. Teams hitting those limits will find Fullstory a natural next step.

Key features:

Heatmaps to visualize where users click, scroll, and engage on key pages
Session recordings to watch individual user journeys on your site
Feedback surveys and polls to collect qualitative input from users
Funnel analysis to identify where users drop off in key flows
Form analytics to see where users abandon forms before completing them

Pros

Easy setup: Heatmaps and session recordings are live within minutes, no engineering support required
Accessible pricing: A generous free plan makes it a low-risk starting point for small teams
Visual insights: Heatmaps and click maps give marketers a quick read on how users engage with key pages

Cons

Limited scale: Data sampling and session limits make it less reliable as traffic and team needs grow
Shallow integrations: Does not connect deeply enough with marketing stacks to tie on-site behavior to campaign performance
No behavioral depth: Useful for spotting obvious friction but lacks the insight needed to connect user behavior to revenue

Pricing

Free: core features for small teams, available via Contentsquare's free tier
Paid plans: priced on request through Contentsquare, contact the team for details

Reviews

Hotjar’s heatmaps and session recordings provide clear, visual insights into user behavior, making it easy to spot UX and UI issues and use those findings to improve conversions.
- Kamala B., G2 Review

Types of marketing analytics tools

There are several types of marketing analytics tools, each focused on a different part of your marketing funnel. Understanding the differences is key to building a stack that answers every question your team has.

Type	What it does	Best tool
Web analytics	Tracks page views, session duration, bounce rate, and traffic sources, but stops short of explaining why users leave without converting	Google Analytics
Behavioral analytics	Tracks interactions, hesitations, and drop-off points to connect campaign performance to on-site conversion	Fullstory
Social media analytics	Measures engagement, audience growth, and which content drives traffic back to your site	Sprout Social
SEO and SEM analytics	Improves search visibility and measures organic and paid performance through keyword research, rank tracking, and competitor intelligence	Semrush, Ahrefs
Content analytics	Measures how content performs across channels, tracking views, shares, and conversions to help teams focus future campaign efforts	BuzzSumo
Marketing attribution	Connects campaign spend to revenue by tracking which channels and touchpoints drive conversions	HubSpot, Triple Whale

How to build a marketing analytics stack

The best marketing stacks connect every layer, taking you from campaign spend to revenue with no gaps. Here is how the three core layers work together:

Traffic and acquisition: GA4 shows you where users come from and how campaigns perform across channels. This is your foundation.
On-site conversion: Fullstory sits between your acquisition tools and your CRM, showing which campaigns drive users who actually convert and where the ones who do not get stuck.
Attribution and channel performance: HubSpot Marketing Hub and Triple Whale close the loop, connecting spend to pipeline and closed revenue.

Adding GA4 and Fullstory covers the essentials for most teams. For enterprise teams, an attribution layer closes the loop from first click to closed revenue.

Why marketers choose Fullstory

Fullstory fills the gap between campaign data and on-site conversion, showing you not just how many people showed up, but why they left and which campaigns actually drove results.

Fullcapture automatically indexes every user interaction without manual tagging, so you get the data you need without waiting on engineering.

StoryAI surfaces friction points, drop-off patterns, and campaign-level behavior, so you spend less time digging and more time acting.

That is how you stop guessing and start showing leadership exactly what is working and why.

See Fullstory MCP in action

Angela Sze — Wed, 17 Jun 2026 06:00:00 GMT

When we launched Fullstory MCP in late March, we were thrilled by the response. Thousands of builders across companies of all sizes have now joined our beta program. Teams are wiring Fullstory behavioral context directly into their development workflows, support pipelines, and AI agents. The video below walks through two of the use cases we’re seeing most.

→ MCP was one of three products we announced at our Summer Release Event. See what else is new.

Learning from our customers

The beta has surfaced a few clear patterns in how teams are putting MCP to work. See the prompts that are working for our beta users in the MCP prompt library.

Proactively detect and fix issues: Set up an automated process to monitor for broken user flows and deliver rich, contextual insights directly where your teams work. Automatically attach session context to Engineering and Support tickets to accelerate resolution.

Zac Sheffer, CEO of The Last Unicorn, did this. Every time a ticket is created in Linear, an automation pulls the associated Fullstory session, attaches it to the ticket, and generates a proposed fix, including a Playwright script built from synthetic data that mirrors the user’s actual experience. Engineers pick up tickets with the reproduction steps already done.

Release monitoring without manual digging: After a deploy, you shouldn’t have to manually trace whether a new issue was introduced. With MCP, you can ask it to check for regressions across a release window. It discovers escalating problems, pulls aggregated stats on who was affected and on which devices, and combines that with actual session data to give you a synthesized diagnostic, including a recommended priority for action.

One knowledge base, no more silos: Unify your digital experience insights so your entire team can get answers with natural language, without needing to be trained on every tool. Richard Coombes, VP of Technology at Equals Group, described using MCP alongside CloudWatch logs and Datadog to detect issues before customers report them. For a payments company where a delayed resolution can mean a missed salary or a supplier that doesn’t get paid on time, that speed matters.

Agentic Session Review

We recently announced Agentic Session Review. Normally, even if you’ve identified a trend, say a spike in checkout errors, you still have to watch sessions manually to understand what’s actually happening. Agentic Session Review removes that step. Prompt an AI agent to review a batch of sessions on your behalf. It analyzes DOM events, network calls, and user interactions, then returns a synthesized summary directly to your command line.

To avoid burning through your context window or distorting the AI’s reasoning, it uses a zoom-switch-layer model. The agent starts with a high-level session map, zooms in on specific timestamps to view actual pixels and the page’s accessibility tree, and can pull in network traffic and console logs at the exact moment an issue occurred.

Agentic Session Review is now in early access via Fullstory MCP.

Developer documentation is now live

Developer documentation is now live for all paid Fullstory customers. MCP is also open to self-serve onboarding: anyone with a paid Fullstory account can join the beta and get started today.

The agentic AI market has a verification problem

Joel Webber — Tue, 26 May 2026 06:00:00 GMT

On June 16, we hosted our summer release event where we showed what verifiable, autonomous agents look like in practice—watch on demand.

Most “agentic AI” being sold right now is a co-pilot in a costume.

I don’t say that to be cynical. I say it because the word “agent” is being applied to so many different things this year that it’s stopped meaning anything, and that makes it hard to evaluate what you’re actually buying.

Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. The technology isn’t the issue. The issue is that teams committed to “agentic” before anyone agreed on what the word required, and discovered eighteen months later that what they bought couldn’t carry the weight of the job.

So before you write another line of your AI roadmap, I want to offer the test I run on every vendor.

A two-part test for whether something is actually an agent

Ask the vendor: Can the AI finish the job without me in the loop, and can I verify what it did when I check? If the answer to either half is no, it’s not an agent. It’s a faster co-pilot. Co-pilots are useful, but they aren’t the category shift the marketing implies, and they aren’t going to move your ROI conversation past “we’re saving some time.”

The reason the test is two parts and not one is that almost everything being sold this year passes the first half and fails the second. The second half is where the value lives.

The “without me in the loop” half

This is the one most products fail outright. A lot of what’s being sold as agentic still requires the human to ask the right question, interpret the answer, and decide what to do next. The AI is doing more of the work than it used to, but it isn’t doing the job.

A real agent owns an outcome, not a task. That distinction matters. A task is “summarize this session.” An outcome is “tell me which of my A/B tests are losing and why.” The first one ends when the AI produces a paragraph. The second one ends when a human running an experimentation program has the answer they actually needed, derived from evidence and ready to act on.

The agents that are starting to earn their keep this year own the second kind. They watch the A/B test and tell you why the variant is losing—not just what happened. They monitor site health between releases and flag the regression before your support queue notices. They handle the investigative work that used to eat an analyst’s Tuesday, start to finish, while the analyst is in other meetings doing higher-leverage work.

If the human is still the one finishing the thought, you don’t have an agent yet. You have a smarter assistant, which is useful but not transformative, and not going to change your ROI math.

The verification half is the one that matters more

This is the part almost nobody is talking about, and it determines whether any of this works at scale.

When you hand a job to a teammate, you’re trusting them to make a call without you there. The reason that works in human teams is that you can ask them later what they saw, what they did, and why. They can show you. The work is auditable, and the auditability is what makes the trust possible. New employees don’t get autonomy on day one. They earn it by showing their work until the work earns the room to operate.

An autonomous agent that can’t show its work isn’t a teammate. It’s a black box that occasionally produces an answer. You can’t put that on the org chart. You can’t put it in front of a regulator. You can’t put it anywhere the cost of being wrong is real—and in any business worth automating, the cost of being wrong is always real.

This is where most agent deployments quietly stall. They work in the demo. The first time something goes sideways in production and someone asks what the agent did and why, the team can produce a confidence score and not much else. That’s the moment leadership pulls back the autonomy, and the project’s value collapses.

The question to push harder on

Not “can it do the thing.” Can it show me what it saw, what it decided, and why, at a level of detail I can actually check?

If the answer is a confidence score and a summary, that isn’t verification.

Real verification means the agent’s work is traceable to ground-truth evidence. For an agent operating on user behavior, every action it takes should be grounded in something concrete: the session it analyzed, the click that triggered the flag, the moment a user got stuck and dropped off. Not aggregated metrics or inferred patterns. The raw record of what happened, that you can replay and check.

This separates agents you can deploy at scale from agents you can only demo. A lot of products on the market today don’t clear that bar, because they were built on top of analytics infrastructure that throws away most of the underlying behavioral evidence in favor of pre-defined events. You can’t verify what the data doesn’t contain.

What this looks like in practice

A few examples of jobs that pass the test.

An A/B test evaluator that not only tells you the variant is underperforming, but shows you the specific user sessions where the friction occurred, so you can watch what went wrong instead of reading a summary.

A site health agent that doesn’t just alert on a metric anomaly, but identifies the deploy that caused it and links you to the broken user journeys, with the relevant sessions queued up and ready to review.

A release monitor that watches new feature adoption around the clock and surfaces the moments where users hit confusion, with replay evidence, so the product team can make the fix on Monday instead of arguing about whether the issue is real.

In each case, the agent owns the outcome rather than the task. And in each, the work is traceable back to something a human can verify in seconds. Without both halves, you don’t have an agent you can trust with autonomy.

If you’re evaluating AI this year, the question isn’t whether the demo looks impressive. The question is whether you’d let it run on Tuesday morning without watching, and whether you’d be able to defend what it did on Tuesday afternoon.

Get caught up on our summer release event.

AI ROI: Spend is growing faster than most teams’ ability to justify it

Jason Wolf — Tue, 19 May 2026 06:00:00 GMT

On June 16, we hosted our summer release event where we showed exactly how we’re helping teams close this gap—watch on demand.

Every company has a version of this meeting. Someone (likely in finance) pulls up the slide with the AI tools budget and asks the question nobody has a clean answer to: what are we actually getting for this?

It’s not a hostile question, but it can feel like one. Enterprise teams have spent the last two years deploying AI tools, automation platforms, and agent workflows at a pace that would have seemed impossible a handful of years ago. And yet when it’s time to report on outcomes, most leaders are working from inference rather than evidence.

Return on AI—AI ROI, ROAI, however your team is referring to it—are the terms of the moment for this problem. But the concept outpaces the practice, because most teams can tell you what they spent but only a fraction can tell you what changed because of it.

The measurement gap is structural, not a reporting failure

When companies struggle to show ROAI, the instinct is to point at the analytics stack, get better dashboards, or run more reports. But the problem usually isn’t the reporting layer. The problem is that the underlying data doesn’t capture what actually happened to produce the reports.

AI tools don’t operate in a vacuum. They’re embedded inside human workflows: a support agent who uses an AI-suggested response, an automation that routes a ticket before a person ever touches it, a copilot that surfaces a knowledge article mid-conversation. The AI acts, but the action lives inside a process that spans multiple systems, multiple people, and often no clean audit trail.

The result is a process you can’t see, measuring an outcome you can’t trace.

What good measurement actually needs

To understand whether an investment in AI is working, you need a clear view of the full process around it — what happens before the AI acts, what happens after, and how outcomes shift depending on whether and how it was used.

For support and CX teams, that maps to questions like:

Did average handle time actually drop after you deployed that agent-assist tool, or did it drop for some ticket types while quietly climbing for others?
Are your top-performing support agents using the AI differently than the rest of the team, and if so, what does that tell you about how you should be training people?
When a ticket escalates despite AI involvement, where in the process did things go sideways?

None of those questions can be answered with just AI output logs. You need a clear view of the workflow the AI is part of, the full path a ticket takes from open to close, which systems it moves through, and where time is actually being lost or saved.

A framework for thinking about ROAI

ROAI measurement works when it connects to outcomes that were already meaningful before AI entered the picture. The mistake most teams make is building new AI-specific metrics that don’t map back to the business numbers leadership is already managing.

A more grounded approach starts with a metric that predates the AI deployment: average handle time, time to resolution, escalation rate, first-contact resolution. Something already on the scorecard, already owned by someone accountable for it. From there, you need a ground-truth view of the process that metric lives in. Not a survey of how agents say they work, and not a sample of tickets. A complete picture of how work actually moves across your systems. Then you measure the delta, what changed when AI was introduced, where the gains are real, and where friction just moved somewhere less visible.

The hard part is the middle step. Ground-truth process visibility is what most teams are missing, and it’s why ROAI conversations so often stall at “we think it’s working.”

Why this is harder than it sounds for support teams

Support and CX operations are some of the most process-dense environments in any enterprise. A single ticket can move through a CRM, a knowledge base, an AI assist tool, a quality review system, and an internal messaging platform, sometimes within a single interaction. Support agents routinely switch between six or more applications in the course of resolving one issue.

Traditional process mining tools weren’t designed for this. They need instrumentation on every application you want to track, they have no view into what happens in the browser, and they can’t follow a workflow that crosses systems the way a modern support agent’s day actually does. So process maps look clean on paper while the actual work looks nothing like them, and without better data, there’s no reliable way to square the two.

Getting to an answer your CFO will believe

A credible AI ROI story doesn’t need perfect data, but traceable data, a clear line from the AI investment to a process change to an outcome metric, with enough visibility into the middle that the story holds up when someone starts asking follow-up questions.

That’s what finance and leadership are really asking for when the budget slide goes up. Not a guarantee, not a model built on layered assumptions. A before-and-after view of how work gets done, with evidence that the investment moved something that was already worth measuring.

The teams getting there aren’t necessarily the ones with the most sophisticated AI deployments. They tend to be the ones that solved for process visibility upfront rather than as an afterthought. Get caught up on our summer release event.

Intelligent digital experiences: Why product teams should care

Fullstory — Fri, 12 Jun 2026 11:00:00 GMT

Your funnel shows a 60% drop-off at step three, and the dashboard confirms what everyone can already sense: something in the experience is creating friction and changing user behavior. That should give your team a clear starting point. Instead, a messy investigation ensues.

Product reviews the journey, design checks the flow, engineering asks for reproduction steps, support looks for related tickets, and analytics slices the funnel again to see whether a different segment explains the drop. It’s an all-hands-on-deck effort, but no one can get ot the root of the problem. You know where users left, but you don’t know what they experienced right before they made that decision. You don't have a data shortage; you have a context one.

You can measure conversion, retention, activation, abandonment, and churn with impressive precision, but the user effort inside those metrics stays out of reach. A dashboard can tell you that checkout completion fell. On its own, it can’t show that users clicked a promo-code field, got no useful feedback, tried again, scrolled for help, and abandoned once the flow started feeling shaky.

An intelligent digital experience closes the distance between behavioral signal and product response. It helps your team move beyond noticing something changed and toward understanding why it’s happening and acting before they face another support queue full of preventable frustration.

What intelligent digital experience actually means for product teams

An intelligent digital experience is a product journey that adapts to user behavior. It’s not the CMS meaning of digital experience: page delivery, content orchestration, or channel management. It’s the experience your user has with your product at every step of their journey.

Intelligent experiences start with the path users actually take through your product, not the one your team hoped they’d take. Sign-up, onboarding, search, checkout, upgrade, renewal, authentication, support, and recovery all belong to the digital experience, but many revealing moments happen between the milestones your dashboards already track.

Those small moments are where the designed journey and the lived journey start to split. Your team may see a clean sequence of steps, but the user experiences hesitation, repeated attempts, and growing doubt until it feels easier just to leave.

A few behavioral signals show where users are struggling with the product:

Behavioral signal	What it tells you
Rage click	The user expects a response, gets nothing useful, and repeats the same action because the product feels unreliable.
Dead click	The interface appears to invite action, but the click does nothing.
Session path	The user’s actual route through the product, including detours, loops, and backtracking, shows where the intended journey breaks down.
Cohort drift	A group of users starts behaving differently over time, often signaling that something in the experience has changed, weakened, or stopped providing value.
Funnel fall-off	Users leave at a specific step, giving your team a clear place to investigate, but not enough context to diagnose the problem on its own.
Frustration signal	Hesitation, repeated attempts, backtracking, stalled progress, and visible confusion start forming a pattern.

Forrester’s exploration of The Future Of Digital Experiences describes emerging digital experiences as more assistive, anticipatory, and agentic, with consumer context helping organizations respond to needs more proactively. That's the distinction worth keeping. Intelligent digital experiences improve because real behavior shapes the next version of the journey, removing friction before users have to fight through it.

Why most analytics stacks only see half the picture

Most analytics stacks are built to report outcomes. They can tell you what changed, but may not be able to show what users experienced before the metric moved. Event-based analytics is the clearest example because it only captures what you set up to track.

When a team plans tracking around clicks, submits, and page views, it can miss the hesitation before a form submission, retries after weak feedback, or confusion caused by vague copy. The click still gets tracked, and the dashboard still looks orderly, even though your analytics setup never captured what actually went wrong.

Cohort analysis has the same limitation at a higher level. It can show that users churned in week three, but it can’t show what happened in their sessions during that period. As a result, the numbers live in one place while the reasons behind the numbers live somewhere else, forcing the team to stitch the story together later.

Funnel analysis without behavioral context creates the sharpest trap because it gives you a precise location without a usable explanation. You can see the exact step where users leave a defined path, but the data doesn’t tell you what they tried, what confused them, or what failed to respond. Teams then fill the gap with hypotheses, and A/B tests end up validating guesses that session-level evidence could have narrowed much earlier.

Callout: UserGuiding’s roundup reports that 88% of online consumers are less likely to return after a bad user experience, raising the cost of unresolved friction.

What makes a digital experience intelligent?

An intelligent digital experience is the outcome you get when your product team can see real behavior clearly enough to improve the buyer and user journeys users leave.

In reality, that outcome rests on three properties working together.

Session-level behavioral signals give you context instead of a thin layer of tracked events.
AI product analytics can reason over that context, so teams get explanations they can act on rather than summaries they already suspected.
A feedback loop turns insight into a ticket, a prioritization decision, or in-product guidance without needing a specialist to interpret the story.

Here’s how traditional analytics and intelligent digital experiences differ:

Traditional digital analytics	Intelligent digital experience
Tracks selected events	Shows behavioral context across the session
Reports what happened	Helps explain why it happened
Surface friction after the fact	Reveals friction while there is still time to act
Requires translation across teams	Makes insight usable across product, design, support, engineering, and growth
Improves reporting	Improves the user’s path to value

Want to see how Fullstory brings human context into product decisions, from session to insight to action? Start for free →

Behavioral data makes AI worth trusting

AI product analytics can sound decisive on thin event data because the model is reasoning over whatever your tracking setup captured. That can produce polished answers built on partial context, which is how teams end up with confident conclusions that fall apart once someone looks at the actual journey.

Accurate session-level behavioral data changes what AI can credibly explain. Instead of correlating outcomes with a handful of events, you can connect the outcome to the experience that produced it, including hesitation before a form field, dead clicks on elements that never respond, rage clicks that signal rising frustration, and session paths that loop through the same detour before users quit. Human context means seeing what users tried, where they got stuck, and what happened next. That’s what turns AI output into a diagnosis your team can act on.

The difference shows up fast when you’re trying to explain churn. A retention dip can tell you something is wrong, but it can’t tell you what changed in the experience, so the team starts cycling through plausible culprits like pricing, onboarding, or feature adoption. If you can review what the churned cohort actually experienced in their final sessions, the picture becomes clearer. You see the repeatable pattern, you can name the exact friction moment, and prioritization shifts from debate to a clear fix.

Fullstory makes this possible. Fullcapture logs behavioral data at session fidelity, and StoryAI analyzes that data and turns it into actionable takeaways so teams can move from what changed to why it changed without spending days interpreting dashboards.

Practitioner scenario: Activation dips, and the cohort report shows week-three churn climbing. Session paths make the story obvious. The same users hit a dead click during account setup, retry a couple of times, detour into support, and then give up. At that point, you’re not debating theories; you’re writing a ticket around a specific break in the journey and fixing the issue blocking progress.

From insight to action: Closing the loop with AI and integrations

Most product investigations still run like a relay race. Insight gets pulled, written up, reviewed, debated, and eventually turned into a ticket. Each handoff feels reasonable on its own, but the delay created by all those handoffs is where experience improvements lose momentum.

A more intelligent workflow compresses the distance between signal and action in three ways:

Behavioral data stays available continuously, so teams don’t have to wait for a manual investigation cycle to begin.
AI can reason over session-level context, which helps product teams move from a metric change to a specific moment in the journey.
Insight lands where work happens, so action can move through your backlog, support workflows, and in-product guidance instead of sitting in a dashboard.

Fullstory’s Model Context Protocol launch is a useful example because it frames the opportunity around human context rather than AI novelty. Fullstory MCP enables teams, regardless of technical ability or Fullstory knowledge, to ask questions about the human context that makes or breaks digital products. While teams analyze yesterday’s data, customers are still experiencing friction right now.

That’s the shift product teams should care about. The meaningful change isn’t another dashboard with AI attached, but a shorter distance between signal, interpretation, and action.

When behavioral data can flow into the systems your team already uses, intelligent experiences become easier to build:

Product can prioritize with more confidence because the story behind the metric is clearer.
UX teams can see where intent and behavior diverge without guessing.
Engineering can move faster because the reproduction path is grounded in real sessions.
Support can respond with context instead of isolated complaints.

What product teams can do right now

Right now, you can pressure-test your analytics setup with three questions that reveal how quickly your team can move from a metric change to a fix you can stand behind.

When your funnel drops off, can you identify the specific UI element that preceded it without manually reviewing 50 session replays? A funnel shows where users leave a defined path, but it doesn’t show what they tried right before they bailed. If your team can’t connect drop-off to a specific element, state, or interaction, you’re stuck debating theories instead of fixing a breakpoint that’s draining conversion.

When a cohort churns, can you identify what users actually did before they left, not just which feature they stopped using? Churn analysis is harder to act on when the only evidence is a chart. Session-level patterns make it concrete, especially when the same users keep stalling at the same step, looping through the same detour, or abandoning after the same moment of failure.

When AI surfaces an insight, can you tell whether it’s grounded in real session behavior or just event counts? AI only helps when it can point to the experience behind the claim, including the common paths, the representative sessions, and the moments where users got stuck. If the insight can’t be grounded in that context, it’s still a hypothesis, and your backlog is about to absorb another round of debate.

Intelligent experiences are built, not bought

You can’t buy an intelligent digital experience as a finished product. Intelligence lives in the outcomes your users feel, not in the tools you own. Building it takes session-level behavioral data that shows how users actually behaved, AI that can reason over that context, and a workflow that can turn insight into prioritization and action without constant translation.

Teams that pull ahead aren’t always the ones with the biggest redesign or the most tests running. More often, they’re the teams that can spot effort building inside a critical path, understand why it’s happening, and remove the friction before it turns into churn, abandonment, or avoidable support load.

The clean test stays the same. Look at what your behavioral data actually shows, then improve the journey based on that reality.

Ready to start building intelligent digital experiences? Book a demo to see how Fullstory can help.

Fullstory vs. Quantum Metric: A breakdown for enterprise teams

The Fullstory Team — Fri, 14 Nov 2025 11:00:00 GMT

Fullstory turns every digital interaction into insight, going beyond traditional metrics to reveal the full context of user behavior. By giving you trustworthy, retroactive analysis and complete behavioral context in one place, Fullstory reduces manual effort and accelerates decision-making.

You’re here to make an informed decision, and you’re busy. This comparison gets straight to the point: Fullstory is engineered to help enterprise teams get faster answers with less effort, with AI that is deeply integrated to proactively surface business-critical opportunities.

The core difference: Fullcapture™ vs. Manual Tagging

Teams start comparing Fullstory to alternatives when they realize how differently platforms capture data. The biggest divide begins right here. The first and most critical difference between Fullstory and Quantum Metric lies in how they collect data.

Fullcapture: Complete, retroactive data, instantly

Autocapture records interactions automatically and structures them for analysis. Processing occurs server-side, helping sites and apps stay fast and responsive. You can ask a new question tomorrow and instantly look back at yesterday’s data, no waiting—and no re-instrumentation. Beyond clicks and forms, autocapture tracks long-tail behaviors, dynamic content, and even custom errors, indexing every interaction for rapid, reliable analysis.

With Fullstory's Fullcapture, you gain a comprehensive view of user behavior across web and mobile from a single snippet or SDK, ensuring full coverage from day one.

Fullcapture gives you a complete, future-proof dataset from day one. If a new question arises weeks or months later, you can go back and analyze user behavior related to any element on your site—even those you didn’t think were important at the time. When you update your site or release new features, you don’t have to re-instrument anything.

This approach is designed to reduce the need for ongoing tag implementation and management, freeing up engineering teams to focus on other priorities and helping to ensure critical insights aren't missed.

Event-based models: The hidden costs of tagging

Quantum Metric uses configurable, event-based tracking. Platforms that use event-based tracking often require a strategy for tag management to keep data collection aligned with evolving sites and apps. This can sometimes involve third-party services or dedicated internal resources. Frequent updates to custom scripts often require additional review for performance and security consistency.

Time-to-value, cost of ownership, and privacy

For any enterprise, the true cost of a platform isn't just the license fee. It’s the total cost of ownership (TCO), which includes implementation, maintenance, and the resources required to get value from it.

Speed and self-sufficiency

With Fullcapture, teams are empowered from day one. There is no waiting for a services team to implement tags. This self-serve model not only accelerates time to insight but also drastically reduces dependency on external consultants or your own strained engineering resources. The result is a lower, more predictable TCO and a more agile organization.

Fullstory’s server-side data collection is designed to minimize manual script injection, a best practice for maintaining site performance and a consistent security posture.

Privacy and security by design

Fullstory is built on a foundation of Private by Default. The platform offers granular, UI-driven controls to automatically mask sensitive data before it's ever captured. Your team retains full control over their privacy posture without needing to write code or engage a vendor.

Proactive vs. reactive AI-powered insights:

Both platforms leverage AI to help surface insights, but philosophies and capabilities differ significantly.

Fullstory’s StoryAI

StoryAI is deeply integrated into the Fullstory platform and is designed to do the foundational work for you. It transforms behavioral data into clear, actionable insight, and currently offers:

StoryAI Summaries condense multiple sessions to help you understand user behavior patterns without watching every replay. StoryAI goes beyond single-session context to reveal broad user trends and issues across journeys.
StoryAI Opportunities proactively highlights frustration signals, drop-offs, and anomalies in funnels, complete with quantifiable business impact. These insights are embedded directly within Fullstory, eliminating the need for manual steps or external tools to validate their significance.
Ask StoryAI acts as your built-in analyst, proactively surfacing insights and follow-up questions that help you uncover the reason behind user behavior—without starting from scratch. It understands what you’re working on, your goals, your role, and your company context to bring forward the most relevant insights, right when you need them.

Because all behavioral data is captured automatically, StoryAI can surface insights even from interactions you weren’t explicitly tracking, helping teams uncover “unknown unknowns.”

And more capabilities are on the way.

Quantum Metric's FelixAI

Quantum Metric’s Felix AI is a generative AI solution built on Google Cloud's Gemini Pro. Its primary capabilities revolve around session summarization, allowing teams to get quicker context on individual user experiences and ask clarifying questions about a session. Felix AI also helps quantify the business impact of known issues and provides context for Voice of the Customer (VoC) feedback.

Beyond AI-driven insights, Fullstory also allows you to act on what you learn and close the feedback loop with Guides and Surveys. This allows teams to launch in-app surveys to gather qualitative feedback or provide proactive guidance to users, directly within the digital experience.

What both platforms do well

At their core, both Fullstory and Quantum Metric are designed to answer a question that traditional analytics can’t: Why are users doing what they’re doing? They move teams beyond simple metrics like page views and bounce rates to the actual human experience behind the numbers.

Both give teams visibility into digital experiences and help identify opportunities to improve product performance and customer outcomes.

Both offer a strong foundation of session replay, funnels, behavioral cohorts, dashboards, alerts, and integrations that support cross-functional collaboration.

Both tools also provide tools to surface friction points, monitor site health, and quantify the impact of user behavior on key business metrics.

Key questions for enterprise teams to consider

Before investing in user behavior analytics software, ask your team:

How much will ongoing services and maintenance really cost?
What happens when our product or site changes? Will tracking break or will data be lost?
How quickly can we answer new questions?
Will user data be private by default?
Can our non-technical teams use the platform effectively?

The verdict: Choose Fullstory for speed, agility, and a predictable cost of ownership

Choose Fullstory if your organization values speed, data democratization, and predictable ownership costs. Fullstory empowers teams to be self-sufficient and provides a complete, retroactive view of your customer experience.

An alternative like Quantum Metric may be a fit for organizations that prefer a service-led implementation model with dedicated vendor support for ongoing configuration.

Fullstory offers breadth without the busywork: automatic, server-side data collection; retroactive answers; contextual session replay; frustration signal detection (like rage clicks); and AI that turns insights into action. Move faster, reduce tool sprawl, and minimize engineering lift. Our unified analytics ensure high-fidelity experiences across both web and mobile, providing a single source of truth for every interaction.

See the difference for yourself

P.S. Your behavioral data solution should do more than just analytics. See how Fullstory power's real-time personalization and digital employee experience.

Fullstory vs. Mixpanel

The Fullstory Team — Fri, 12 Sep 2025 12:00:00 GMT

You’re looking to learn more and make a decision. You’re also busy. This comparison goes straight to what matters: Fullstory delivers faster answers with less effort than Mixpanel, with AI built for outcomes, not hype.

At a glance: Quick comparison

Fullcapture vs event tags

Fullstory Fullcapture

Fullcapture records interactions automatically and structures them for analysis. Processing runs server-side, which helps sites and apps stay responsive. You can ask a new question tomorrow and look back on yesterday without re-instrumenting or waiting for fresh data. Beyond clicks and forms, Fullcapture sees long-tail behaviors, dynamic content changes, and custom errors, then indexes the data so you get answers to your queries fast.

Mixpanel’s event model

Mixpanel shines when your tracking plan is based on expected outcomes. You define events, engineers implement and test them, and data starts to flow. Answering questions about untracked interactions typically requires instrumenting new events and waiting for data, which can slow decision-making.

What both do well

Both platforms help teams measure product health and find improvement opportunities. You’ll see overlap in funnels, pathing, behavioral cohorts, dashboards, alerts, and integrations. If you only need configurable charts, both deliver.

Fullstory unifies diagnostic and operational data with behavioral data, allowing you to move faster, align teams, and uncover growth opportunities without added engineering overhead.

Built-in AI that speeds decisions

Fullstory includes StoryAI, which turns behavioral data into practical help. Today it offers:

StoryAI Summaries that condense multiple sessions so you can grasp what happened without watching Session Replay first.
StoryAI Opportunities proactively highlights funnel drops and spiking issues with clear context so teams can act faster.

+ More StoryAI capabilities are on the way.

The verdict

Choose Fullstory if you want breadth without the busywork: Fullcapture for automatic, server-side data collection; retroactive answers; context like Session Replay and frustration signals such as Rage Clicks; and agentic AI that helps teams focus on decisions, not dashboards. Move faster with fewer tools and less engineering lift.

Choose Mixpanel for event-based analytics if you have a well-defined tracking plan and engineering resources for custom instrumentation.

Pick your path to proof:

One more thing: Anywhere: Activation and Workforce

Choosing Fullstory also gives you room to grow across your stack. Beyond analytics, you can act in near real time with Anywhere: Activation and improve how teams work with Workforce.

Add what you need now and expand later. It all runs on the same Fullcapture foundation.

About Anywhere: Activation

Anywhere: Activation is a real-time Journey Agent that turns behavioral signals into timely actions. It streams events in near real time, triggers messages or support in the moment, and adds AI summaries for context. Simple connectors make personalization possible without complex pipelines.

About Workforce

Workforce shows how employees actually use their tools daily. It reveals adoption, friction, and shadow IT across apps so leaders can streamline workflows, right-size licenses, and measure ROI. Teams use these insights to improve digital employee experience and accelerate AI adoption.

Building systems that proactively catch and resolve product issues

Addison Price — Mon, 08 Jun 2026 06:00:00 GMT

When you're releasing a new product, website, or mobile app design, there is one absolute truth: it won’t be perfect. Digital products are a reflection of human creativity and effort, and with that comes the possibility of room for growth or improvement. Even as we look toward a future where AI might build everything for us, we must remember that AI itself was built by humans, too.

Since imperfection is inevitable, you need a system in place to catch issues when they inevitably arise, understand why they're happening, and resolve them before they cost you customers. But before we get to what that system looks like, it helps to understand how products are designed and where traditional testing falls short.

Product design is a hypothesis, not a guarantee

At its core, product design is focused on aligning business objectives with hypotheses about how users will interact with a product to achieve those objectives. But that alignment is difficult to achieve.

Every user experience across websites and mobile apps is made up of micro-funnels designed to help users complete specific tasks, like buying a product or signing up for a service. SaaS applications are particularly complex in this regard, as they serve more "jobs to be done" than other industries, resulting in significantly more micro-funnels to design and maintain. Predicting user behavior is inherently difficult, and that challenge compounds over time as behavior evolves. As a result, designing a perfect experience that never confuses or frustrates users is practically impossible. This is the root of a lot of churn; frustrated users quickly become ex-users.

The limits of planned testing

To try and get ahead of churn caused by imperfect experiences, teams test.

Most product teams rely on Synthetic User Monitoring (SUM) to test their applications before launch. SUM simulates ideal user journeys to catch issues before real customers encounter them, but it has a fundamental limitation: it can only test the scenarios you planned for. And in complex SaaS applications with countless possible routes, it is nearly impossible to plan for every scenario.

Real User Monitoring (RUM) is where the rubber meets the road, as the product finally lands in the hands of human customers. But humans are unpredictable; even your most optimized happy paths aren't guaranteed to be the ones users take to complete tasks. Instead, "cowpaths" form. Like a worn path in the grass that creates shortcuts between sections of sidewalks, but within digital products. RUM helps teams discover the user behaviors and navigation routes that weren’t necessarily accounted for in the original design or synthetic testing.

Four requirements of a proactive system

SUM and RUM each play a role in understanding your product experience, but neither alone nor both together is enough to catch, diagnose, and fix issues before they cost you customers. To catch and resolve product issues before they impact your bottom line, you need a system built on four key components.

1. Complete data capture

To truly capture human behavior, a system must record everything by default—every action on every element in every corner of your product, including everything happening behind the scenes. A system that only captures the most common element types or basic friction heuristics simply won't cut it. Catching every friction point requires an all-or-nothing approach to data.

2. Automated issue detection

Raw data alone doesn't tell you where to look. You need intelligence layered on top—something that can identify spiking issues, surface their root causes, and quantify their business impact without requiring a team of analysts to dig through the data manually.

3. Fast, flexible remediation

When issues are identified, speed matters. A proactive system doesn't just flag problems; it empowers teams to act on them immediately, whether that means deploying a fix, reaching out to affected users, or bridging the gap while a permanent solution is in progress.

4. Accessibility across teams

Data must be accessible to people across various job roles and technical skill levels, within the tools they already use, so that the right people can act on the right information at the right time.

How Fullstory brings it all together

Fullstory is built to deliver on all four of these requirements.

To capture complete data, Fullstory records every action on every element across your product by default.

Then, Fullstory's StoryAI turns that data into actionable insights, alerting you to spiking issues, their root causes, and their business impact. When issues are identified, Fullstory empowers teams to act fast—deploying in-app guides on-the-fly to assist users while a code fix is in progress, and activating data in real time to send email offers that encourage affected users to return.

Finally, Fullstory's MCP makes this data accessible across teams and technical skill levels, allowing anyone to analyze data in plain language on a single pane of glass alongside other systems like CRM and IT ticketing tools.

All of these elements combine to proactively resolve product issues, allowing teams to win more customers, expand account spend, and maximize retention.

→ Want to see how Fullstory can help you catch, diagnose, and fix issues before they cost you customers? Watch a demo here.

Common handoff failures and how to fix them

Alex Gallo — Thu, 04 Jun 2026 06:00:00 GMT

The promise of omnichannel retail is a seamless, connected experience where every touchpoint, from email to app to checkout, feels like part of the same conversation. But for many retailers, the reality looks very different. Somewhere between the campaign launch and the confirmation screen, the experience breaks down. And when it does, customers notice.

These omnichannel handoff failures erode trust, kill conversions, and quietly chip away at the loyalty brands work so hard to build.

The good news is that they're preventable. But only if you know where to look.

What is an omnichannel handoff failure?

In the world of retail, an omnichannel handoff failure typically means there was a disconnect in what should have been a seamless, cross-platform experience. A customer moves from one channel to the next, and something doesn't carry over. The context is lost. The experience fractures.

When we look closer at these digital friction points, a few recurring themes begin to surface, specifically around how retailers handle high-stakes promotions and how they identify and serve their guests in real time.

Three omnichannel gaps costing retailers conversions

1. Fulfillment inconsistencies

Imagine your delight when an email arrives in your inbox notifying you that a product you’ve anxiously been waiting for is now available. You rush to click the link, prepared to buy, only to find that, to your disbelief, the product is already…out of stock?

We’ve all been in this scenario and can attest that it’s incredibly frustrating. It takes what should be a chance for a retail organization to establish rapport and loyalty with a customer and turns it into an experience laced with friction, potentially detrimental to the long-term customer relationship and sentiment towards your brand.

This kind of missed opportunity will send customers looking for the next best thing elsewhere.

The root cause is usually a data synchronization problem; inventory signals that don't update in real time across channels, leaving email campaigns and product pages out of sync with actual stock levels. The fix starts with tighter integration between inventory management systems and customer-facing channels, paired with real-time visibility into where those gaps are occurring and how often customers are encountering them.

2. Peak campaign promotional failures

Peak moments drive tremendous traffic to your site, which means your promotional strategy is critical to driving revenue. But consider a scenario many retailers know all too well: a campaign launches, driving high-intent buyers to your site, only for them to encounter a faulty promo code or a confusing layout when they attempt to check out.

During peak campaigns, customers are in a high-intent mindset with little patience for troubleshooting a promotional field that won't populate their discount. The end result is an abandoned cart and a lost conversion.

Without visibility into these navigational discrepancies, retailers aren't just losing out on immediate revenue—they're facing long-term implications. A faulty code experience shapes how buyers feel about your brand. Once a guest loses confidence, how likely are they to return for your next promotional campaign?

The solution here is to be proactive, not reactive. Retailers need the ability to monitor promotional experiences in real time. Post-campaign analysis helps, but by the time a support ticket surfaces the issue, the damage is already done. Teams that can observe and act on these failure points as they happen are far better positioned to protect both revenue and reputation.

3. Audience identification gaps

In the retail landscape, there can be varying tiers of audiences, ranging from low- to medium-intent buyers to your most dedicated, high-intent loyalists. Campaigns are carefully curated and tailored to target select audiences, but what happens when the content is shown to the wrong audience?

Imagine a low-intent new shopper is served up content intended for credit card members only. Instead of feeling invited to the brand, they might feel snubbed by a gatekept experience they cannot access. And from the retailer's perspective, it's a wasted impression that could have driven a conversion had it been seen by the right person.

Retailers without a digital insights tool often rely solely on support tickets or complaints surfaced after the fact to understand when and where this might have happened. By then, it's usually too late.

When retailers lack the visibility to properly identify who is on their site in real-time, the consequences go beyond a simple mismatch. It creates an environment where the guest feels invisible, and the brand feels out of touch.

You can’t fix what you don’t see

Fulfillment gaps, broken promotions, audience mismatches—these failures look different on the surface, but their root cause is the same: a lack of real-time visibility into the digital experience.

Most retail teams are working with lagging indicators. They hear about problems through customer complaints, see them in post-campaign analytics, or discover them through manual QA. By the time the issue is identified, it has already impacted thousands of sessions and an unknown number of customer relationships.

The retailers best equipped to close these gaps are the ones investing in tools and processes that surface friction at the moment it happens. That means moving beyond aggregate metrics and getting closer to the actual experience: understanding where customers are struggling, why they're dropping off, and what's going wrong between the moment of intent and the moment of conversion.

Find and fix friction with Fullstory

Omnichannel handoff failures are not inevitable. But they are invisible without the right lens. Fullstory gives retail teams the real-time visibility they need to catch friction before it costs them. If your team is ready to stop finding out about problems from your customers and start getting ahead of them, watch a demo to see how Fullstory can help.

The ultimate guide to mobile app onboarding: Best practices and examples

The Yodel Mobile Team — Thu, 20 Jul 2023 04:00:00 GMT

Creating a best-in-class product means nothing if your users can't get up and running smoothly to benefit from those features your team has carefully coded into your mobile app. Optimizing your app onboarding sets you up for success— maximizing the potential to unlock the full value of your new users while also providing them with an enjoyable first encounter with your app.

Why a good app onboarding experience is so important

Onboarding new users well can improve user retention rates by up to 50%. In-app onboarding is simply the process of introducing new users to your mobile app, guiding them through what it does and how it works, and why their life is going to be richer with your app in it. You can do this through account creation, a set of screens or tutorials explaining the features, or other in-app pointers to signpost the way.

Making this first interaction slick and intuitive is crucial for a number of reasons:

First impressions matter

Don’t make the mistake of making the onboarding process an afterthought. Twenty-five percent of users abandon an app after just one use, which likely means they’ve had a lackluster first experience. We all make judgments based on initial impressions, whether that’s walking into a shop and then leaving immediately or literally judging a book by its cover. And while a beautifully branded app is important, first impressions go beyond aesthetics. A frustrating user experience from the outset will make your user think they’re likely to find the same the more they use the app.

Users feel more engaged

By showing users clearly how to use the app, they’re much more likely to engage with all of its features. A highly engaged user is going to have a higher lifetime value.

Increase your app store ranking

In turn, high engagement signals to the app stores that people are enjoying your product, which is a signal that determines how it ranks in app marketplaces. High user churn is another rank determiner; the fewer users who delete or neglect your app, the better news for your app store placement in search results.

6 app onboarding best practices

1. Keep it simple

When it comes to onboarding, brevity is your friend. By keeping instructions simple and concise, you avoid overwhelming your user with information. If your app demands a lot of explanation, split your tutorials into sections that can be done at various stages rather than immediately upon downloading (or consider a redesign!).

2. Keep it awesome

Make sure you demonstrate the benefits of your app, rather than just a list of features. So, a dating app might walk a user through how to find better matches faster through their filtering, rather than simply telling a user that they can set an age range.

3. Consider your creative

No one wants to wade through dense, badly designed text when they’re learning about something new. Make sure your explanations are well thought out, and consider where graphics, video, or an interactive tutorial can be used instead of text. The chart below shows students’ recall of information using different types of learning, proving that text isn’t always the best method of teaching your users.

Source

4. Show progress

A progress bar provides vital information regarding the length of your onboarding process. It keeps users motivated, and lowers the risk of drop-off as people know what they’ve committed to.

5. Make it accessible

Users now expect to be able to use their social logins (e.g. Apple, Google, Facebook) as well as their email address to register an account. Provide options for all of these to maximize your registration conversion.

6. Explain your reasons

If your onboarding includes asking users for personal information in order to personalize the user experience, say that. It’ll make them a lot more comfortable about sharing this data.

Examples of effective mobile onboarding strategies

Progressive onboarding

A progressive onboarding experience walks a new user through an app one step at a time. Gradually introducing a user to different features and capabilities can help ensure that they don’t feel overwhelmed, confused, or frustrated.

Often, this type of onboarding experience is personalized to highlight the most relevant parts of the app based on what a user chooses to see or do. Progressive onboarding might include tool tips that pop up as a user moves through an app for the first time, a short questionnaire to help personalize their experience, or an interactive to-do list for getting started with the app.

Benefits-oriented onboarding

As its name suggests, benefits-oriented onboarding focuses on what the app does for the user, rather than just what it does. Often, a benefits-oriented onboarding experience will highlight three to five key benefits an app provides. For example, a travel booking app might greet new users by emphasizing that users will be able to easily compare prices for flights and hotels, make changes to their reservations directly within the app, and keep all their travel information in one place for easy access.

Function-oriented onboarding

This onboarding strategy takes care to highlight an app’s key features, often walking through them one at a time on consecutive screens. Similar to the benefits-oriented onboarding, this method focuses on three to five core features that every user should know, and avoids going into granular details. Function-oriented onboarding is often used for apps with complex features that might not be intuitive to use.

How to measure the success of your onboarding process

By measuring a range of key metrics through mobile analytics, you’ll get an overview of how your process is performing. It’s also worth researching digital product benchmarks for your particular vertical, or using a measurement tool that provides this information.

Install-to-registration rate: What percentage of people complete registration out of all those who downloaded the app?
Completion rate: What percentage of people complete the entire onboarding process?
Retention rates: How many are still using the app one day, seven days and 30 days after downloading? The inverse number is your churn rate—i.e. if your seven days retention is 25%, your churn rate is 75%.
Paywall conversion: If you ask people to subscribe or buy as part of your onboarding process, how many do this?
Engagement: How much time do users spend on the app and what features do they interact with? This is particularly important if you have introduced a brand new onboarding process—do users now understand your app better and use it more?

It’s important to track these metrics consistently, because if you make any changes to your onboarding process, you want to be able to see if they’ve had the desired effect. Equally, consistent tracking will mean you catch any huge drop-offs, which are a sure sign that something needs fixing urgently.

How to continually improve the mobile onboarding experience

Testing should be iterative when it comes to your app onboarding. The data you collect can help you finetune your app and the onboarding process for users. Additionally, it can also be incredibly useful to conduct qualitative interviews or observe users completing the steps, as this kind of verbal or live feedback can provide a rich stream of improvements you can make.

Mobile session replay gives you a similar view at scale: you can watch how real users navigate your onboarding flow and identify friction points that interview data alone can miss.

Continual improvements will also help you stay inline with user expectations and UX trends. For instance, there’s been an increase in the use of:

Dark Mode, for accessibility and ease of night reading
Gamification, rewarding users for completing certain actions
Augmented Reality, using visual overlays to the user’s own surroundings to explain an app’s features.

By proactively asking for customer feedback and researching competitor processes, you can build these features into your own app on a regular basis and continually optimize onboarding for new users.

Armed with the steps outlined above, your app onboarding has the potential to be a thing of beauty, slickly designed to move your new user from inexperienced to expert over the course of their journey.

This post is brought to you by Yodel Mobile, the award-winning and leading global app marketing consultancy. Yodel Mobile has driven sustainable business success for thousands of app companies, both start-ups and scale-ups, through their award-winning growth marketing strategies.

Yodel Mobile is a Fullstory Partner.

Together, Yodel Mobile + Fullstory provide holistic app marketing solutions combined with digital experience intelligence, enabling clients to experience long-term sustainable growth while crafting a more perfect mobile customer experience.

If you want to find out more about how improving the mobile customer experience can impact your app marketing campaign or you have any app marketing questions, book your free consultation with the Yodel Mobile Growth Team.

Survey finds most businesses failing to turn AI and data into action

Kailey Boucher — Mon, 24 Mar 2025 05:00:00 GMT

Findings based on 2024 market survey. For more, download the Reality Check Report. Companies are collecting more data than ever, AI is everywhere, and personalization is a top priority. But despite all the talk, there’s not a lot of action. Businesses recognize that AI and data have the potential to transform digital experiences, but most are keeping AI in test mode, relying on outdated decision-making, and failing to make the bold moves that drive real impact. In a landscape that’s shifting this fast, hesitation is a liability.

AI is stuck in experiment mode

Despite the buzz around AI, only 17% of businesses consider it a strategic pillar. Companies know AI has the potential to drive real impact, but most still see it as something to test, not trust. Instead of using AI to predict trends, optimize operations, or power real-time personalization, the majority (63%) of businesses limit it to low-risk automation. The hesitation isn’t just about fear of AI, though. Skill gaps, integration challenges, and data quality issues are major roadblocks.

Companies have data but aren’t using It

Businesses aren’t struggling to collect data; they’re struggling to put it to work. 87% of companies gather data, but only 25% say they use it effectively. The challenge is turning data into decisions that actually drive measurable business impact. Too often, insights remain stuck in silos, scattered across disconnected platforms, or buried under layers of dashboards and reports, leaving teams without the clarity or speed needed to take action.

UX is a competitive advantage most brands aren’t maximizing

A great user experience keeps customers coming back—but most companies aren’t doing enough to stand out. Companies that invest in UX see a 15% increase in retention, but while 73% of companies focus on identifying friction in user journeys, only 12% say they lead in real-time personalization, and a mere 4% rank themselves as best-in-class for user journeys.

Here’s how you can stay two steps ahead

AI, data, and personalization can drive business growth, but only if you stop dipping your toes and jump in head-first.

Treat AI as a business driver, not a side project. Instead of asking, "Where can we test AI?" shift the question to "Which critical business challenges could AI solve?" That reframe also changes the AI ROI conversation from "what did we spend on AI tools?" to "what in the business actually changed?"
Prioritize personalization. 58% of companies are planning major investments in personalization. Lead the charge by personalizing product experiences, website interactions, and digital customer journeys. Use behavioral data to anticipate needs and remove friction.
Create a bias for action. A culture of execution starts at the top. When leadership makes action the default—reinforcing speed, accountability, and forward momentum—it becomes part of the company’s DNA.

The future belongs to brands that lean in

The survey shows that AI adoption is half-hearted, data isn’t being used to its full potential, and UX improvements are incremental. But the biggest divide isn’t just in who has access to these tools—it’s in who is actually using them to drive change.

The gap between lagging and leading is only getting wider. Businesses that commit, move fast, and turn strategy into action will set the pace for their industries.

For deeper insights on AI, data, and the future of personalization, download the Reality Check Report and listen to our podcast series, where Fullstory’s experts explore how today’s top companies are turning AI and behavioral data into real business impact.

The agentic workspace: Adapting and investing in the age of AI

Lee Dallas — Mon, 16 Mar 2026 06:00:00 GMT

Many of the podcasts I listen to today begin with the disclaimer, “guaranteed human.”

Why does this matter? In one sense, it's a show of solidarity with content creators and a way to protect intellectual property. That's noble, and I support that idea. But as AI-generated content becomes indistinguishable from human-created work, the label risks becoming performative rather than meaningful.

Perhaps the bigger question is about work itself. What does it mean for work to be "guaranteed human" when AI is woven into everything we do? What role do humans play in an AI-enabled workplace? And how do organizations adapt and invest wisely when the tools keep changing faster than anyone can measure their impact?

Every company I speak with is struggling with these questions and wondering how to make the right investments.

Self-disruption is preventing AI ROI

The greatest obstacle to achieving AI ROI is self-disruption. Things change so quickly that organizations pivot to new objectives before they have the opportunity to measure the output of the last one.

Ask yourself: how many agents deployed six months ago are you still using? For many, that answer is none.

The AI agents themselves are ephemeral. They will evolve much faster than the platforms they rely on, and they will be replaced by newer, more capable versions.

Four key waves in the evolution of the workspace

So how do you invest wisely in something that keeps changing? The answer is to zoom out. The tools change fast, but the way work evolves follows a pattern we've seen before. Every major shift in how we work has followed the same trajectory: a new tool emerges, becomes essential, and eventually gets absorbed into a larger platform that becomes the new center of gravity. Understanding this pattern helps us see where durable value lives, even when the tools themselves are temporary.

1. Email

The first wave seems so long ago as to be hardly relevant, but there was a time when email did not exist. It cannot be understated how drastic a change email introduced in defining what "work" is in modern business. Email products became the workspace where you both received assignments and delivered work products. For the first time, the work itself lived in a digital container. It remains a ubiquitous connection point between the individual and their employer, even as subsequent waves have followed.

2. Content creation platforms

The second wave was content creation platforms, notably Microsoft Office and Google Workspace. These suites didn't kill email; they absorbed it. Outlook became one tab among many. The design paradigm matters here: software companies created suites of related tools managed from a single console. This console captures the "time" of the employee as much as it facilitates access to the tools that produce the work. The platform, not the individual tool, became the workspace.

3. Collaborative messaging

The third wave is collaborative messaging, specifically Teams and Slack. The adoption of these work patterns may have been accelerated by the pandemic, but they were already well on their way to reshaping how organizations work together. Once again, the new wave absorbed the last. Teams didn't replace Office; it became the interface through which you accessed Office. Slack didn't replace your tools; it became the connective tissue between them.

4. The agentic workspace

This brings us to the fourth wave: the agentic workspace. This is the emerging model of work where humans define objectives and agents execute across tools. The human is the conductor. The agent is the instrument. The job to be done is what matters.

The pattern holds: Do not assume that the tools used today to produce "work" will themselves be replaced. They will evolve along with the infrastructure of AI.

But while the paradigm is clear, the platform is not. OpenAI, Anthropic, and others are leading with core AI technology, but there is no guarantee they will be best positioned to host the agentic workspace. The traditional enterprise players—Microsoft, Salesforce, Google, AWS—are all vying to be the platform for human engagement with AI as well as for autonomous workloads.

Where to place your bets

Thoughtful providers will develop their own agentic workspaces, leveraging the tooling they are simultaneously building to expose value in both intra-product and inter-product scenarios. Deciding on investments requires understanding both approaches for any given vendor.

But the agents themselves are not the bet. They're ephemeral. They'll be replaced by newer, more capable versions faster than you can measure their ROI.

Focus investments on two areas:

Data quality and completeness. The data that supports agent workflows is the foundation. Repeatable, deterministic work performs best with well-structured data—and that's where agents will provide the greatest value early.
Curation and insight definition. Defining meaningful insights that inform the decisions humans need to make will remain critical. Agents can surface information. Humans must decide what matters.

Rethinking time-to-value

Traditional ROI models assume stability. You invest, you deploy, you measure. But AI doesn't work that way because the tools change before you’ve finished measuring results.

This new era demands a different mindset: agile AI implementation. Shorter cycles. Smaller bets. Continuous iteration. Time to value becomes less about "when does this pay off" and more about "how fast can we learn and adapt."

The organizations that treat AI as a fixed investment will struggle, and those that treat it as a continuous evolution will thrive. The goal isn't to get it right the first time. The goal is to build the muscle for constant adaptation.

This is why data and curation matter more than any individual agent. Agents come and go. The ability to feed them well-structured data and extract meaningful insights remains. That's where durable value lives.

Where humans fit into the new workspace

The agentic workspace redefines work entirely. Agents take on the repetitive, deterministic work that used to fill human calendars. Humans set objectives, validate outputs, and make judgment calls that agents cannot.

So what does "guaranteed human" actually mean in an AI-enabled world? It's not about who created the content, but rather about who is accountable for the outcome.

If you’re struggling with how to make the right AI investments in this new workspace, the answer may be simpler than it seems: invest in the things that make human judgment more effective. That's where durable value lives.

Power better decisions with Fullstory

While AI will keep evolving, the need for well-structured, actionable data won't.

Fullstory captures behavioral data that powers better decisions, helping teams understand where users struggle, what's working, and where to focus next. Ready to see it in action? Request a demo today.

Introducing a new approach to visual analytics

The Fullstory Team — Wed, 13 May 2026 06:00:00 GMT

Most heatmaps show you where users clicked. What they can’t tell you is what those clicks were worth, and that’s what ecommerce and merchandising teams actually need to know.

Our heatmaps already give teams a clear view of how users move through a page, and now we’re taking that foundation further.

Clicks without context

The traditional heatmap workflow goes like this: you open a report, see a red cluster over a hero banner, and assume it’s working. Then you check your sales data and realize conversions are down.

What the heatmap didn’t tell you: those clicks came from a low-intent segment that rarely converts. Your highest-value customers scrolled right past the banner. You had activity data, but not answers.

From clicks to cash

Upcoming Conversion Heatmaps let you overlay revenue, conversion rates, and other business metrics directly on a visual of your page—and drill into the results through zone-based hierarchy drilldowns.

Start at the page level (say, shoes), narrow to a specific layout area like the product carousel, pinpoint individual product cards, then surface the granular details that actually drive the sale—specific brands, sizes, price points. A merchandising manager can see revenue-per-click on every product tile in real time, giving them a prioritized list of revenue opportunities backed by behavioral data.

No more manual tagging

Legacy tools have a manual tax: if you didn’t tag an element before launch, you don’t get the data. Every site update means re-mapping zones from scratch.

Fullstory is built on autocapture. We record 100% of user interactions automatically, with no manual setup required. Your data is retroactive and gap-free from day one, and it stays intact when your site changes.

Getting to the root cause

When something shifts in your data, you need to know why. Our updated workflow connects aggregate visual trends—like a drop in clicks on a new promotion—directly to the individual session replays that show what happened. Teams can get to the root cause in minutes.

Building this with our customers

This vision of click to cash isn’t something we’re shipping in a vacuum. We’re going into early access with a select group of customers who are helping us shape Heatmaps in partnership with our product team. Their feedback is informing how the workflow comes together, from the metrics that matter most to the drilldown patterns merchandisers actually use day to day.

If you want a closer look at what’s coming, contact your account representative. New to Fullstory? Schedule a demo to see where heatmaps are headed.

Fixing fragmented customer intelligence in banking

Ricky Page — Fri, 15 May 2026 06:00:00 GMT

Financial institutions have plenty of data but lack the connected, contextual, and actionable insights needed to truly understand their customers.

Analytics platforms, CRMs, feedback tools, call center logs, and BI dashboards each provide only a partial view, leading to fragmentation rather than clarity. Much manual effort is still spent piecing together the customer story, while event-based tracking and dashboards reveal what happened but rarely explain why. As digital ecosystems grow more complex and organizations pursue AI-driven personalization, the gaps caused by fragmented data and organizational silos become even more pronounced.

Data alone isn’t enough—achieving measurable outcomes requires a more connected approach to customer insight.

The myth of the unified customer view

Many banking organizations assume they have a unified customer view via central dashboards, but insights remain fragmented across analytics, CRM, surveys, and support systems.

Each system captures a different slice of the banking customer journey—web analytics logs clicks across online banking, CRM stores account and product history, feedback tools track sentiment, and contact centers log service and dispute issues. These sources are rarely stitched into a cohesive narrative. As a result, no single owner oversees the full banking journey.

Data responsibility is divided across digital, risk, operations, and product teams, governance is unclear, and metrics are sometimes defended rather than examined. Teams optimize locally without understanding cross-journey effects, and organizational complexity grows faster than insight maturity. Without alignment and shared ownership, unified reporting in banks is just a surface layer over disconnected realities.

The hidden cost of event-based tagging and data silos

Event-based and tagged data are central to digital analytics in banking. Every interaction in online banking or mobile apps must be defined, tagged, and maintained, creating a high-effort model reliant on engineering and predefined use cases.

Teams can measure entry points and conversions for products like loans or credit cards, but struggle to see what happens between steps, across sessions, or channels. New business questions often can't be answered if events weren't tagged during the initial release.

This slows experimentation—tagging backlogs delay analysis, and product managers wait for data updates before validating hypotheses on key journeys like onboarding or payments. Agility decreases, even as institutions aim for continuous delivery.

Trust erodes as tools report conflicting numbers for applications, approvals, or drop-offs. Teams rely on tightly controlled A/B tests because broader datasets seem unreliable, narrowing insight.

As digital banking ecosystems expand with new apps, partner integrations, and open banking APIs, complexity increases. Without governance and shared ownership of data quality, fragmentation grows. Adding tools amplifies the issue instead of solving it.

Connecting digital behavior to operational impact

Improving customer experience in banking means understanding behavioral signals before drop-off and linking journeys to operational and financial outcomes. Long onboarding, repeated authentication, form errors in loan or mortgage applications, and inconsistent experiences across channels cause frustration that dashboards can't explain. In heavily regulated financial services, friction affects conversion, compliance, risk exposure, and support costs.

The disconnect between digital behavior and banking operations compounds issues. When customers abandon applications and call contact centers, customer service agents often lack visibility into prior online interactions, causing repetition, delays, and reduced trust.

Over-reliance on surveys in financial services skews feedback toward extremes. The silent majority—those who struggle with payments, transfers, or account access but don't complain—remain invisible without large-scale behavioral insight.

Enabling self-serve insight and faster experimentation

Empowering product managers in banking with self-serve behavioral insight is key. Teams should explore journeys such as mortgage applications or account openings and validate opportunities independently, without waiting for custom queries. This shift enables continuous optimization, moving organizations from opinion-based to evidence-driven decisions anchored in real customer behavior.

To move from data collection to outcome-driven execution, organizations must:

Create a unified customer view by combining behavioral, feedback, and operational data. Link digital banking interactions with contact center activity, back-office processing, and financial outcomes.
Reduce reliance on manual tagging. Behavioral data capture across mobile and online banking should minimize blind spots and enable teams to explore new questions about lending, payments, or servicing without significant engineering effort.
Embed clear governance and ownership aligned to financial regulations. Define custodians, align on definitions for core banking metrics, and build trust via transparency and consistency.
Integrate AI purposefully. Summarise behavior for service agents, trigger proactive fraud or churn interventions, personalize product recommendations, and automate operational tasks, all using high-quality, compliant data.
Measure success by customer and business outcomes. Reduced friction in onboarding, faster resolution of service cases, higher product conversion, lower cost-to-serve, and improved regulatory compliance—not feature volume.

Operationalizing insight for more intelligent digital banking experiences

Disconnected dashboards and event-based metrics do not create a unified understanding of your banking customer. To move forward, financial institutions must shift from collecting data to operationalizing insight. That requires a single, end-to-end view of the customer journey across onboarding, lending, payments, and servicing that combines behavioral, feedback, and operational data. It also requires reducing dependency on manual tagging, strengthening governance and ownership, and enabling teams to explore and act on insights without engineering bottlenecks.

AI and automation in banking will only be as effective as the data foundations supporting them. Without a complete, high-quality behavioral context, predictive models for churn, fraud, or next-best action amplify blind spots rather than eliminate them.

The goal is not more reporting. It is faster experimentation, clearer root-cause analysis, lower support and servicing costs, improved compliance outcomes, and measurable improvements in customer trust and financial outcomes. When insight becomes embedded in workflows—not confined to dashboards—banks close the gap between what metrics say and what customers actually experience.

→ Ready to see how Fullstory helps brands like yours? Watch a demo to get started.

Building lasting customer loyalty with behavioral data and AI

The Fullstory Team — Fri, 05 Jul 2024 05:00:00 GMT

Maintaining customer loyalty in a fast-paced market can feel like chasing a moving target. But with the advent of behavioral data and artificial intelligence (AI), businesses have powerful new tools to foster deeper customer connections and enhance experiences. By leveraging these technologies, companies can not only understand their customers better but also create personalized interactions that drive loyalty and long-term engagement.

The impact of personalization on customer loyalty

Personalization is key to building lasting customer loyalty. A study by Epsilon reveals that 80% of consumers are more likely to make a purchase when brands offer personalized experiences. This underscores the transformative power of personalization in today’s competitive landscape.

With AI-driven insights, businesses can tailor their offerings, communication, and recommendations to meet each customer's unique preferences. AI rapidly analyzes vast amounts of data, uncovering patterns and trends that predict customer behavior. This proactive capability allows businesses to anticipate needs, make recommendations, and deliver a seamless, customized journey that keeps customers returning

Revolutionizing customer support with AI

AI customer service delivers a level of service that was once unimaginable. Chatbots and virtual assistants provide instant, 24/7 assistance, resolving issues and answering queries in real time. This boosts customer satisfaction, frees up human agents to tackle more complex problems, and reassures businesses about the efficiency and effectiveness of their customer support. According to Gartner, by 2025, AI will power 95% of all customer interactions, ensuring seamless and efficient support. These AI-driven solutions enhance the customer experience by providing quick, accurate responses, leading to happier customers and more streamlined operations, ultimately setting a new standard of service excellence.

Leveraging sentiment analysis for enhanced customer engagement

Understanding customer sentiment is essential for fostering engagement and loyalty. AI-driven sentiment analysis enables businesses to decode customer emotions by analyzing feedback from reviews, social media, and surveys. This provides real-time insights into customer sentiment, helping companies promptly address concerns and improve the overall customer experience.

By monitoring customer sentiment, companies can make informed decisions that resonate with their audience. This proactive approach builds trust and enhances engagement, ensuring that customers feel heard and valued. In today’s competitive landscape, leveraging AI for sentiment analysis is a game-changer in maintaining strong, positive customer relationships.

Crafting a winning strategy

Businesses need a well-structured strategy to leverage the full potential of behavioral data and AI. Here are the essential steps to build a robust plan that drives customer loyalty and business success:

Collect and consolidate data: Gather data from all customer touchpoints and centralize it for comprehensive analysis. This unified data repository is the foundation for meaningful insights.
Invest in AI tools: Choose AI tools that align with your business needs, such as customer segmentation, predictive analytics, and sentiment analysis. These tools help you uncover patterns and trends that drive strategic decisions.
Implement personalization strategies: Use AI insights to create personalized marketing campaigns, product recommendations, and customer support experiences. Tailored interactions make customers feel valued and understood.
Monitor and adapt: Continuously track the effectiveness of your AI-driven initiatives and refine strategies based on customer feedback and changing behaviors. This ensures your approach remains relevant and impactful.

Following these steps, businesses can transform raw data into actionable insights, creating personalized experiences that foster loyalty and drive growth.

The advantages of AI-driven customer loyalty

Integrating AI and behavioral data offers numerous advantages that significantly enhance customer loyalty. Here are the benefits:

Increased customer satisfaction: Personalized experiences and proactive engagement lead to happier customers. AI-driven insights allow businesses to anticipate customer needs and deliver tailored interactions that make customers feel valued.
Higher retention rates: Satisfied customers are likelier to remain loyal to your brand. By understanding and addressing customer preferences and pain points, businesses can create a seamless and enjoyable customer journey that encourages repeat business.
Better resource allocation: AI-powered tools streamline proactive customer support and marketing efforts, allowing your team to focus on high-impact areas. This efficient resource allocation leads to improved operational performance and cost savings.
Enhanced competitive edge: Companies that leverage AI and behavioral data can stay ahead of the competition by anticipating and meeting customer needs more effectively. This proactive approach positions businesses as industry leaders, driving innovation and market success.

By embracing AI and behavioral data, businesses can unlock powerful insights that drive customer loyalty, ensuring long-term growth and competitive advantage.

Final thoughts

Leveraging behavioral data and AI is crucial for businesses aiming to excel in today’s landscape. Understanding customers deeply and using AI to enhance experiences can build lasting loyalty and growth.

Start by collecting and consolidating data from all touchpoints. Invest in AI tools to uncover insights and implement personalized campaigns and support. Continuously monitor and refine your strategies based on feedback to stay relevant.

Turning raw data into actionable insights helps craft personalized experiences that boost loyalty and drive growth. Embracing AI and behavioral data ensures long-term success and a competitive edge.

Want to find out where your company stands with behavioral data? Download the Fullstory Data Maturity Matrix and take our quiz to find out.

How IT teams can master SaaS sprawl with Fullstory Workforce

Elif Alverson — Mon, 11 May 2026 06:00:00 GMT

Managing hundreds of SaaS applications—especially during proof-of-concept (POC) phases and contract renewals—has historically been a sluggish, manual process. You send out user surveys, analyze messy spreadsheets, and ultimately guess how many licenses you actually need to support your company.

But guessing is no longer a viable strategy. Organizations need precise data in order to make informed decisions about their software usage and spending.

Fullstory Workforce is completely flipping the script on how IT teams manage software. By transforming procurement from a tedious chore into a highly efficient, data-backed operation, Workforce uses real-time analytics to save your team massive amounts of time and money. It gives you the ultimate leverage: indisputable proof of how your vendors are being used, right down to the individual session.

Procurement in the age of AI vendors

We are officially operating in the era of AI-featured vendors. Every tool in your stack is evolving, and the sheer volume of software required to run a modern enterprise is expanding at an unprecedented rate. Because of this rapid growth and the push for AI integration across all platforms, the timing and efficiency of your procurement process are more critical than ever.

But the current landscape is fraught with inefficiencies. When IT teams evaluate a new vendor during a POC or prepare for a massive enterprise renewal, they traditionally rely on basic login metrics or, worse, the vendor’s own usage reporting. This creates a massive blind spot. Just because an employee logged into a platform once a month doesn’t mean they are actively utilizing it, nor does it mean the tool is functioning properly for your specific business needs.

That's why IT leaders are increasingly looking for tools with deep AI capabilities that can drastically cut down procurement cycles by surfacing actionable insights fast—not just dumping raw, unfiltered data on their desks. With IT budgets under a microscope and software bloat creeping into every corner of the enterprise, having definitive, real-time proof of a tool's ROI is the only way to walk into your next vendor negotiation with leverage.

How Fullstory manages its own software stack

Workforce is more than an employee experience tool; it’s a heavyweight champion for real-time AI data analytics, specifically built to optimize IT operations and supercharge procurement.

At Fullstory, we rely on Workforce to procure and manage our own vendors. We use real-time AI analytics to look at usage patterns across our entire organization. In seconds, we can decide with absolute certainty whether we need to renew or cancel a vendor. We know exactly how to adjust our contracts—whether that means reducing unused licenses, increasing seats for power users, or purchasing additional services that the data shows we actually need. These insights dictate the negotiation methods we bring to the table because we hold the actual usage data.

Beyond just the financial aspect, there is a massive operational benefit that isn't talked about enough. We use Workforce’s session captures to troubleshoot vendor-specific issues. When a team member experiences a login failure, an active usage bug, or a configuration error on a vendor’s page, we don't have to wait days for the vendor's external support team to figure it out. We review the session, identify the exact friction point, and resolve the issue internally.

Data is your biggest procurement advantage

If you are managing IT procurement or engineering operations, it's time to fundamentally reframe how you view user interaction data. It is not just for product teams monitoring website conversions; it is your most powerful negotiation and troubleshooting asset.

In practice, this means integrating Workforce directly into your procurement and IT support workflows. During a POC, don’t rely on a vendor’s success metrics. Instead, use Workforce to see if your team is actually adopting the tool, how they are interacting with the AI features, and where they’re getting stuck. Ninety days before a contract renewal, pull the AI-driven usage reports to build your negotiation strategy. If the data shows, for example, that only 20% of your licensed users are active, you have the concrete proof you need to right-size that contract and save your company significant capital.

Kill the guesswork in your procurement process

The era of relying on gut feelings, user surveys, and vendor-supplied reports is over. AI is here, and it is ready to drastically reduce the time you spend managing SaaS sprawl. With Workforce, the same behavioral data you use to understand and improve user experience can be used to master IT procurement.

By leveraging real-time analytics and session insights, you are no longer just managing licenses—you are actively troubleshooting your stack and negotiating from a position of absolute power. Ready to take control of your usage data and stop leaving money on the table during your next vendor renewal? Request a demo to see how Workforce can help.

Rethinking real-time data: The shift to just-in-time data

Lane Greer — Fri, 24 May 2024 05:00:00 GMT

Understanding customer behavior is crucial to building loyalty in today’s fast-paced digital world. With customers interacting constantly, “real-time” data sounds like the perfect solution to stay ahead. But what does "real-time" customer behavioral data really mean?

The truth is, there's no one-size-fits-all answer. The ideal timeliness of data depends on what you're trying to achieve. This blog post explores the concept of "just-in-time" customer behavioral data, and how it can empower you to make data-driven decisions and unlock real-time actionable insights.

What is real-time data?

Real-time data is often defined as information that’s captured, processed, and surfaced the instant an event happens. This approach, often referred to as real-time data processing, powers systems that rely on low-latency streaming and real-time analytics. While real-time data analytics can surface important insights quickly, it can come with complexity and cost.

Benefits of real-time data collection

Faster decision-making: Events are captured and acted on quickly, empowering faster reactions and potentially preventing negative outcomes.
Enhanced user experiences: Personalization engines and dynamic UI adaptation benefit directly from fast, session-aware data streams.
Proactive support and intervention: By detecting behavioral friction—such as rage clicks or repeated form errors—teams can offer help before a session is lost.
Integrated system updates: Data can feed alerts, dashboards, and downstream tools to maintain platform awareness.

That said, the value of real-time capture often isn’t in reacting instantly; it’s in how, when, and why the data is ultimately used.

Rethinking what “real-time” means

Over my tenure at Fullstory (coming up on seven years, wow), I’ve directly engaged with over 500 customers and prospects, and when formulating the capabilities required of their behavioral data platform, some sense of real-time data is not uncommon.

But upon double-clicking into the requirement, sometimes “real-time” means milliseconds, sometimes minutes, sometimes hours, sometimes days. When it comes to the usefulness of customer behavioral data, truly “real-time" might not always be the most helpful, cost-effective, appropriate, or practical.

In truth, literal "real-time” isn't even possible. From the moment a customer takes some action on a site or mobile app, the browser doesn't even respond in real-time. It takes many microseconds for native browser functions to respond to the customer. Therefore, when you define “real-time," you are always working with an abstracted definition, and when an abstraction defines any definition, no definition becomes literal or all-encompassing.

The premise and thesis of this post will suggest an alternative colloquialization. We should use a different term. We should choose a term that is helpful in all contexts, and the helpfulness will be defined by the term’s utility in encouraging deeper discovery into the purpose of the requested data. Instead of using the term "real-time,” which has a literal definition but an abstract meaning, we should use the term "just-in-time,” (JIT) as its literal definition implies the necessity of additional clarity.

JIT data begs the questions:

Just in time for what?
Just in time to be used by whom?
Just in time to enable what specific outcome or capability?

The requirement of JIT digital behavioral data removes the focus on the data itself and places the emphasis squarely where it should be; it focuses the attention on the outcome you were trying to catalyze.

The power of just-in-time data

When we talk about 'real-time' data, it's often associated with lightning-fast millisecond updates that fuel personalization engines. In the context of site performance monitoring, it could also mean achieving to-the-second accuracy in daily reported and aggregated data.

Sure, getting data instantly has its advantages, but crunching and analyzing enormous datasets at those super-high speeds can be costly, impractical, and useless.

Just-in-time data takes a more measured approach, focusing on delivering the right data at the precise moment needed to satisfy a stated and scoped requirement that matters.

That moment may come during a live session, when a user opens an email, or when they hover over a key product feature. With Fullstory Anywhere: Activation, behavioral insights can be pushed directly into the tools you use to personalize, message, or guide users in real time.

Unveiling customer behavior with just-in-time data

Imagine seeing exactly how customers interact with your website or app in granular detail and receiving that information at the very moment a struggling customer is experiencing that digital pain. When do you need that data? Just in time for your support agents to assist them live.

Or, imagine having to-the-second accuracy regarding the performance of your back-end APIs in context with the lag of your site loading spinners or the thrashing of your users’ mouses. The additional behavioral data adds color and context to your server performance data in that scenario. A 10- or 15-minute delay allows additional context about the customer journey to be included in the performance report. In this case, just-in-time data prioritizes context and completeness over timeliness.

Just-in-time customer behavioral data enables practical, measurable, and relevant outcomes for your business. For instance, you can use just-in-time data to see where customers drop off in a checkout funnel and make changes in your next design sprint to improve conversion rates. You can also train a model to identify previously invisible customer segments with specific browsing behaviors and tailor your marketing messages accordingly. Then market to them over the next major retail holiday. By delivering the correct data at the right time, just-in-time data empowers you to act on customer insights and optimize your digital experiences. The data isn’t the focus. Your business’s outcome is the focus.

Turning insight into action through smarter data processing

Just-in-time data isn’t just about speed—it’s about delivering structured behavioral insights in a form that’s actually usable. Teams often struggle not because they lack information, but because they’re forced to process data manually or wait days for stakeholder-ready results. With structured data pipelines, teams spend less time managing raw inputs and more time delivering value through targeted data analytics and immediate decision-making.

Equipping yourself for just-in-time user behavior data collection

The key to unlocking the power of just-in-time data lies in scoping your use cases and choosing flexible tools to meet those data needs. You need flexible data collection tools that can adhere to various time requirements at numerous levels of data fidelity and completeness.

Look for tools that can capture customer behavioral data in real time and then deliver that data to your downstream systems at the precise moment and in the correct form required for a given outcome.

For instance, at Fullstory, 100% of customer behavioral data is collected in true “real-time.” Every mouse movement, page load, and network error—click, thrash, change—everything. But capturing it and immediately (real-time) making that available wouldn’t be helpful.

It’s too much data and not in an easily usable form. Some of that data can be used in near-real-time. The recreation of sessions for viewing customer experiences, for example, can be served up and made available for real-time co-browsing in a support experience, but it’s important the interaction analytic event data is processed, cleaned, and structured so it can run on a few minutes delay.

Structure is more important than timeliness when it comes to accurate analytics, but timeliness is more important than structure in the context of real-time intervention.

That’s where Fullstory’s Fullcapture plays a foundational role. Unlike traditional approaches, Fullcapture captures user behavior comprehensively in real time—without requiring manual tagging or slowing down the client experience. That complete dataset is then flexibly structured and delivered based on the needs of the outcome you're driving.

If you need analytic accuracy in real time, Fullstory enables near real-time sequenced streaming webhooks, so if you need some set of data to inform personalization decisions, some set of data can be streamed to satisfy that use case.

Connecting just-in-time data to your broader data ecosystem

Just-in-time behavioral data has the most impact when it’s connected to the rest of your digital data strategy. Whether it’s powering systems downstream, training models, or informing long-term planning, just-in-time delivery makes behavioral insights more usable across your organization.

Historical data: Unlock longer-term insights

Because Fullstory captures everything (by design), you don’t need to predict which interactions to track. That means you get a complete behavioral archive that’s immediately useful not only in the present but also in historical analyses. Teams can go back and re-segment, discover patterns, or evaluate UX changes over time—without missing context or events that weren’t “tagged.” Historical data makes just-in-time delivery retrospective, too.

Data warehouses: Make behavioral data universally available

Behavioral data becomes even more valuable when it can flow freely into the tools your teams rely on day to day. Just-in-time pipelines can deliver clean, complete user data to a central data warehouse, where it can be joined with product metrics, transactions, and customer attributes—unlocking a unified view of your digital experience.

With Fullstory Anywhere, teams can access structured behavioral insights not just inside the Fullstory platform, but across the business—within CRMs, CDPs, BI tools, and wherever data decisions are being made. That means the same behavioral context guiding product and UX decisions can now support marketing, customer success, and business intelligence use cases, without requiring custom exports or extra engineering work. With insights made available directly inside tools that business users already rely on—like CRMs or BI dashboards—context no longer needs to be asked for; it’s already there.

Predictive analytics: Fuel smarter decisions

When behavioral data is reliable and delivered just in time, it becomes a powerful ingredient in predictive models. You can train models to surface high-value segments, identify leading indicators of conversion or churn, or prioritize opportunities based on user behavior patterns. Whether used for personalization, messaging, or forecasting, well-timed behavioral signals increase the accuracy and relevance of predictive analytics efforts across the business.

When this data feeds machine learning models, it can also power automated anomaly detection, flagging unexpected behavior and enabling proactive responses based on user patterns.

Real-time vs. batch processing: Not either/or

Some teams assume they need to choose between real-time data streams and batch processing workflows. But with a just-in-time mindset, the goal isn’t choosing one or the other—it’s coordinating the two to match your decision-making needs.

Just-in-time delivery doesn’t exclude streaming or batch—it sits on top of them. Sometimes batch delivery provides the most efficiency for historical reporting or long-range planning. Other times, streaming partial insights to support teams in near real time is ideal. When integrated properly, the two work together as part of a unified data infrastructure.

Analyzing data in motion vs. at rest

Traditional systems prioritize data at rest—data that’s already been captured, batched, and stored. But modern digital analytics increasingly demands the ability to analyze data in motion: a continuous stream of behavioral signals happening in real time.

Just-in-time models offer the flexibility to do both. You can stream lightweight signals for fast responses, and then aggregate structured data for in-depth retrospective analysis—all without overwhelming teams with unnecessary data volume.

Real-time data analysis gives you a competitive advantage

Companies with strong behavioral instrumentation can react more quickly to risks, capitalize on trends, and serve customers more proactively. Real-time data analysis is a core driver of competitive advantage—not just in digital products, but across the supply chain, marketing, customer support, and operational strategy.

The key isn’t just having access to data constantly—it’s structuring it in a way that drives confident, timely decisions. That’s where the just-in-time philosophy excels.

Unlock better outcomes with just-in-time behavioral data

The pursuit of a single definition for "real-time" user data can be a misleading chase. The reality is, the ideal data availability depends on your specific use case. By embracing a just-in-time approach, you can ensure you're getting the right data, at the right time, to make data-driven decisions and optimize your customers’ experiences.

In essence, real-time capture + just-in-time delivery = better outcomes.

Just-in-time data accessibility, powered by real-time data capture, is the key to unlocking a new level of customer understanding, personalization, and customer delight.

Whether you’re activating predictive analytics models, diagnosing drop-offs, or using historical data to inform future design, Fullstory empowers you to act—not react. Because it’s not just about being fast. It’s about being ready, with the right data, at the right moment.

Ready to start collecting real-time insights about customers with Fullstory? Get a demo today.

Retail transformation: Win the AI revolution with intelligent experiences

The Fullstory Team — Wed, 25 Mar 2026 06:00:00 GMT

The retail landscape is being entirely redefined by artificial intelligence. As we navigate this shift, it’s not enough to simply deploy AI; retail executives must strategically wield it to enhance the entire customer journey. By understanding the full scope of the shopper journey with insights surfaced by AI, retailers can create personalized experiences and find and fix friction before it leads to abandoned carts.

Moving from personalization to prediction

Personalized shopping is now table stakes. Basic product recommendations are no longer enough; leading brands are using AI to understand the context behind customer behavior and predict what shoppers need next. For instance, an AI-powered retailer can analyze a shopper's entire digital session to understand the intent behind their journey and surface relevant solutions, content, or offers rather than just recommending a single product.

This is the new standard. Instead of reacting after the fact or creating one-size-fits-all experiences, retailers can predict what individual shoppers want and need, which has been shown to boost conversion rates by up to 30%.

Mastering the ecommerce journey by eliminating invisible friction

One of the biggest challenges for retail executives is understanding why customers abandon their carts or drop off during the shopping process. To overcome this, they must look beyond basic metrics and use AI to identify the root causes of these issues.

Imagine a shopper repeatedly abandoning their cart. A true AI-powered intelligence platform won't just flag the event; it will pinpoint the exact cause (like unexpected shipping costs or a broken promo code field) and quantify the revenue impact. This proactive, data-driven approach transforms how retailers optimize their digital properties, turning costly friction into revenue.

Embracing transactional agents: AI that solves problems

The retail space is evolving from simple chatbots to sophisticated transactional agents capable of handling complex tasks autonomously. Unlike chatbots of old that basically regurgitated an FAQ list, these agents can guide customers through transactions, resolve issues, and provide personalized support.

Unifying the customer experience with a "phygital" strategy

The line between physical and digital retail has been erased. The modern customer journey is fluid, and a phygital strategy—one where in-store and online touchpoints work as a single ecosystem—is no longer optional.

Major retailers are leveraging in-store mapping apps that guide customers through the store and deliver real-time location-based offers. AI is the connective tissue that makes this possible, creating a single, unified view of the customer across every touchpoint. It processes data from in-store behavior and online activity to create a truly seamless and personalized brand journey, eliminating the disconnects that frustrate customers and damage loyalty.

Building a self-healing supply chain

World-class digital experiences are built on a foundation of operational excellence. AI-driven predictive analytics enable a "self-healing" supply chain where inventory levels are adjusted in real-time based on buying patterns. By anticipating demand, retailers can prevent the "out of stock" messages that abruptly end a promising customer journey, directly linking operational intelligence to customer satisfaction and revenue protection.

How Fullstory powers the AI evolution

To win in this new era, retailers need a platform that provides a complete, pixel-perfect view of every customer journey. Fullstory's StoryAI is the engine for this transformation. It moves beyond surface-level analytics to automatically identify, quantify, and prioritize the most critical experience issues impacting your customers and your revenue.

By turning massive sets of user interaction data into proactive, actionable insights, StoryAI empowers retail leaders to eliminate friction, optimize conversions, and build the seamless digital experiences that define modern retail.

Are you ready to transform your retail strategy? See how StoryAI can help you elevate every shopper experience.

The essentials of data protection: Principles, strategies & policies

Fullstory — Wed, 05 Jun 2024 07:00:00 GMT

What is data protection?

Data protection embodies all of the people, tools, and practices used to keep data safe from both accidental and deliberate misuse. Companies that store and process data are keen to protect their data since misuse can have serious business consequences, including total business failure. Generally, companies face ongoing legal action, regulatory fines, and loss of customer trust whenever they fail to protect their data.

Best practices to protect your customer data

1. Have a team dedicated to cybersecurity and risk management

A dedicated cybersecurity and risk management team will design data management systems that are purpose-fit for the types of data that you store and process. Understanding risk is the first step to defining which tools and processes your company needs to bring online. Once risk is well understood, adopting relevant cybersecurity frameworks and executing their policies happens next. Should you adhere to SOC2 or ISO 27001 (security) and 27701 (privacy) or other controls? Your risk assessment will guide you. Risk assessments need to happen periodically to ensure that your risk mitigation strategies and cybersecurity posture won’t go stale as your business evolves.

Learn more about best risk management and cybersecurity practices.

2. Classify your data according to the business risk of misuse

Assign risk categories to your data that map to the expected business impact if the data were leaked or otherwise misused. For example: customer data stored in production systems is far more detrimental to your business if it is misused vs. internal data like your employee handbook.

A data classification rubric could look something like this:

Highly Confidential: we could go out of business
- Example: customer data
Confidential: bad but we'll survive
- Examples: company financial data, product source code
Internal Use: embarrassing or uncomfortable
- Examples: vendor lists, customer lists
Public: no risk
- Examples: content that would otherwise by published on your website or blog

3. Adopt the principle of least privilege for data access

Your employees should only be able to access the data required to perform their job functions, and only for as long as they are required to do their work. By limiting access rights for users to the bare minimum, the potential for damage from accidents or breaches is reduced. Proper website security practices are essential to mitigate these risks."

Least privileged access also applies to access between systems. For example: backend servers may be able to access some databases, but not others. Limiting access between parts of a system can help maintain system stability by preventing services from altering data that they should otherwise have no reason to access.

4. Have clear policies on data retention and deletion (and automate them)

How long is your data stored? And what are the expectations for how quickly you can access your data over time?

You should define storage timeframes for all of your data, and in some cases you should include storage categories that determine the availability of your data. You should automate deletion of your data after a defined timeframe as well as transitioning your data through storage categories as it ages, if necessary.

Your data retention policy should mandate that all data is encrypted at rest and in transit.

5. Train staff on data protection expectations

Periodic mandatory training sessions for all company employees will reinforce good data management hygiene and help keep data protection at the top of everyone’s mind. A security awareness program should be established and maintained by your dedicated cybersecurity and risk management team.

Everyone at your company should know:

How to properly access data
How to spot potentially malicious emails or social media messages and what to do when they receive them
The latest trends in cybersecurity and security vulnerabilities

6. Use Single Sign-On (SSO) with Multi-Factor Authentication (MFA) with any application that stores or processes data

SSO enables a single point of management for all logins to the tools used by your company to access its data. SSO providers include MFA tools that provide a secondary form of authentication (like a one-time use code) to further limit the opportunity for unauthorized access. One of the functions of your cybersecurity and risk management team will be to audit your tools and ensure that they are all under the SSO+MFA umbrella.

How is data protection different from data privacy?

Data protection is owned by IT security teams. Their goal is to safely store and control access to all manner of data, including highly sensitive data. They are responsible for mapping data classification protocols to cloud and on-premise infrastructure (servers, networks, databases, etc.) and enforcing data governance processes.

Data privacy is owned by business and legal teams. Legal teams want to ensure that data is captured in accordance with local laws and regulations. Business teams want to eliminate the reputational risk associated with the misuse of sensitive customer data. These teams define governance, controls, and guidance that determine what data is transmit-able and store-able in internal or external systems and how this data may be processed.

The importance of data privacy

A good data privacy posture is necessary to ensure goodwill between a business and the customers whose data they manage. Being good stewards of customer data helps you maintain trust that you are handling data securely, limiting risk to your customers while allowing them to maximize the value of their data.

Fullstory’s data protection and privacy practices are published in a single location, available for all of our customers and partners to review: https://trust.fullstory.com.pgm.c5nprx.cc/.

Data privacy regulations & standards to know

The General Data Protection Regulation (GDPR)

Introduced by the European Union in 2018, GDPR is designed to give EU citizens control over their personal data and to unify the regulatory environment around data for international businesses. It applies to all companies processing the personal data of subjects residing in the EU, regardless of the company's location. Due to this stipulation, many international businesses (Fullstory included) have adopted data management systems and policies to ensure they meet GDPR's requirements for data protection and privacy.

GDPR enumerates the set of rights granted to individuals (“data subjects” in GDPR parlance) regarding their personal data. These rights include the right to be informed of how their personal data is used, the right to restrict the usage of their personal data, and the right to have their personal data erased from a company’s systems (aka “the right to be forgotten”).

GDPR also defines the relationships between business entities that manage personal data and the legal guidelines that govern their behavior. There are two types of entities that manage personal data: Data Controllers and Data Processors.

Data Controllers determine why and how personal data should be processed and are primarily responsible for ensuring the processing complies with GDPR. They are also responsible for managing data subject rights.

Data Processors process personal data on behalf of the controller. Data processors could be IT firms, payroll companies, or any other service provider that handles personal data. They have a legal obligation to protect personal data from misuse.

Some businesses, like Fullstory, may act as both a Data Controller and a Data Processor within the realm of GDPR compliance. For more details about Fullstory’s GDPR compliance can be found here.

California Consumer Privacy Act of 2018 (CCPA)

Both GDPR and CCPA aim to protect personal data. Whereas GDPR applies to all businesses that interact with EU residents regardless of where that business is located, CCPA applies only to businesses that operate in California. CCPA also provides other stipulations required for enforcement, such as businesses having annual gross revenues exceeding $25 million, buying or selling the personal information of 100,000 or more California residents or households, or earning more than half of their annual revenue from selling California residents’ personal information.

CCPA has a slightly different data focus than GDPR: whereas GDPR is focused on data about identified individuals, CCPA focuses on personal information which is data that could reasonably be linked with an individual or their household. Personal information includes names, social security numbers, email addresses, records of products purchased, internet browsing histories, geolocation data, fingerprints, and inferences from other personal information that could create a profile about an individual's preferences and characteristics.

Both GDPR and CCPA enumerate a set of rights granted to individuals about the processing of their data. There is some overlap (like “the right to be forgotten”) but generally speaking the scope of rights in GDPR is larger than CCPA, while CCPA contains a specific provision granting California residents the right to opt-out of having their data sold or shared between businesses.

Fullstory has published a privacy notice for Californians identifying the personal information we collect from our customers, in accordance with the CCPA: https://www.fullstory.com.pgm.c5nprx.cc/legal/ccpa/

Health Insurance Portability and Accountability Act (HIPAA)

HIPAA is a US federal law enacted in 1996 that establishes privacy standards to protect patients' medical records and other health information provided to health plans, doctors, hospitals, and other health care providers. Title II of the law specifically addresses the security and privacy of health data and creates regulations to protect Personal Health Information (PHI). The goal of HIPAA Title II is to define standards for securely exchanging digital healthcare information throughout the US healthcare system.

Any one of 18 PHI identifiers applied to healthcare or medical history data will render that data as PHI and thus it will fall under the purview of HIPAA compliance. These PHI identifiers include names, email addresses, and telephone numbers - all of which are data that is generally considered Personally Identifiable Information (PII).

HIPAA declares that a company that is hired to handle PHI is a Business Associate (BA). Under HIPAA, covered entities like healthcare providers and insurers must have contracts in place with their Business Associates, ensuring that they use and disclose PHI properly and safeguard it appropriately. This type of agreement is called a Business Associate Agreement (BAA) and it outlines how PHI will be safeguarded between two HIPAA-beholden organizations. Fullstory provides a BAA to support our Customers’ HIPAA obligations when using our services. More information about Fullstory’s BAA can be found here: https://help.fullstory.com.pgm.c5nprx.cc/hc/en-us/articles/17635706856599-Business-Associate-Agreement-BAA.

ISO 27701

ISO 27701 is a Privacy Information Management System (PIMS) standard that establishes guidelines intended to satisfy the regulations established through data privacy laws like GDPR and CCPA. These and other international data privacy laws define what is expected of organizations within their jurisdiction. ISO 27701 defines how organizations can meet the requirements of these laws. It defines a standard for data privacy controls that are necessary to protect PII in accordance with international law.

ISO 27701 is an extension of ISO 27001, an industry standard for information security management and data protection. An organization cannot be ISO 27701 certified without first being ISO 27001 certified. Any organization that is certified as compliant with both standards is demonstrating that they have the right people, processes, and tools in place to maximize data protection and data privacy for their customers.

Fullstory has also been certified as ISO 27018 compliant. This is a cloud-specific extension of ISO 27001 designed for public cloud service providers that handle PII.

Threats or challenges to data protection & privacy

Phishing, malware, and ransomware

These attacks use social engineering to attempt to trick your employees into either disclosing sensitive information like their company account login credentials or installing malicious software into your company IT systems.

Did you just get an email from Facebook to your work email address, asking you to change your password? Don’t click! This is likely a phishing attempt to get sensitive information from you.

Did you just get an email from Bank of America congratulating you on receiving an offer for a checking account with a $300 pre-seeded balance? Don’t click! You could accidentally install ransomware that shuts you out of your computer and spreads to other machines in your corporate network.

And don’t ever open attachments without verifying the sender first!

Phishing, malware, and ransomware can lead to unauthorized access to your data or to data loss.

Software vulnerabilities

Software vulnerabilities can be introduced in several ways, including:

Unpatched Operating Systems
Out of date dependencies on 3rd-party code libraries
Insecure coding practices like not sanitizing user input (which can lead to code injection attacks)

Misconfigured infrastructure

The resources that host code and store data are another area that can expose vulnerabilities if not managed well. Signs of misconfigured infrastructure include:

Storing data at rest in an unencrypted format
Leaving network ingress ports open to the public internet
Storing privileged access control keys outside of dedicated key stores

Popular checklists like the OWASP Top 10 for web security highlight the most common security risks that you need to be aware of.

You can find additional guidance for what a strong data protection operation looks like thanks to this guide written by Mark Stanislav, Fullstory’s VP of Security Engineering.

Leveraging advanced technologies for data privacy

Data privacy controls for behavioral data

Behavioral data analytics tools like Fullstory capture a variety of user experience data including clicks, mouse movements, display content, and form data. There may be some areas of a site or app that contain PII data. This data is not useful for the purposes of analytics and thus can be left out.

Fullstory has several ways to ensure that only data intended for behavioral analysis makes its way into our customers’ accounts:

Default data exclusion rules: Any password field, credit-card field or hidden input will be excluded from data capture, which means it will not leave users’ devices and will not be sent to Fullstory servers.
Configurable data masking and exclusion rules: Customers can selectively determine which elements in the user interface are either excluded or masked. Neither excluded data nor masked data will be sent to Fullstory; the difference is in how the element renders during session replay and in heatmaps in the Fullstory application.
Form privacy: No form data will be sent to Fullstory servers and all form inputs will either be masked or excluded.
Private by Default: All text content in the UI is masked, with the ability for customers to selectively unmask elements. Additional data masking and exclusion rules can be applied as needed.
Network data redaction: Field values are blocked in network request and response bodies by default and selected field values can be blocked from URL query strings. This ensures that any PII that might travel from a UI to a backend service is not captured by Fullstory.
Data capture with consent: Fullstory provides a consent API for web browsers and mobile apps that our customers may use to proceed with data capture only upon user consent.

More details about Fullstory’s data privacy capabilities can be found here.

Using AI to enhance data privacy and protection controls

New techniques that use Artificial Intelligence and Machine Learning are being developed to further safeguard user data from misuse and to help organizations manage data privacy more effectively.

Threat detection

AI can continuously monitor the data flowing through data management systems and instantly detect potential cybersecurity attacks. Data used in these attacks can then be automatically shut down or sequestered for further investigation. For example, AI algorithms can detect SQL injection and cross-site scripting (XSS) attacks by examining text inputs and URL parameters. Once detected, AI-Powered automation can determine the best course of action to shut down the attack.

In an effort to help our customers uncover suspicious activity, Fullstory provides a filter to search for suspicious activity in your data. XSS and SQL injection attacks can be analyzed in your behavioral data streams.

More information about Fullstory’s suspicious activity detection can be found here.

Data Loss Prevention (DLP)

DLP generally refers to reducing or eliminating the risk that data can leak out to places where it shouldn’t go. AI can detect data that looks unusual for a given context. For example: an algorithm may detect a credit card number in a data stream that is expected to only include first names. An AI-powered DLP tool continuously inspects data streams and then will automatically detect data that looks inappropriate and quarantine it for further inspection. People may then inspect this quarantined data to determine if it is safe to process or if it should forever be eliminated from processing.

All varieties of data can be found in a user’s browsing session on a web site or mobile app, but only a subset of that data is needed to analyze user experiences with behavioral data. Any captured data that is outside the scope of behavioral data is a candidate for DLP. Fullstory provides an AI-powered capability to automatically identify and categorize PII data originating from your web site or mobile app. Once detected, this data can then be triaged and further remediated.

Read more about Fullstory’s Detections capability.

Fraud detection

Any application that trades in goods & services or financial transactions is a target for fraud. Malicious actors, often using non-human agents (aka “bots”), will try to find ways to exploit these applications for financial gain.

Examples include:

Quickly wiring money out of compromised bank accounts.
Swarming a discount promotion to receive large quantities of goods at discounted prices, to be resold at a profit on secondary markets.

Behavioral data is rich with potential signals that can be used to determine if any given website or app user is a customer trying to accomplish a valid use-case, or a bot trying to game the system. Unsupervised machine learning models can be derived from this behavioral data, detecting anything unexpected about a particular user’s interaction time or user journey that might indicate their account has been compromised by a bot.

As more AI-Powered virtual assistants come online, behavioral data fidelity and model sophistication will become ever more important to distinguish between “bad” bots and “good” bots that represent digital surrogates of actual users executing valid use-cases. Fullstory’s Data Direct provides the detailed behavioral data that our customers rely on to build fraud detection models and distinguish good user traffic from bad user traffic. This data can enhance data protection strategies by providing deep insights into user interactions, allowing our customers to identify and mitigate risks before they escalate.

For more details, visit the Fullstory Help Center.

Fix friction before it starts: Key insights from health and fintech leaders

Peter Antoniou — Fri, 08 May 2026 06:00:00 GMT

I recently had the pleasure of hosting a fireside chat with two leaders who are currently operating at the intersection of high-stakes regulation and cutting-edge innovation: Stephen Murphy from Bupa UK and Richard Coombes from Equals Group.

We dove deep into how these organizations are leveraging behavioral insights to turn anonymous user data into actionable growth and ironclad security. In industries like health and finance, where trust is the only currency that matters, the move from reactive troubleshooting to proactive excellence isn't just an advantage—it's a requirement.

Here are the key takeaways from our conversation.

1. Driving unshakable customer loyalty

In both healthcare and finance, customer loyalty is built on a foundation of deep trust. But trust isn't built through transactions; it’s built through engagement.

Maximizing customer lifetime value requires a strategic focus on deep engagement. By investing in technology that merges qualitative and quantitative insights, Bupa connects the 'why' with the 'what.' This empowers the fostering of meaningful, lasting relationships rather than mere transactional interactions. This proactive, tech-driven strategy is the driving force behind the ambitious goal to achieve a lofty Net Promoter Score (NPS).

2. Transforming support into a proactive advantage

Traditionally, customer support begins when a user submits a ticket. That model is now obsolete. At Equals Group, Richard and his team are using real-time analytics to flip the script.

By leveraging platforms like Fullstory, they identify friction points—like rage clicks or payment errors—long before a customer ever picks up the phone. As Richard noted: "We can probably detect that an issue has happened without the customer contacting us." For mission-critical actions like payroll or supplier payments, this level of proactive detection doesn't just solve a problem; it eliminates churn before it starts.

3. Pioneering preventive healthcare through personalization

A significant transition is occurring as private healthcare moves from reactive treatments toward proactive wellness. The focus is shifting toward genomics and digital strategies to provide more tailored value to patients.

Healthcare providers are increasingly leveraging genome sequencing to deliver targeted treatments and preventative strategies. As Stephen noted, the same trends that drive high performance on the web—such as a personalized user experience—are now translating into private healthcare. This level of precision helps improve patient outcomes and allows organizations like Bupa to navigate complex regulations while delivering high-quality, customized care.

4. Bulletproof fraud prevention via real-time insights

In these highly regulated sectors, security is non-negotiable. But the friction required for security often kills the user experience.

Richard explained how Equals Group balances these two by monitoring user behavior in real time to spot anomalous patterns. By watching how bad actors navigate a product, the team can see what they investigate and, crucially, what scares them off. This real-time visibility allows AI and analytics to neutralize threats at scale without slowing down the legitimate user.

5. Empowering teams to drive innovation

The technical takeaways were vast, but the cultural takeaway was perhaps the most important: Democratize your tools.

Innovation doesn't happen in a vacuum. By giving curious, cross-functional teams access to cutting-edge behavioral tools, you accelerate problem-solving. Furthermore, Richard and Stephen both emphasized that regulatory constraints should never be an excuse for stagnation. By challenging the status quo and embracing AI, you can dramatically accelerate your time-to-market.

→ Unlock the full conversation and see these insights in action. Watch the webinar on demand here.

Unlock the full potential of Adobe AI with Fullstory

Lee Dallas — Wed, 06 May 2026 06:00:00 GMT

Recently, I had the privilege of attending and speaking at Adobe Summit for the first time. Fourteen thousand of my new best friends gathered in one place to hear the latest from this amazing brand. As someone who spent the better part of two decades building and advising on CMS deployments, it felt like home turf.

The demos and conversations I was part of reinforced a conclusion we at Fullstory came to last year: Every Adobe Digital Experience customer can get more out of their investment by combining it with Fullstory’s digital sight.

That conviction is what my colleague and I brought to the Summit stage.

Why your Adobe AI is blind (and how to give it sight)

AI is everywhere, and we’re all rushing to implement the latest intelligence into every aspect of our work lives. But in the midst of this hype, there is a glaring, fundamental problem: AI built on traditional analytics is rigid and static in an ever-more-dynamic world.

To become truly intelligent, predictive, and human-centered, AI needs digital sight. It needs a deep understanding of how real people behave, struggle, and succeed in their digital journeys.

Adobe Customer Journey Analytics (CJA) is a powerful platform. But traditional analytics, reliant on manual instrumentation, are incredibly labor-intensive and only capture the basics.

When you look at a funnel in CJA, you can easily spot where a massive drop-off occurs—say, between a summary page and checkout. But that data alone cannot tell you why.

Behind every drop-off is a human being experiencing friction—a network error, a broken modal, a page that simply didn't load. Where Adobe might capture 10 or 12 events from that session, the actual experience contained hundreds of signals. Offhandedly referring to these as "abandonments" ignores the fact that your systems have zero human context. Revealing an experience one event at a time is simply not enough for modern AI.

Fullcapture: The optical nerve

That’s where Fullstory’s Fullcapture™ comes in. Instead of guessing what tags to create, Fullcapture automatically captures all user interactions without tagging. It acts as a high-resolution optical nerve for your digital experience, capturing every click, input, navigation, network call, and frustration signal.

Without that connection, your AI is processing in the dark. With it, every downstream agent, workflow, and model has access to the full picture of human behavior—not a handful of pre-tagged events, but the complete experience.

Closing the loop for Adobe users

So, how does this actually add value to your existing Adobe stack? It fundamentally shifts your posture from reactive reporting to proactive insight.

Actioned insight in CJA

Reporting is, by definition, reactive. You open CJA, spot a significant drop-off between two steps in a critical funnel, and then the investigation begins. Normally, that's where the story stalls; you know something is wrong, but you can't determine why from the data alone.

With Fullstory integrated into CJA, you can click directly from any funnel step into the actual user sessions behind that drop-off. StoryAI analyzes every event in the session and surfaces the root cause. It can identify a failing API request triggering an error modal, then build a segment showing exactly how many users encountered the same issue.

But you don't have to go looking for problems. Story AI Opportunities is an AI-powered anomaly detector that continuously monitors your critical funnels across hundreds of dimensions. It flags issues as they emerge, shows you when they started trending, measures the conversion impact, and gives you diagnostics down to the device, browser, and IP level. When you're ready to act, assign it to your team and push it directly into Jira.

And getting that new signal back into CJA doesn't require an engineering sprint. Fullstory's activation streams let you define a new behavioral event and push it into your Adobe schema. Your funnel doesn't just show the drop-off anymore. It shows the cause.

Hyper-personalized campaigns

Traditional remarketing sends everyone the same generic "you left something behind" email. A customer browses three product pages and abandons. Adobe knows which campaign brought them in and that they saw three pages. That's about it.

With Fullstory's integration into Adobe Campaign and Adobe Journey Optimizer (AJO), our AI reads the abandoned session and outputs a deterministic, structured JSON payload directly to Adobe.

This is powered by Fullstory's Session Summary API, which uses summary profiles—hyper-specific instructions that control exactly what data gets extracted from a session. Which product held the user's attention longest. Their frustration level. The recommended email tone. Whether a discount makes sense, and if so, how much.

Adobe reads the payload, and the email practically writes itself. This allows your marketing engine to send hyper-personalized messages that empathetically acknowledge the exact friction or frustration the user experienced.

Get more from your Adobe investment with Fullstory

Stretching your capabilities is important. But you cannot stretch into the era of intelligent digital experiences if your systems have no memory of the human context. By plugging Fullstory’s digital sight directly into the Adobe ecosystem, you’ll collapse analysis from hours to seconds, and give your AI the human feedback loop it desperately needs to succeed.

→ Request a demo to see how Fullstory helps businesses like yours.

What financial services leaders should demand from analytics

Ricky Page — Mon, 04 May 2026 06:00:00 GMT

It's easy to feel a false sense of confidence because dashboards are live and reports are flowing. But in regulated environments, fragmented transparency creates more risk than clarity.

Across enterprise financial services, analytics stacks have typically evolved tool by tool. Web, mobile, assisted channels, and privacy controls frequently sit in separate systems. The result is blind spots, inconsistent governance, and competing interpretations of customer behavior.

Because most banking interactions are now digital, experience quality directly shapes trust and retention. At the same time, regulatory scrutiny continues to intensify. Any gap between what customers experience and what you can evidence is now material.

Digital transparency, privacy compliance, and regulatory defensibility must be treated as one connected system—and evaluated as such.

Clarity data protection and regulatory standards work together, not separately

In recent years, the regulatory landscape for financial services has evolved significantly, emphasizing consumer protection and transparency. In the UK, the FCA's Consumer Duty sets a benchmark for financial firms, requiring them to prioritize consumer interests and demonstrate positive outcomes throughout the entire product lifecycle. This framework highlights the importance of not only compliance but also genuine transparency in customer interactions.

In regulated digital environments, clarity, privacy, and compliance are interdependent constraints. Firms must be able to evidence compliance, making digital transparency and governance central conduct considerations rather than technical afterthoughts.

Without true end-to-end visibility across web and mobile journeys, organizations can't prove whether experiences actually work or support good outcomes. This weakens governance, challenges monitoring expectations, and reduces defensibility under board and regulatory scrutiny.

Privacy must be embedded at the point of capture, not retrofitted in reporting. When masking, consent, and data minimization controls differ across systems, fragmentation becomes a material risk, particularly in high-sensitivity journeys such as payments and personal data updates.

Data silos compound this risk. When teams rely on conflicting datasets to assess conversion, straight-through processing, foreseeable harm, or complaint root causes, time is spent reconciling numbers instead of fixing friction. Full-journey transparency is therefore foundational not only to organizational confidence and accountability, but also to evidencing ongoing compliance with Consumer Duty outcomes monitoring and governance requirements.

Tradeoffs when combining analytics tools into one system

Most large banks didn't design their analytics estates holistically; they accumulated specialized tools over time.

Point solutions may appear strong in isolation—web analytics for acquisition, mobile tools for crash reporting, separate consent platforms, and niche tools for payments or deposits. Risk emerges when governance models and privacy controls differ across onboarding, lending, payments, and servicing.

Each additional SDK or data feed increases complexity and fragility. Manual tagging and retrospective fixes slow time-to-value and introduce risk, especially when tracking high-volume financial events.

Customer expectations continue to rise, yet fragmented analytics obscures friction between digital and assisted journeys—for example, when a failed online application triggers a call center interaction that is not reconciled in the same dataset.

Common pitfalls are predictable: mistaking dashboard coverage for full visibility, assuming privacy settings carry across systems, and underestimating change resistance in regulated models. These are structural issues with conduct and operational risk implications.

Building executive alignment around behavioral data strategy

Technology decisions rarely fail on capability alone; they fail on misalignment. A successful behavioral data strategy requires clear executive sponsorship and shared ownership across risk, compliance, product, and data teams.

To achieve that executive alignment and shared ownership:

Define measurable objectives at the outset. Whether the priority is reducing fraud losses, improving onboarding conversion, optimizing mortgage completion, or increasing retention, clarity of intent prevents tools from drifting into isolated experimentation.
Create shared indicators that link behavioral insights to outcomes such as reduced deposit drop-off, improved straight-through processing, lower cost-to-serve, or earlier detection of anomalous transactions. When leadership reviews the same dashboards and MI, decision-making becomes evidence-led.
Agree on governance frameworks early, with accountability for data quality, privacy controls, and compliance sign-off. This ensures innovation strengthens rather than compromises regulatory posture.

A structure for assessing digital data tools in financial services

Incremental analytics upgrades may feel productive, but can compound long-term governance risk.

Focus on four criteria:

Comprehensive visibility: Ensure tools provide a complete view across customer journeys, from onboarding through transactions. This visibility is critical for identifying areas of risk and improving customer outcomes.
Privacy-by-design: Data capture processes should embed privacy measures right from the start. This is essential for compliance with regulations and for building customer trust.
Seamless integration: Tools must integrate effortlessly with existing tech stacks. Effective integration helps ensure data consistency and reduces friction, which enhances user acceptance and operational efficiency.
Cross-functional enablement: Utilize shared datasets across departments to create a unified understanding of customer behavior. By aligning insights with organizational goals, teams can collaborate effectively to enhance decision-making.

Unifying visibility, privacy, and compliance into a competitive advantage

Financial institutions must prioritize end-to-end visibility, privacy-by-design, and deep integration to transition from dashboards to defensible evidence. This comprehensive approach enables firms to identify potential issues, assess value propositions, and enhance customer understanding. By focusing on these strategies, teams can improve onboarding experiences, reduce drop-offs, and proactively manage customer risk.

The outcome is faster issue resolution, clearer board reporting, and stronger accountability, all contributing to consistent customer interactions. Institutions equipped with well-governed, audit-ready data can effectively manage risks and foster innovation, gaining a competitive edge in a financial services landscape marked by heightened expectations for conduct and integrity.

In particular, with regulatory frameworks like the FCA's Consumer Duty emphasizing the need for transparent outcomes and consumer protections, organizations that successfully integrate visibility, privacy, and compliance not only meet regulatory demands but also build deeper trust with their customers. That, in my view, is where sustainable advantage now lies.

→ Ready to see how Fullstory helps financial services organizations like yours? Watch a demo here.

What is user behavior analytics (UBA)?

The Fullstory Team — Tue, 18 Jun 2024 12:00:00 GMT

Ever wondered what truly drives your users' actions on your website or app?

Understanding these interactions is key to enhancing their experience, boosting engagement, and ensuring security. User behavior analytics (UBA) dives deep into the patterns of user activity, offering insights that reveal what your customers are doing and why. By analyzing this data, you can tailor your services to meet their needs better and proactively identify potential security threats, ensuring a smoother and safer user experience.

UBA isn't just about tracking clicks and movements—it's about painting a full picture of user actions and preferences.This can lead to higher customer satisfaction, retention, and improved security, as every interaction becomes an opportunity to learn and improve.

UBA plays a crucial role in identifying potential threats on the security front. Monitoring deviations from normal trends in user behavior helps spot suspicious activities that might pose risks. This proactive approach helps protect user data and maintain the trustworthiness of your platform.

Key takeaways

UBA enhances security with essential data for analyzing and identifying patterns in user activities that could be potential threats.
UBA can also provide insights into user interactions, helping improve navigation, identify popular features, and enhance overall user satisfaction.
Advancements in AI and machine learning, along with a focus on user privacy and new applications, are rapidly evolving UBA's capabilities.

What is user behavior analytics (UBA)?

User behavior analytics (UBA), or user and entity behavior analytics, involves analyzing user activities to achieve specific goals. While traditionally used to enhance security by spotting potential threats, UBA also provides valuable insights into how users interact with your products.

UBA applications in security

User Behavioral Analytics (UBA) is a game-changer that enhances cybersecurity. By monitoring user activities, UBA tools can detect insider threats, improve incident responses, and support multi-factor authentication (MFA). Here's how:

Detect insider threats and anomalous behavior

UBA is essential for spotting insider threats and unusual activities, a cornerstone of effective website security.

Example: If an employee starts accessing sensitive files they usually don’t, and UBA will flag this unusual behavior. Recognizing such anomalies helps secure data before any damage occurs.

Behavioral analytics tools use machine learning to understand typical user behavior. This real-time threat detection ensures swift responses, which is especially useful in large organizations where manually spotting anomalies is challenging.

Enhance security visibility and incident response

UBA significantly boosts your security visibility and incident response capabilities. By continuously monitoring user activities, it provides insights into security events and potential breaches.

Example: UBA can track user actions across networks and devices, helping identify security breaches early. This detailed monitoring allows for quick reaction times. Integrating UBA with Security Information and Event Management (SIEM) systems further enhances this visibility.

Behavioral analytics tools generate alerts when abnormal behavior is detected, helping the security team address issues swiftly. This proactive monitoring reduces the impact of security incidents.

Multi-factor authentication

Implementing multi-factor authentication (MFA) with UBA strengthens security even further. UBA analyzes user behavior to verify identities and flag suspicious login attempts.

Example: If login attempts occur from an unusual location or at odd hours, UBA will prompt for additional authentication steps. This ensures that access is granted only to verified users.

Combining UBA with MFA reduces the risk of credential-based attacks. By scrutinizing user activities, it can prevent unauthorized access even if passwords are compromised. This integration provides a robust layer of protection for sensitive information.

UBA applications in product analytics

User behavior analytics can also profoundly impact your product's success. Understanding how users navigate and engage with your app or website helps drive data-driven decisions for product improvement and user satisfaction.

Analyzing mobile app user behavior

Mobile analytics provides valuable insights into user engagement by examining how users interact with your app.

Example: Tracking navigation patterns reveals which features are most popular, which pages users spend the most time on, and where they encounter issues. If many users drop off at a specific stage during sign-up, it's a sign that the process needs simplification.

Cohort analysis allows you to group users based on shared characteristics, such as the date they signed up or their usage frequency. This helps identify trends and user needs over time.

Product improvement

Utilizing user behavior analytics enables continuous product enhancement. By gathering user feedback, you can make informed product development decisions.

Example: User feedback can highlight missing features or areas needing improvement. A/B testing helps you compare different versions of a feature to see which performs better, ensuring that changes positively impact user engagement.

Analyzing user behavior leads to identifying bottlenecks and friction points within the app or website. This insight is used to streamline navigation and improve the overall user experience.

Combining these analytics approaches influences your product strategy, ensuring that updates and new features align with user needs and preferences. By focusing on real user data, you create an effective and user-friendly product.

Future trends in user behavior analytics

User behavior analytics are evolving rapidly, driven by advancements in technology and a growing focus on user privacy and data security. Here are some key trends to watch:

Advancements in machine learning and AI

Faster insights: Machine learning and AI process massive amounts of data quickly and accurately, helping you make better decisions faster. AI algorithms can identify patterns in user actions and predict future behavior.
Personalization: Understanding user preferences allows you to tailor experiences to meet their needs, keeping them engaged and boosting business outcomes.
Real-time analytics: Instant feedback on user interactions helps you make timely adjustments to improve user experience.

The growing importance of user privacy and data security

Data responsibility: New regulations like GDPR and CCPA require responsible data handling, protecting your users, and building trust.
Transparency: Clearly communicate what data you're collecting and why. Give users control over their information to comply with legal requirements and show respect for their privacy.
Enhanced security measures: Use encryption and secure servers to protect data from breaches and regularly update security protocols to avoid potential threats.

Emerging applications and innovations

Improved customer support: Use insights from user behavior analytics to understand common pain points and address issues before they escalate.
Behavioral data in marketing: Analyze user interactions to create more effective campaigns, leading to higher conversion rates and better ROI.
Predictive analytics: Innovations can forecast user actions, helping you stay ahead of trends and proactively meet user needs. Teams use UBA to predict customer churn, identifying the behavioral patterns that precede cancellation long before aggregate metrics catch up.

Maximizing the potential of analyzing user behavior data

UBA is a powerful tool that enhances security and improves user experience. UBA helps identify patterns and potential threats by analyzing user activities, ensuring your platform remains secure. It also provides invaluable insights into how users interact with your products, enabling data-driven decisions that boost engagement and satisfaction.

As UBA evolves with advancements in AI and machine learning, alongside a growing focus on user privacy and new applications, it continues to be an essential component for businesses. Embracing UBA safeguards your data and enhances the overall user experience, driving long-term success.

What is behavioral data & why is it important?

Fullstory — Wed, 28 Feb 2024 07:00:00 GMT

Key Takeaways

Complete, accurate, and structured behavioral data is valuable for improving customer experience (CX). It can provide insights into customer interactions, preferences, and habits through various activities like website visits, social engagements, and purchase history.
It's collected from sources like CRM systems, websites, mobile apps, and social media platforms. It is categorized into first-party, second-party, and third-party data, each with its own benefits and uses.
Utilizing behavioral data enables businesses to personalize marketing campaigns, identify and optimize customer journey friction points, develop forecasting and digital benchmarks, and boost engagement, although it must be managed with stringent privacy and ethical considerations.

What is behavioral data?

Behavioral data describes how customers, applications, or systems interact with your business: page views, sign-ups, clicks, logins, purchases, calls, or even user frustrations can be captured via websites, apps, email, social media, CRM, and sensors. These context-rich signals power personalization, advanced analytics, and AI-driven predictions.

Alongside demographic data, this data is generated by user interactions online, such as:

website views
newsletter sign-ups
shopping cart activities
sentiment signals, like rage clicks
social media engagements

It’s like a digital breadcrumb trail that customers leave behind, revealing their preferences and habits and allowing businesses to significantly improve the value of their existing systems, such as analytics, personalization, product forecasting, A/B testing, and marketing automation.

Where is behavioral data collected?

Businesses leverage a variety of digital platforms and tools to gain a comprehensive understanding of customer behavior. These systems not only track user actions but also provide valuable insights into preferences and experiences.

Marketing automation and CRM systems: Collect data on user interactions, purchase history, and preferences.
Websites and mobile apps: Utilize pixels and tracking technologies to capture browsing behaviors and user engagement metrics.
Billing systems: Reveal customer payment patterns and preferences, adding a financial dimension to user behavioral data.
Call centers: Offer direct insights into customer inquiries and service experiences.
Social media and surveys: Enrich understanding by capturing user preferences and feedback directly from the source.

Types of behavioral data

Three main types of behavioral data exist, each with a unique role in decoding customer behavior:

First-party data: Collected directly from customers, offering unparalleled reliability and insight into customer behaviors, preferences, and actions. This builds a foundation for personalized marketing and customer experience enhancement derived from direct interactions like purchases, abandoned carts, and website visits.

Examples of first-party data:

Website clicks
App usage time
CRM data

Second-party data: Acquired through partnerships, second-party data is another company's first-party data shared or sold for mutual benefit. This data extends marketing reach by accessing insights from similar customer bases, therefore enriching understanding without direct collection.

Examples of second-party data:

Data purchases
Partnership data
Industry consortiums

Third-party data: Sourced from external aggregators or brokers, this data provides a broad view of market trends and consumer behavior across various sources. It is valuable for broad market analysis and identifying general trends, albeit less directly relevant to individual customer actions.

Examples of third-party data:

Demographic information
Behavioral patterns across various platforms
Purchase history from external vendors

Behavioral analytics tools turn data into action

Tools like Fullstory and Google Analytics function as the core components in the machinery of data-driven decision-making, facilitating behavioral analytics.

Google Analytics tracks user behavior across devices, offering detailed reports.

Fullstory deciphers hidden buying behaviors, equipping data teams to boost revenue with immediate personalization and predictive analysis. It surfaces the sentiment behind the clicks, aiding businesses in crafting superior experiences that secure lifelong loyalty.

These platforms specialize in providing insights into conversion, engagement, and retention without complex queries. Behavioral analytics tools employ advanced methods like cohort and funnel analysis to study user retention and conversion paths, enhancing customer experiences by combining qualitative feedback with quantitative data and tailoring offerings based on user activity metrics.

Behavioral data in marketing automation systems

Marketing automation systems utilize behavioral data as their powerhouse to craft personalized experiences for customers.

They trigger actions automatically following specific behaviors, thereby creating a seamless and personalized customer journey. By providing consistent messaging across multiple channels, these systems ensure a coherent brand presence, playing a vital role in customer acquisition in today’s multi-channel environment.

Behavioral data is instrumental in achieving business goals such as new customer acquisition, user retention, and minimizing churn rates. It builds trust and grows conversion rates.

Personalization campaigns that analyze customer behavior data like browsing history and purchase patterns allow marketers to send messages tailored to individual customer preferences. At the same time, segmentation enables more targeted and effective marketing strategies. This approach aligns closely with broader marketing analytics practices, enhancing overall campaign effectiveness.

Enhance user journey using customer data

Enhancing the customer journey stands out as a key application of behavioral data. It involves using personalization strategies, identifying friction points, and boosting user engagement to improve overall customer experience.

Let’s dive a bit deeper.

Personalization strategies

Personalization is no longer a luxury but a necessity.

Customers expect brands to understand their preferences and buying habits, and deliver personalized experiences. Organizations leverage behavioral data to understand customer behavior and create targeted segments, enhancing personalized marketing campaigns and improving customer experience.

Take Coca-Cola’s use of image recognition for targeted ads as an example.

This strategy has succeeded in increasing engagement, cautioning against over-personalization, and emphasizing the importance of allowing opt-out from personalized experiences. Product recommendations tailored to customers based on their past behaviors exemplify an effective personalization tactic contributing to increased interest and potentially higher sales.

Identifying friction points

Potential friction points, areas causing discomfort or frustration, are inherent in every customer journey. By analyzing behavioral data and surfacing the customer sentiment behind the clicks, businesses can work towards optimizing these friction points.

Behavioral data platforms like Fullstory offer the following benefits:

Collect data on user behavior patterns
Identify and alert about user friction in a user experience
Improve customer satisfaction and engagement
Lead to a more successful customer journey

Boosting user engagement

Boosting user engagement is a critical goal for many businesses, and behavioral data is often the missing link that breaks down the barrier to success.

Heatmap software, for instance, provides visual illustrations of user behavior on a website. These illustrations are critical in identifying areas that receive the most engagement and those that are ignored.

Feedback and voice of the customer tools offer insights that may not be apparent through quantitative data alone. Constantly enhancing the user experience based on insights from behavioral analytics plays a key role in fostering user loyalty and improving engagement.

Privacy and ethical considerations in behavioral data collection

As is true for any form of data collection, gathering customer behavior data carries its own privacy and ethical considerations. Behavioral tracking is governed by stringent regulations such as GDPR in Europe, CCPA in California, and PIPEDA in Canada, which mandate explicit consumer consent and the protection of personal information.

Around 40% of consumers lack trust in how brands handle their data, underscoring the urgent need for complete transparency and ethical practices in the use of behavioral data to prevent brand trust loss.

Practices like data manipulation or lack of transparency can lead to severe outcomes, including legal repercussions and fines, as well as a tarnished brand reputation.

Examples of businesses using behavioral data

To fully grasp the potential of behavioral data, let's consider some real-world examples.

Organizations leveraging behavioral data analytics see 85% more sales growth and a more than 25% increase in gross margin. By analyzing data, they can make informed decisions and optimize their strategies.

McDonald’s, for instance, has used behavioral data acquired from Dynamic Yield to optimize their high-volume business, effectively increasing customer satisfaction and sales.

DBS Bank’s investment in technology, including AI and data analytics, has transformed it into a data-driven organization, significantly improving the user experience.

Starbucks and Netflix have also benefited from the strategic use of behavioral data to tailor offers, shape product development, and strengthen customer relationships.

Applying behavioral data

Behavioral data unlocks the power of true personalization by revealing valuable insights into customer sentiment patterns.

By understanding your consumers' personalities through their actions, you can tailor your strategies to not just meet but match their moods, making every interaction deeply personalized and effective.

Improve customer engagement

Businesses use behavioral data to understand customer patterns such as page views and interactions. By analyzing how customers engage with digital platforms, companies can create personalized experiences.

Tools like recommendation engines leverage this data to suggest products, enhancing the customer's journey and interaction with the brand.

Data-driven product development

Insights into customer behavior significantly enhance product development. Companies leverage feedback and purchase patterns to refine new features or products. Integrating AI with a behavioral data platform further sharpens this targeted approach.

AI engines process and analyze data more deeply, revealing trends and sentiments that inform development strategies. This ensures efforts are not just aligned with, but also predictive of, actual customer preferences and needs, making development more responsive and tailored.

Optimize sales and marketing

Sales and marketing campaigns are tailored using predictive lead scoring and segmentation based on customer activity. This targeted approach can result in more effective campaigns and higher conversion rates. Marketing automation systems utilize behavioral data to fine-tune advertising and lead-generation strategies.

Enhance customer retention and loyalty

The analysis of customer interactions and purchase history informs strategies to increase lifetime value, and reduce and predict customer churn. Personalized email sign-ups and newsletter subscriptions are examples of how a Customer Data Platform (CDP) can foster loyalty and encourage repeat business.

By strategically applying behavioral data across various facets of a business, companies can enhance customer experiences, resulting in improved engagement, more efficient product development, and increased sales and customer loyalty.

Maximize impact with behavioral data

Behavioral data is pivotal today. It reveals customer preferences and guides marketing strategies. It enhances customer journeys and campaign effectiveness while upholding ethical standards.

Leveraging this data effectively drives informed decisions and business success.

How financial services teams deflect tickets and provide proactive support

Peter Antoniou — Mon, 27 Apr 2026 07:00:00 GMT

As a solutions consultant who has partnered with many leading financial services companies, I've seen a common thread: the relentless pressure on support teams.

When digital experiences break down, customers stop what they're doing and ask for help. Instead of completing applications, submitting documentation, or progressing through claims, they're stuck waiting for support. The result is a compounding problem: frustrated customers create tickets, support teams become overwhelmed, response times slow, and, all the while, customers continue running into the same unresolved issue, so the ticket backlog grows.

Luckily, that exhausting chain of events isn’t inevitable.

Fullstory helps financial services institutions detect friction before it becomes a wide-scale issue, improve chatbot experiences with behavioral data, and give support agents the context they need to help users faster.

The key takeaways

Proactive beats reactive: Waiting for tickets means problems have already impacted your users. Proactive support surfaces issues as they happen, so teams can step in early and prevent poor experiences from spreading.
Reliance on tickets leaves blind spots: Depending solely on support tickets provides an incomplete picture of user issues, as many customers abandon tasks without ever reporting the friction they encounter.
AI-powered session summaries enhance resolution speed: Instant, contextual summaries in support tools like Zendesk lead to increased efficiency, reducing average ticket resolution time by over 30%.
Proactive detection prevents scale issues: Fullstory enables teams to identify friction, like rage clicks or repeated form errors, in real time across every user session, allowing them to fix problems before they become widespread.
Context makes chatbots effective: By injecting behavioral data into chatbots, teams can transform them from simple FAQ systems into context-aware tools that understand a user's specific struggle, increasing ticket deflection rates.
Visuals speed up resolution: Tools like Session Replay and Fullcapture eliminate the need for back-and-forth troubleshooting by giving support agents a step-by-step view of the exact errors and actions that preceded a support request.

Proactive vs. reactive support

Traditional support models rely on customers to raise their hands when something goes wrong—but by then, frustration has already set in. The longer issues go undetected, the more users abandon key tasks like applications, claims, or onboarding.

A proactive approach flips that script.

Rather than responding to tickets after friction has already impacted the customer experience, proactive support gives your team real-time visibility into what’s happening. You can identify patterns of struggle—like repeated form errors, rage clicks, or drop-offs—as they happen, and take action before customers ever reach out (or give up).

Behavioral data also helps teams predict customer churn before it reaches that point, spotting the friction patterns that precede cancellation weeks before any customer opens a ticket.

Moving from reactive to proactive may sound like a massive undertaking, but Fullstory makes it simple.

Detect friction before your customers report it

Tickets give customer support teams only a partial picture. The information submitted often includes unclear descriptions, missing steps, or incomplete details about what went wrong. Plus, for every customer who reports an issue, many more will simply abandon the task and move on, without ever reaching out.

If your team only knows what’s broken based on ticket submissions, you‘re probably missing a lot of moments where critical flows silently fail or where friction causes repeated drop-offs.

To address problems more effectively, teams need visibility into friction as it occurs. They need to know when a step within a customer journey is underperforming, whether the problem is isolated or systemic, and what users are doing in response.

Fullstory gives financial services teams this level of insight, capturing complete behavioral data across every user session and providing a detailed view of where customers are struggling—even if they never file a ticket. Real-time signals like rage clicks, repeated form errors, or sudden drop-offs help identify problems as they emerge. Teams can set alerts based on key behaviors or thresholds, so teams are notified the moment something goes wrong.

Instead of waiting for reports to accumulate, teams can step in early and prevent the user experience from breaking down at scale.

Deflect tickets with smarter AI-powered chatbots

Many support teams rely on chatbots as their first line of defense. They’re fast, scalable, and always on, making them a natural fit for handling repetitive questions and reducing the burden on live agents. However, many chatbots historically lacked the necessary context to be truly effective. “Glorified FAQ bots” is a phrase I’ve heard to describe these tools many times, and in honesty, I’ve agreed with it—until now.

To address this, organizations can take advantage of Fullstory’s ability to stream in-session data within 2-5 seconds. Streaming this context to AI chatbots like Intercom Fin dramatically enhances their responses and their capacity to resolve issues in real time. By augmenting human support with these autonomous tools, teams can focus on handling more complex issues, fostering higher levels of customer service.

One standout capability that I’ve seen have the most impact for organizations is Fullstory’s ability to stream in-session data within 2-5 seconds. Streaming this context to AI chatbots like Intercom Fin dramatically improves their responses, and most importantly, their ability to solve issues and convert customers in the moment. By augmenting human support with these autonomous tools, teams can focus on more complex issues and provide even higher levels of service. And that’s on top of the ultimate benefit: A smoother, more intuitive customer experience that fosters loyalty and satisfaction.

Give support agents context to resolve issues faster

Of course, some issues will still be escalated to your support team. Too often, support teams are forced to piece together what went wrong based on vague descriptions, screenshots, or long threads of back-and-forth troubleshooting. This takes time, adds friction on both sides, and slows down resolution.

Fullstory eliminates that guesswork. With features like Session Replay and Fullcapture™, support agents get a clear, step-by-step view of what the user experienced. They can see what was clicked, which errors appeared, where the user retried an action, and what happened just before they reached out for help.

Key moments and session summaries can even be pushed directly into tools like Zendesk. So when a ticket hits the queue, your team already knows what happened and can focus on fixing it.

Real-life results: The ROI of enhanced support systems

The organizations I work with see tangible ROI from building proactive support systems. Some of the most impactful outcomes include:

Increased ticket deflection: By injecting behavioral insights into tools like Fin to identify and resolve frustrating user experiences, organizations have dramatically reduced the number of support tickets created, leading to a significantly higher ticket deflection rate.
Improved first contact resolution: When tickets are created, agents have all the information they need to solve the issue on the first touchpoint. I've seen first contact resolution rates jump by 25-30% in a matter of months.
Reduced discovery time: Thanks to session replays, agents spend minutes, not hours, on discovery. This leads to a significant drop in average handle time and allows teams to handle more complex issues
Enhanced issue reproduction: The ability to view past interactions means that frustrating and costly tickets, where agents previously struggled to reproduce issues, can now be addressed effectively. With Fullstory, if an issue occurred, it’s visible and understandable through contextual AI-powered summaries.
Elevated customer satisfaction: Many of the organizations I work with see noticeable improvements in customer satisfaction (CSAT) scores because faster, smarter, more proactive support is the key to delighting customers. And the cherry on top? Happier customers are more loyal and have a higher lifetime value

TBC Bank builds trust and drives efficiency with Fullstory

The challenge:

TBC Bank’s dashboard was complex and scroll-heavy, and many visitors never scrolled beyond the first viewport. They also had a time-consuming onboarding process that created friction for new users.

The solution:

Using Fullstory's behavioral data, the organization identified exactly where customers were running into friction. This insight powered a dashboard redesign with banking personalization features and a streamlined onboarding flow.

The results?

145% increase in conversion rate
81% reduction in time to task
3x faster onboarding flow

Read the full story →

Provide proactive customer support with Fullstory

When your team has to wait for a complaint to know something’s wrong, you’re already behind. But when you can see friction as it’s happening, respond with context, and continuously improve the underlying experience, you put your team in control and your customers back in motion.

That’s what Fullstory enables: a support model that’s proactive, self-service-friendly, and grounded in the deep insights derived from advanced fintech analytics. The result is less churn, faster journeys, and a support team that’s solving real problems instead of chasing symptoms.

Ready to raise the bar? Start with a demo and learn how Fullstory helps financial services organizations like yours.

Predicting customer behavior with data

The Fullstory Team — Wed, 17 Apr 2024 11:00:00 GMT

What if you could predict every customer's next move, anticipate their needs, and deliver exactly what they're looking for before they even ask? This is not the plot of a futuristic novel but the reality of today's competitive marketplace.

By understanding and anticipating customer preferences and actions, businesses can enhance engagement, supercharge marketing efforts, and significantly improve sales.

AI and machine learning are largely involved in predicting customer behavior. By analyzing patterns in past purchases and interactions, businesses can uncover valuable insights into customers' preferences and use that information for more effective marketing campaigns.

Insights enable businesses to tailor their services and products to align with their customers’ needs, ultimately driving customer loyalty.

Key takeaways

Customer behavior isn’t random. With the right data, it can be predicted, modeled, and influenced in real-time.
Behavioral data is the fuel. Clicks, scrolls, hovers, drop-offs, and even text feedback become signals that AI can interpret.
Prediction means action. It’s not just about knowing what customers might do; it’s about triggering the right campaign, offer, or intervention at the right time.
The payoff is loyalty and growth. Brands that anticipate needs and reduce friction win more repeat customers and long-term value.

What is customer behavior?

Customer behavior is the study of how people make decisions and take actions related to their consumption of products or services. It aims to understand the various patterns and trends that emerge as individuals interact with the market and make purchasing choices.

At its core, customer behavior involves researching and analyzing consumers' actions to gain insights into their preferences, needs, and desires. Businesses can then use this information to design effective marketing approaches, improve customer experience, and drive sales.

Businesses often turn to various research methodologies to analyze customer behavior effectively. These may include quantitative approaches like surveys and data analysis and qualitative methods like interviews and focus groups.

By gathering and analyzing data, companies can create more targeted marketing campaigns, develop products that meet consumer needs, and predict future behaviors.

Factors that influence customer behavior

Now that we've discussed the basics of customer behavior, you might be asking: What actually influences these behaviors?

Let's explore the diverse factors shaping consumers' decisions.

Demographic factors

Demographic factors are pivotal in grasping customer behavior, where aspects such as age, gender, occupation, and education deeply impact customers' purchasing decisions.

For example, a person's income and financial status can dictate their spending habits and preferences.

Psychographic factors

Psychographic factors explore an individual's personality, values, interests, and lifestyle, which are crucial in determining their preferred products or services.

For example, a person's commitment to environmental sustainability might lead them to favor eco-friendly brands.

Social factors

Social factors significantly influence customer behavior. The views and actions of family, friends, social media, and the wider society can shape how decisions are made.

For instance, social media trends can drive consumer interest in specific products or brands.

Cultural factors

Culture, including language, values, norms, and traditions, plays a key role in consumer behavior. People from different backgrounds may have unique preferences influenced by their cultural upbringing.

For example, dietary preferences influenced by cultural practices can determine food product choices.

Economic factors

Economic conditions are critical in affecting customer behavior. Factors such as individual income, the overall economy's health, inflation, and taxation influence spending habits and product preferences.

For example, economic growth can lead to increased spending on non-essential items, while economic recessions might see a rise in demand for basic necessities.

Types of customer behavior

When it comes to what consumers decide to buy, a lot hinges on the product or service itself. Factors like price, how easy a product or service is to get, and how often they buy it matter.

And here's the thing: how much shoppers get involved in buying depends on these factors. Also, how risky the purchase feels plays a big part.

Generally, the more something costs, the bigger the perceived risk, and the more thought and effort people put into deciding whether to buy it. This careful consideration directly influences their buying behavior.

There are 4 main types of customer behavior:

1. Complex buying behavior

Complex buying behavior occurs when customers invest significant time and effort in evaluating products before making a purchase. High-involvement products, such as cars or expensive electronics, often trigger this type of behavior.

Consumers engage in extensive research, comparing product features, prices, and reviews to ensure they make the best decision. Companies can leverage this understanding by focusing on providing detailed product information and emphasizing how their offering stands out against the competition.

2. Dissonance-reducing buying behavior

Dissonance-reducing buying behavior takes place when customers experience post-purchase anxiety or uncertainty about their decision. This can arise when consumers feel that they had to make a decision quickly, without sufficient time to weigh the pros and cons, or if their choice was informed by limited information.

To minimize such dissonance, marketing efforts should emphasize elements of reassurance. For example, companies can:

Offer clear return and exchange policies
Display positive customer reviews
Provide easily accessible customer support or AI support

3. Habitual buying behavior

Habitual buying behavior is characterized by consumers relying on routines and habits when making purchasing decisions. This type of behavior is commonly found in less involved product categories, such as groceries or personal care items, where consumers are not as inclined to research products extensively before purchase.

Consequently, companies should focus on building brand recognition and loyalty and creating memorable advertising to entice customers.

Some strategies include:

Consistent branding and packaging
Implementing loyalty programs or offering regular discounts
Utilizing eye-catching promotion strategies

4. Variety-seeking buying behavior

Variety-seeking buying behavior arises when customers actively seek new experiences, products, or brands, even if satisfied with their current choices. This behavior typically occurs in categories where products are low-involvement, low-cost commodities, and consumers feel minimal risk in trying new options.

Companies should capitalize on this by frequently introducing variations or limited-time offers. Techniques to engage variety-seeking consumers can be:

Launching seasonal or unique-flavored products
Rotating promotional offers
Collaborating with other brands for co-branded products

How to predict customer behavior with AI

Step 1: Capture rich behavioral data

The foundation of effective prediction is quality data. AI is only as good as the signals it's trained on, so the first step is capturing a complete and unbiased view of user behavior. This includes every click, scroll, rage click, form field, hover, and navigation event—structured in a way AI can interpret.

Want to know where your company stands with behavioral data? Use the Fullstory Data Maturity Matrix to get your AI and data snapshot

Step 2: Surface meaningful patterns

Once the AI-ready data is captured, machine learning models analyze it by comparing it to an even larger data set that includes behaviors and outcomes. This makes it possible to detect patterns that humans would miss—like subtle drop-off points, hesitation before conversion, or consistent behavior loops that precede churn or friction.

StoryAI—Fullstory’s system of AI agents—accelerates this process with:

Summaries, which condense user sessions into clear, actionable narratives
Opportunities, which flag struggling or high-intent sessions in real time
Answers, which help teams ask natural-language questions to uncover predictive trends instantly

This moves teams from reactive investigation ("Why did this happen?") to proactive insight ("Here’s what will likely happen, let’s act now").

Step 3: Predict and act in real time

AI doesn’t just uncover patterns—it empowers action. With the right integrations and real-time data streams, teams can:

Personalize offers based on predicted intent
Trigger support interventions before frustration turns into churn
Flag fraud risks as they begin to emerge
Route findings into martech, product, or CX workflows instantly

What is predictive behavior modeling?

Predictive behavior modeling is a technique that uses data analysis and machine learning algorithms to predict future customer behavior. It plays a crucial role in industries such as retail, finance, and marketing, helping businesses better understand their customers and user groups.

Companies collect large amounts of behavioral data from various sources, such as transaction histories, browsing patterns, and customer interactions, to create a predictive model. The data is then processed, cleaned, and analyzed to identify trends and patterns that can be mapped to future behavior. Some common applications of predictive behavior modeling include:

Estimating customer lifetime value
Identifying potential churn risks
Discovering cross-selling and upselling opportunities
Forecasting revenue and sales

Using artificial intelligence and machine learning algorithms like decision trees, k-means clustering, and neural networks, these models grow more accurate over time as more data is added.

Although predictive modeling provides significant insights, it’s important to use it as a tool to enhance, rather than replace, human intuition. When used correctly, it helps companies design targeted promotions, improve customer retention, and increase revenue by effectively predicting and meeting customer needs.

How can AI and machine learning predict customer behavior?

Machine learning has become an essential tool for predicting and understanding customer behavior. It allows businesses to anticipate customer needs and preferences, leading to improved customer engagement and retention.

Now, let's explore how machine learning can be utilized in various aspects of customer behavior prediction.

Personalize campaigns

Machine learning helps businesses identify meaningful patterns in customer behavior, enabling more accurate targeting across channels. By analyzing behavioral signals like browsing habits, feature usage, and past engagement, AI makes it possible to personalize product recommendations, promotional messaging, and content delivery at scale.

This kind of personalization is most effective when it's tied to real-time behavioral data. With Fullstory Anywhere: Activation, teams can act on live customer signals across platforms, triggering timely, relevant experiences based on what users are doing in the moment—not just what they’ve done in the past. Whether it's surfacing a helpful message after a stalled checkout or adjusting content for users actively comparing products, personalization is more precise and impactful.

When personalization is informed by real-time context, campaigns become smarter, not just more tailored. Combined with advanced marketing analytics, AI-driven personalization helps brands increase engagement, deepen loyalty, and drive long-term value.

Improve customer satisfaction

Machine learning can improve customer satisfaction by identifying and addressing potential customer pain points. By predicting common customer complaints, businesses can take prompt action to rectify the problems before they escalate, leading to higher levels of customer satisfaction.

For example, Complaints Management can anticipate customer grievances, helping companies address these issues proactively.

Predict customer churn

Knowing when a customer is likely to leave can make the difference between retention and revenue loss. Machine learning identifies patterns across behavioral data—such as repeated visits to support pages, skipped onboarding steps, or drop-offs during key workflows—that signal a higher risk of churn and help you predict customer churn before it shows up in cancellation data. Recognizing these signals early allows teams to take action before it’s too late.

StoryAI helps surface these moments by analyzing customer behavior at scale and flagging sessions that show signs of frustration or disengagement. Instead of reviewing hours of session data manually, teams can focus on addressing the at-risk experiences StoryAI highlights for them.

With the ability to act on churn risks in real time, brands can deliver more proactive experiences and protect long-term customer value. When implemented well, this kind of predictive insight can help drive customer loyalty—and lead to significantly improved revenue streams.

Detect fraud

Through user behavior analytics tools, machine learning can also help detect potential fraud cases.

Suspicious transactions, account creations, and other activity can be flagged to prevent losses and protect legitimate customers. The machine learning algorithms sift through vast amounts of data, quickly identifying potential fraud risks and allowing businesses to conduct early investigations.

Benefits of predicting customer behavior with AI

Understanding customer behavior has always been critical for growth, but artificial intelligence now makes it faster, more scalable, and significantly more actionable. Predictive models powered by AI allow brands to deliver meaningful experiences that put users first while meeting key business goals.

1. Enhanced personalization

AI enables highly personalized customer experiences by analyzing behavioral patterns and intent signals in real time. Instead of manual customer segmentation, brands can rely on machine learning models to recommend products, suggest content, or offer support precisely when users need it.

For example, an AI model can detect when a user hesitates on a checkout page and trigger a timely message, dynamic discount, or proactive support pop-up to encourage conversion.

2. Early detection of churn risks

By identifying session patterns linked to customer frustration—like repeated interactions with the same element or sudden drop-offs—AI can surface user sessions at risk of abandonment or churn. This gives teams an opportunity to act before the customer leaves.

3. Better allocation of resources

AI helps surface high-priority customer actions or friction points, allowing product, marketing, and support teams to focus efforts where they matter most. For CX teams, this means prioritizing struggling sessions, while product managers can streamline bug fixes and iteratively improve the digital experience. With these kinds of insights, teams can spend less time filtering through data and more time solving problems.

4. Real-time customer experience optimization

Traditional analytics provide a retrospective view. AI models deliver real-time recommendations based on what customers are doing in the moment. This allows brands to proactively address customer needs, improve navigation, and optimize flows without waiting for issues to show up in metrics or support tickets.

5. More accurate forecasting and revenue predictions

AI enables more precise forecasting by building models that account for a variety of real-time and historical behavioral data. Businesses can identify high-value customers earlier in the funnel, estimate customer lifetime value more reliably, and predict pipeline performance based on how users are currently engaging. These predictive metrics help inform everything from campaign planning to product roadmap prioritization.

Challenges of using AI to predict customer behavior

Adopting AI-powered behavioral predictions also comes with important considerations. From privacy regulations to data quality and model transparency, brands must carefully navigate how these systems are implemented and maintained.

1. Data privacy and compliance

Customer behavior data is valuable but also highly sensitive. Businesses must be intentional in how they collect, process, and store this data. When implementing AI models, it's important to ensure that data inputs are anonymized or pseudonymized where appropriate and that data usage aligns with customer expectations.

→ Learn more about Fullstory’s privacy-first approach.

2. Data quality and consistency

AI relies on structured, high-quality data. Incomplete, fragmented, or biased data can negatively affect model accuracy and lead to misleading predictions. Organizations need to assess how behavioral data is captured across platforms and ensure alignment between tools, teams, and business units.

3. Over-reliance on automation

AI should enhance, not replace, human judgment. Predictive models can surface patterns and probabilities, but teams still need to validate insights and understand the "why" behind behaviors. Overrelying on AI outputs without context can lead to missed nuances or a disconnect from the real customer experience.

4. Model interpretability and transparency

Some AI algorithms, especially those involving deep learning, can act as "black boxes," making it difficult for stakeholders to understand or validate how predictions are made. This lack of interpretability can limit adoption across teams and erode trust in AI-driven findings.

Predict customer behavior with AI-powered insights from Fullstory

Predicting customer behavior is a game-changer in any business plan. Data science and predictive analytics can help retain customers, improve satisfaction, and drive revenue—but it’s not just about having the right tools. It’s about understanding your business, knowing your customers, and making your data actionable.

With Fullstory, teams can move from reactive decision-making to intelligent, proactive experiences that scale. Built directly into the Fullstory platform, StoryAI helps teams go beyond reporting, surfacing patterns, highlighting friction, and predicting what users might do next. By turning complex behavior into clear, actionable insight, it helps you move faster and focus on what matters most.

→ Ready to take the next steps? Learn more about StoryAI.

Phygital experience: Fixing the data gap in omnichannel retail

Fullstory — Thu, 16 Apr 2026 04:00:00 GMT

Most retailers have phygital strategies (BOPIS, in-store apps, kiosks), but lack clear visibility into what happens on the digital parts of those journeys.
Phygital is as much a behavioral data capture problem as it is a design challenge.
Behavioral data and digital experience analytics show where phygital journeys break down and how much revenue is at risk.

You probably have BOPIS. An in-store app. Maybe a few kiosks. Your team can describe the phygital strategy. But can anyone say where customers abandon, why they hesitate, or how much that friction costs?

Physical retail is back. But most leaders have invested in the infrastructure without the instrumentation. The store floor is observable. The digital legs of those journeys are a black box.

Behavioral data is the missing layer that makes phygital measurable.

What is a phygital experience (in retail)?

A phygital experience is a single customer journey that spans physical and digital touchpoints without losing context. The term combines "physical" and "digital" to describe moments where both worlds meet in one interaction.

In retail, this usually looks like discovering products on social, checking stock via mobile apps, then picking up in store. Or scanning a QR code on a shelf to access product details, reviews, or sizing guides. The customer experience moves between digital channels and brick-and-mortar stores, and expects prices, promotions, and account details to follow.

Today's customers expect to switch between online and offline channels without losing their place or their context.

Concrete examples of phygital experiences include:

Click-and-collect (BOPIS): Purchase online, pick up at the physical store
In-store app usage: Open the retailer's app while aisle browsing to check sizes, read reviews, or find product availability
QR codes on shelves: Scan to access rich content, video demos, or home delivery options

For a VP of Ecommerce, phygital experience is judged by how easily shoppers move between channels without losing context or intent. The goal is a single coherent experience across every touchpoint.

Phygital vs omnichannel vs unified commerce

These three terms get blurred constantly, but they describe different layers of retail execution. Getting them straight matters for how you structure teams and technology.

	Phygital	Omnichannel	Unified commerce
What it is	The customer's lived experience when a single journey crosses physical and digital	The strategy for coordinating all channels with a consistent brand, offer, and service	The technical backbone: one system of record for orders, inventory, pricing, and customer profiles
Scope	Journey-level (one checkout, one store visit, one QR scan)	Business-wide channel strategy	Platform and data infrastructure
Measured by	Journey completion rates, friction points, time-on-task	Channel consistency, NPS, cross-channel retention	Real-time data sync, inventory accuracy, order visibility
Failure mode	Digital touchpoints break silently. No one sees where the journey fell apart.	Channels exist but feel disconnected: different prices, inconsistent offers, slow service recovery	Siloed systems. POS and ecommerce don't share inventory or customer data in real time.

You can have omnichannel and unified commerce on paper and still deliver a broken phygital experience if you can't see friction in the digital steps. The checkout architecture might be sound, but if the mobile pickup flow is confusing, slots are limited, or address validation fails, the experience breaks anyway.

The hidden measurement gap in phygital journeys

Budgets are flowing into BOPIS, curbside pickup, clienteling apps, and kiosks. But reporting still focuses on store sales versus online sales, not the full customer journey. The gap lives between those two numbers.

The physical environment has natural instrumentation. Store teams see lines at the service desk. They notice confused customers near the pickup counter. They see abandoned carts near the till.

The digital environment has none of that. You can't see digital friction unless you instrument for it. No one sees the taps, swipes, and hesitations that lead to abandonment.

Tag-based analytics miss behaviors in phygital journeys when teams didn't predefine events for edge cases or new flows. Retailers know how many BOPIS orders were placed. They don't know how many customers tried and failed, or why.

Behavioral data platforms capture every interaction, tagged or not.

Where phygital retail breaks: concrete journey examples

The physical world is observable. The digital world is only visible with the right data. Here are four common phygital journeys where friction hides in plain sight.

Journey	Where friction hides	What traditional analytics misses
Click-and-collect (BOPIS)	Payment step, slot selection, address validation	Whether abandonment comes from unclear fees, limited slots, or form errors, and which customers tried multiple times before giving up
In-store app usage	Search results, navigation depth, load times on store Wi-Fi	Which searches return no results, where users switch to a competitor's site, which product pages cause exits
QR-to-purchase path	Landing page relevance, mobile optimization, add-to-cart flow	Exact drop-off point after the scan: whether content mismatch or slow load caused it
Kiosk and endless aisle	Account login, address capture, payment on kiosk interface	Which step triggers staff intervention, error patterns that don't surface in aggregate usage counts

Standard analytics count what was clicked or purchased. They don't show rage taps on a broken button, repeated back-and-forth between steps, or the exact moment a customer gave up. Those signals explain where phygital actually breaks.

Phygital is a data capture problem, not just an experience design problem

Design teams often get the brief to "fix the phygital experience." Without trustworthy behavioral data, they're guessing which problems matter most.

The physical environment already has natural instrumentation: staff feedback, observed queues, product returns. The digital environment has none of that by default. It stays invisible unless you capture it.

Without behavioral data from web, mobile apps, and non-traditional interfaces (kiosks, embedded browsers, in-store tablets), retailers can't connect physical outcomes like a missed pickup or an abandoned kiosk back to their digital causes.

Most phygital initiatives stall after pilots because teams can't prove which changes reduced friction or recovered revenue. Investment moves elsewhere.

Digital experience analytics are the instrumentation layer that makes phygital measurable. Platforms like Fullstory capture every interaction without manual tagging.

Using behavioral data to improve phygital journeys

The point is faster decisions: where shoppers struggle, what to fix first, how much revenue is at risk.

Platforms like Fullstory capture every interaction via Fullcapture, so you can retroactively analyze new phygital paths (a newly launched curbside flow, for instance) without waiting on tags.

Session replay and aggregate views (funnels, heatmaps, journey maps) show where customers hesitate in BOPIS checkout or stall in kiosk workflows.

StoryAI does this at scale. Given thousands of sessions, it surfaces patterns like "high-value customers dropping during pickup-slot selection on mobile" without manual review.

Guides and Surveys goes further. Teams can drop in-app prompts or micro-surveys into BOPIS flows or kiosk interfaces to catch confusion in the moment, instead of piecing it together afterward.

Behavioral data feeds experimentation too: A/B tests on pickup options, in-store app navigation, or QR landing pages.

Key metrics for phygital experience success

These metrics tie to specific phygital flows rather than generic KPIs:

Metric	What it measures
BOPIS initiation vs completion rate	Percentage who start and finish click-and-collect checkout
Time to complete pickup order on mobile	Proxy for friction in mobile checkout
Store visits that include app interaction	Adoption and utility of in-store digital tools
QR scan-to-add-to-cart conversion	Effectiveness of QR-based discovery
Kiosk usage vs staff-assisted orders	Whether self-service actually works
Repeat usage of in-store digital tools	Whether first experience was compelling

Most are hard to track with traditional analytics. With complete behavioral data, they become tractable. Connect each to revenue impact (BOPIS order value, incremental store sales from app or kiosk usage) to decide what to fix first.

How retail leaders can operationalize phygital insights

Set up regular reviews. Product, ecommerce, store ops, and CX teams should review phygital journey data together, focused on a small set of flows like BOPIS, returns, and in-store app usage.

Create shared definitions for success. Agree on acceptable abandonment and error rates for each phygital flow. Without those numbers, teams prioritize differently and pull in different directions.

Pair quantitative data with qualitative feedback. Validate behavioral data against feedback from store associates. When a behavioral insight says "customers confused about pickup timing," and a store manager confirms "customers always ask us when they can pick up," you have confidence to act.

Give store leaders access. Simplified views from tools like Fullstory help store leaders spot and escalate local issues.

Phygital experience needs better digital sight, not more channels

The limiting factor for phygital success isn't channel availability anymore. It's behavioral visibility into the digital steps that connect store and screen.

Phygital is a behavioral data capture problem as much as a design problem. Without seeing the digital half of those journeys, you're guessing at what to fix.

Behavioral data platforms like Fullstory give retail and e-commerce leaders the "digital sight" to find friction, size the revenue at risk, and fix it.

Get started with demo →

Seeing is believing: Generating screen captures from session recordings

Joshua Dutton — Thu, 29 Aug 2019 05:00:00 GMT

Creating thumbnail "screenshots" with session replay and Puppeteer.

Building the technology required to power a complex product like Fullstory means routinely solving unique problems with non-routine solutions. Today, we’ll talk about one of these solutions—how we made it easy to identify specific moments from a user's digital experience by generating thumbnails (a.k.a. screen captures or screenshots) from their session, as logged by Fullstory.

You might be thinking, "Screenshots from replaying sessions ... how hard could that be?" If so, that's a testament to the seamless experience of replaying user sessions in Fullstory. This is because when you replay a user session in Fullstory, even though what you're watching seems like a video recording, it is really a complex re-enactment—like a play—of all the little things the user saw on screen (See the Guide to Session Replay). In other words, generating a screenshot is not like grabbing a frame from a video; it's more like conducting a play on demand and at a specific moment in time and then taking a photo of that exact moment, just how it happened.

That's far from easy.

So in this post, we'll cover how we brought thumbnails from proof-of-concept to production ready, walking you through each aspect of the implementation. You’ll get a sense for how real-life software is built, and in particular, how distributed systems come together.

Let's get started!

Passing Notes

A useful feature of Fullstory is the ability to take notes and comment on sessions during replay. Here's how it works: Let’s say you’re watching a Fullstory session and you notice something you don’t want to forget, so you make a comment. The "Note" gets attached to the session recording at a specific moment, and a link is generated directly to that Note in Fullstory. Notes make it easy to collaborate within or across teams. You can share Notes with other users from within Fullstory—or share them via integration with tools like JIRA and Slack. Within the Fullstory app, you can review the “All Notes” page to quickly see all of the recent notes left by you and others.

Up until the beginning of 2019, Notes would show up as text-based cards in Fullstory that included the name of whoever left the comment, a link to the corresponding moment in the recorded session, and more. This worked well enough, except for one thing: the Note cards were a little too abstract. They lacked a visualization of what the Note related to—a snapshot of the digital experience at the time of the comment. This made it hard to scan the notes quickly.

We decided Notes needed screenshots, thumbnails to help the user see what the page looked like when the comment was created. And so we built thumbnails for Fullstory Notes:

As you can see, thumbnails now appear above each of the Notes on the "All Notes" page. While the thumbnails are not earth-shattering, they add a certain je ne sais quoi to the page. 👌They make it easier than ever to quickly scan the page and find a Note that catches your eye.

Great. So how do thumbnails work?

Master of Puppets

Fullstory’s session replay platform is, of course, a core component of the thumbnails implementation. By leveraging it, we can begin replaying a session, pause it at the right moment, and generate a screenshot. This screenshot can then be shrunk down and saved elsewhere to be used as the thumbnail. Done!

This is a good idea. However, implementing it is more complicated than it seems. How do you convince an intern to sit and do this manually every time a Note is created? Obviously, you can't. And that's why we decided a humorless machine would have to shoulder the burden instead.

Enter the Puppeteer library, built by the Google Chrome team. Puppeteer is a Node.js library that allows you to programmatically control headless instances of Google Chrome and its open-source cousin Chromium.

If you aren't familiar with the concept of a headless browser: it’s a fully functional web browser, but one that does not generally render the page to the screen or accept user input. Instead, it is controlled via an API by external tools. This means that it can navigate to pages, log-in, submit forms, execute Javascript, etc. It’s everything you'd expect of a browser, maybe short of having extensions, bookmarks, or similar "quality of life" features. Typically, you encounter headless browsers in the context of automated user interface tests—such as through popular tools like Selenium and Nightwatch.js—which are able to manipulate headless browsers using a scripted series of steps to verify that your website’s functionality has not substantially regressed.

By leveraging Puppeteer and its ability to drive headless Chromium on our behalf, we had a way to power our session replay technology—and generate those moments we needed to screenshot—without having to perpetually caffeinate any humans. This solution could open and close the browser instance, navigate to the session replay page, and pipe the requisite data across the wire. Both spooky and useful, it could even support taking a screenshot of a page that it has not rendered out to the display! Just hit the right timestamp during playback and Puppeteer creates an image we can then buffer out to a file.

Head in the Clouds

How does all of this play out in practice? Let’s look at some details.

First, when a Note is first created inside the Fullstory app, we place a message in a task queue. The message is just a small amount of data describing the session, such as display dimensions and resolution, and the point in time we'd like Puppeteer to take the screenshot.

Next, the message will be pulled from the queue by a task handler, which will validate the request and begin fetching the necessary event bundles for the page. (Event bundles are what drive session replay here at Fullstory.) The bundles are sent alongside the rest of the request to a Google Cloud Function, which orchestrates the thumbnail creation.

Here, the function navigates a fresh browser instance to the rendering iframe and injects the request parameters into the page:

const handler = async (req: Request, res: Response) => { const parameters: Parameters = { ... }; const browser = await puppeteer.launch(); const page = await browser.newPage();

// exposeFunction adds a "bootstrap" function to the window object. It simply returns the parameters found // in the request body, including the event bundles, so that we can feed them to the rendering iframe. await page.exposeFunction('bootstrap', () => parameters);

// Navigate to the playback page, which will simulate session replay. page.goto(`file:${join(__dirname, 'headless_playback.html')}`);

// Continued below. };

Inside the browser, the headless_playback.html begins sending the event bundles over to the renderer:

// This script runs inside of the headless browser, not Node / GCF. (async () => { const parameters: Parameters = await window['bootstrap'](); const [renderer, host, iframe] = openChannelToRenderer();

// Point the playback iframe to the renderer const rendererUrl = buildRendererUrl(parameters); iframe.setAttribute('src', rendererUrl);

renderer.playPage(parameters.page);

await host.ready(); renderer.playToTime(parameters.playToTime);

// Signal to Puppeteer that rendering has begun iframe.style.visibility = 'visible'; })();

Now, at this point, our code is simulating session replay inside of a headless browser, purely for the purpose of extracting a thumbnail from the playback. Pretty cool! When rendering is finished, we can proceed to take the screenshot, do some quick resizing (via sharp), and pipe the resulting image out through the response. Back in the request handler:

// We're only interested in the contents of the renderer const boundingBox = await getBoundingBox(renderer);

// Wait on Puppeteer to generate a screenshot of the page const screenshot = await page.screenshot({ clip: boundingBox });

// Resize the image while maintaining aspect ratio. This should be sufficiently // large for most use-cases while reducing storage costs and page load speeds. const resized = sharp(screenshot).resize({ width: 800, withoutEnlargement: true }).toBuffer();

return res.set('Content-Type', 'image/png').send(resized);

The task handler that sent the original request to the Google Cloud Function will then buffer the image out of the response into a file, where we can store it for use as a thumbnail.

You Know What They Say About the Best Laid Plans ...

So far, the implementation is pretty solid. It just has one hurdle: we need to ensure the page is fully loaded and rendered before we proceed with the screenshot, rather than leaving it to guesswork. Otherwise the thumbnail could end up a garbled mess:

Yep, that's it. White space and all. It goes without saying, this is not what the website looked like at the time in question.

Oof!

A simple solution to this problem would be to just wait a predetermined amount of time, say 5 seconds. But does this solution actually work? We recorded the network activity for a handful of websites out in the wild and compiled the data into the diagrams you see below. Each row represents an individual network request, moving from left to right through time, so rows stacked on top of one another indicate concurrent network requests.

Above, you can see each network request made by the browser. For our purposes, we used this analysis to understand how long it takes to load a page. As you can see in this case, all requests were complete after about 3 seconds. You can pull these Gantt-like visualizations on your own by opening up the Developer Tools panel in your browser.

Now, in this first example, you can see the page initiates many requests simultaneously and they all finish relatively quickly. A+! The only drawback? Since we’re forced to wait the full 5 seconds, our code will continue to execute an additional (wasted) 2 seconds.

Let's take another example. Here, most of the requests finish with ample time to spare. Only if you look closely, there are two straggler requests who land past the 5 second mark. Our code would miss these.

Here, you can see almost all network requests are done after 3 seconds ... except if you look closely, there are a couple more that sneak in after 5 seconds.

Finally, look at these two pages. Both have requests that do not finish within our 5 second window ... and are lost. Hopefully those requests aren’t anything important like, say, a stylesheet or logo.

These two last examples show how network requests can often drag out over many seconds.

Finally, two more pages whose requests do not finish within our 5 second window and are lost. Hopefully those requests aren’t anything important like, say, a stylesheet or logo.

We now know that for some pages, the 5 second cut-off will be too short. The resulting thumbnail could end up looking like an unstyled or partially loaded page. And for others, 5 seconds will be far too long, wasting time, money, and electricity on running code that has nothing to do.

Of course, 5 seconds is just an arbitrarily chosen cut-off. Adjusting it up or down does not fix either of our problems, so there really is no correct number here. What were we to do?

Writing code that gets it right for every flavor of website out there is going to be very hard—and as every seasoned web developer knows, all we're trying to do here is cope. So here's how we coped: we gave the thumbnail function two heuristics: network activity and rendering activity.

Holding the Door Open for Network Activity

As you just saw, whenever the browser begins loading a modern webpage, it fires off a huge wave of requests in the background. It has to fetch all of the necessary fonts, stylesheets, scripts, images, and animations (the list goes on and on). As each of these requests finishes and the network activity “settles down,” we know the page is on its way to being presentable. So this “settling down” is our first indicator.

Luckily, Puppeteer turns this vague concept of network activity “settling down” into a precise notion for us. It has a handful of waitFor* functions, including page.waitForNavigation([options]). This function takes a normal timeout as an option, and in addition it also accepts a waitUntil parameter that can be any of:

load - consider navigation to be finished when the load event is fired.

domcontentloaded - consider navigation to be finished when the DOMContentLoaded event is fired.

networkidle0 - consider navigation to be finished when there are no more than 0 network connections for at least 500 ms.

networkidle2 - consider navigation to be finished when there are no more than 2 network connections for at least 500 ms.

Of the options above, we are interested in the bottom two since they give us a convenient way to measure network activity on the page. Thus, ideally, all of the network requests would finish quickly and Puppeteer would signal networkidle0 right away, allowing the screenshot to proceed. In practice some websites tend to have long-running but nonessential requests—or a recurring request that could prevent the networkidle0 condition from ever being satisfied. (These are the stragglers in example #2 above.) That leaves us with the more practical option—networkidle2. If all but two requests have completed in a 500 millisecond window, we consider it good enough.

What if those last two requests finish just outside the window? Catching the resources that load at the very last moment could make or break our thumbnail. This is that uncomfortable grey area you encounter when holding the door open for others. You should probably just let the door quietly shut, but you don’t. You insist on standing there with a half-hearted smile on your face as your victims do that awkward rush over to the door. This is that same dilemma, but in Javascript. Thankfully, it turns out we can reach a decent compromise without a lot of additional complexity using Promise.race:

// Proceed after either condition is reached. networkidle0 is preferable, but // networkidle2 plus a small fudge factor is also acceptable. const waitForNetworkIdle = async (page: Page): Promise<()> => { const networkidle0 = async () => { return page.waitForNavigation({ waitUntil: 'networkidle0' }); };

const networkidle2 = async () => { await page.waitForNavigation({ waitUntil: 'networkidle2' }); return page.waitFor(1000); };

return Promise.race([ networkidle0(), networkidle2(), ]); };

Promise.race accepts a list of promises. Whenever any of them resolve, no matter the order, Promise.race will also resolve. If the page hits networkidle0 quickly, great! Otherwise, we're willing to hold the door open slightly longer for networkidle2.

Rendering Activity

The second indicator is rendering activity: as the page finishes loading and rendering to the (hypothetical) screen, there will be natural fluctuations in the amount of work the browser is doing. It will mostly be active, but there can be periods of inactivity or idling in between. We can queue up a function to be invoked during that browser idling using window.requestIdleCallback, which takes a function as a callback and an optional timeout.

(Mind that requestIdleCallback is not a standard browser function and will not be available everywhere. Notably, there is no support for requestIdleCallback in Internet Explorer, Edge, or Safari. Since we're relying on Chromium for thumbnails, this is a safe choice, even though it's non-standard.)

In practice, the browser may fire several idle callbacks throughout a typical page load. So only after the page reaches network idle do we register the callback:

const [idleState, renderer] = await Promise.all([ waitForNetworkIdle(page), page.waitForSelector('#rendering-frame', { visible: true }) ]);

// Tell the browser-side code we're ready for the requestIdleCallback // by invoking a custom function we placed on the window object await page.evaluate('window.scheduleRequestIdleCallback()');

Over in the browser, on the headless_play.html page:

// Wait for the browser to signal that it is idle by using requestIdleCallback, // or proceed after a short timeout if necessary window['scheduleRequestIdleCallback'] = () => { const callback = () => { window['onRequestIdle'] = true; };

window['requestIdleCallback'](callback, { timeout: 5000 }); };

And back in the Cloud Function, waitForFunction polls onRequestIdle on the window object and waits for the condition to return true:

// The network is idle and the rendering frame is visible; // just waiting on an idle callback from the browser await page.waitForFunction('window.onRequestIdle');

It may seem like a lot of work, but this extra effort helps ensure we produce good looking screenshots. If the browser is signaling that it is still working hard to render the page, either through network activity or delaying the idle callback, we're willing to wait around (up to a point, anyway).

Progress!

I See What You Did There: Visual Regression Testing

With our network and rendering heuristics in place, let’s talk about testing.

Constant change within a codebase will eventually lead to some broken features. To prevent these feature regressions from accidentally being shipped to the world, modern software engineering practices dictate your code is covered by a variety of automated tests, chiefly unit tests and integration tests.

Thumbnails pose an additional challenge that cannot be easily tested by conventional methods: the code may advance from A to B to C without ever throwing an error even while generating a completely malformed thumbnail image! We need to reliably create thumbnails on-demand and at scale ... so we'll need a way to mitigate this issue.

There are a few possible approaches to consider, such as using traditional feature detection to reason about the quality of the image (e.g. by differentiating a blank webpage from one that is fully styled and rendered). Let's just cover one approach that has proven to work well in practice: visual regression testing.

Visual regression testing uses a fuzzy image comparison algorithm to detect regressions in your code—typically changes in a program's user interface—that are very hard to notice with a normal test. To verify that the thumbnails implementation has not regressed due to any recent changes, we first generate a screenshot that has been checked by a human and certified as correct. This becomes what is known as the benchmark image.

Our benchmark image.

The benchmark image is committed to version control and lives alongside the rest of the code. Now, whenever any code is changed, the visual regression test will automatically run and check the output of the thumbnails function against the benchmark image. If the two differ beyond a low threshold, for instance a 1% difference, the test fails and the code changes are rejected.

We perform this test using the jest-image-snapshot and pixelmatch libraries to perform the fuzzy image comparison:

const toMatchImageSnapshot = configureToMatchImageSnapshot({ failureThreshold: 0.01, failureThresholdType: 'percent', customSnapshotsDir: '__image_snapshots__', updatePassedSnapshot: true, });

expect.extend({ toMatchImageSnapshot });

test('visual regression', async () => { const requestBody = JSON.stringify(testFixture);

return request(app) .post('/thumbnails') .send(requestBody) .expect(200) .expect('Content-Type', 'image/png') .then(res => expect(res.body).toMatchImageSnapshot()); }, 30000);

What does it look like when this test fails? Imagine a bug prevents one of the images from loading inside your session playback. The difference between the two thumbnails is enough to cross the threshold and cause the test to fail. When this happens, the pixelmatch library produces a helpful image diff to aid in troubleshooting.

Above you can see the output of our visual regression test. The test compares the benchmark image (left) to the screen from the session recording (right), identifying any difference (middle image).

Without the visual regression test, this change may have passed through the continuous integration pipeline and made it into production, where it could have gone unnoticed for quite some time.

Automated testing such as this visual regression check help give us confidence that the thumbnail feature works and will continue to work into the future, even as the codebase and the product rapidly evolve.

Conclusion: Thumbs Up!

And that's how thumbnails for Fullstory are created. The interns are saved!

Of course, even with all the above going on, there’s still more happening behind the scenes than what could be covered here—things like securing, deploying, and monitoring the thumbnails code. There's a lot that goes into making Fullstory the product it is. Thumbnails may seem a small thing, but little things add up to make for big differences in the overall experience of a digital product.

I hope this small slice provides a “snapshot” into how a feature is built and implemented here at Fullstory.

P.S. We’re incredibly proud of the work we do, and want to share our love for engineering with the world. Fullstory is constantly on the lookout for others who share our enthusiasm for finding creative solutions to exceptionally challenging software problems. If that sounds like you, say "Hi." 👋

How to improve your user onboarding flow and stop losing customers

Kelsey Winiarski — Wed, 29 Apr 2026 06:00:00 GMT

You did it. After all the marketing campaigns and sales demos, a new user just signed up for your product. They log in for the first time, ready for their problems to be solved.

And then… silence.

They’re met with a generic product tour, a confusing dashboard, or a flurry of tooltips. Overwhelmed, they click around for a minute, decide they’ll "figure it out later," and log off.

They never come back.

This isn’t a rare failure; it’s the default outcome for most SaaS products. The hard truth is that customer retention isn’t won months down the line with new features; it’s won or lost in the first few minutes with your user onboarding flow.

What is user onboarding, really?

Too many product teams think onboarding is just a product tour. It's not.

Product onboarding is the process of guiding new users from their initial sign-up to their “aha!” moment. This is the point where they personally experience the value your product promises. It’s a journey that methodically turns a curious evaluator into an activated, empowered user.

4 ways to fix your onboarding experience

If it’s so critical, why do so many companies get it wrong? Most failed onboarding flows are generic, overwhelming, and built on assumptions. Great onboarding, in contrast, is designed. Here’s how to build an experience that retains users.

1. Personalize the experience with contextual guidance

You wouldn't give a new marketing intern and a seasoned data analyst the same set of tasks. So why would you give them the same onboarding? A one-size-fits-all tour fails because it’s irrelevant to most users, especially when you consider the different experiences required for desktop versus mobile users.

High-performing onboarding is tailored. It uses roles, jobs-to-be-done, or in-product behavior to deliver the right message at the right time.

A new user on a trial plan might see a checklist guiding them toward core activation features.
An enterprise user might get a personalized in-app welcome from their dedicated account manager.

By using an intuitive visual builder, teams can target guides to specific user segments based on their properties, transforming a generic pop-up into a personal welcome that feels native to the product.

2. Make it action-oriented

The goal of your SaaS onboarding is not to teach users everything your product can do. It's to help them accomplish one meaningful thing that proves your product's value. Don't just show them features; guide them to perform meaningful actions.

An action-oriented onboarding flow builds momentum and helps users achieve a quick win.

Interactive tours: Instead of just pointing at a button, prompt the user to click it themselves to complete a key workflow.
Onboarding checklists: Give users a clear path to success by breaking down complex setup processes into small, manageable tasks.
Smart empty states: Use empty UI states as an opportunity to prompt the first critical action, like "Create your first project" or "Invite a team member."

3. Let behavioral data guide your efforts

You cannot improve what you don’t measure. World-class product teams are obsessed with understanding how users are experiencing their onboarding. They don’t use guesswork; they use behavioral data to find and eliminate friction.

To improve your user onboarding, you need a way to answer critical questions:

Where in the funnel are users dropping off?
Are mobile users struggling at different points than desktop users?
Which tooltips are being ignored or dismissed?
Does completing the onboarding checklist actually correlate with higher retention?

The only way to answer this is to close the "insight-to-action" gap. By treating in-app guide and survey interactions as first-class events in your analytics platform, you can measure their direct impact on funnels and metrics. This allows you to identify a point of struggle, target those users with a guide, and measure its impact—all in one connected workflow.

4. Use contextual surveys to understand the "why"

Quantitative data shows you what users are doing, but it can’t tell you why. Traditional email surveys have abysmal response rates, and the feedback they do generate lacks context.

Instead of relying on traditional surveys, a more effective approach is to embed feedback opportunities directly into the product experience. When a user shows signs of friction with a feature, a non-intrusive, contextual survey can slide in to ask about their experience at that exact moment. This provides high-quality, actionable feedback that is tied directly to a user's in-product journey.

Onboarding is a product, not a project

A successful product onboarding experience is never truly done. It’s a living part of your product that should evolve with your users. The key is to build a continuous feedback loop powered by behavioral data.

With a behavioral data platform, you can watch session replays of users interacting with your onboarding to see exactly where they get stuck. You can build funnels to see where they drop off. And you can analyze metrics to see if your changes are actually improving activation and long-term retention. This data-driven approach turns onboarding from a one-time project into your most powerful engine for growth.

Ready to build a better onboarding flow?

Stop losing customers to bad first impressions. With Fullstory’s Guides and Surveys, you can pinpoint user friction with data and proactively deliver contextual guidance to improve activation, adoption, and retention.

→ Request a demo of Guides and Surveys

The ghost in the machine: Why AI agents are exposing our technical debt

Lane Greer — Thu, 30 Apr 2026 06:00:00 GMT

I’m a coffee guy. I love it in just about all forms—except for the frozen, blended ones that are basically a cup of sugar. (That’s not coffee. Come at me in the comments.) Pour-over, French press, AeroPress, immersion, pour-over immersion, espresso, Chemex—I’ve spent years amassing different ways to shove water through grounds.

My home coffee bar is, frankly, a little embarrassing. It’s got its own dedicated space. There are scales (plural). Grinders (plural). Precision distributors and weighted tamps. Every gadget fits into a series of very specific workflows I’ve tuned to create what I consider the perfect cup.

But not long ago, I took the kids to Orlando for spring break, and my in-laws came over to watch our one-year-old. When I woke up the first morning of the trip, I had a text from my mother-in-law: "I just want to make a cup of coffee. I need help."

Even my wife is in a similar boat. That coffee bar is my world, but the methodology isn’t written down anywhere. It’s my own personal, caffeinated institutional knowledge. Without me there to interpret the equipment, the gear is just a collection of confusing metal and glass.

We’ve been building digital storefronts the exact same way for years.

We push code where a "Buy Now" button is actually a <div> wrapped in a <span> with a CSS class name like .btn_x92j_active. To the engineer who built it, it’s a workflow. To a browser, it’s just a node. We’ve relied on the "institutional knowledge" of our human users to navigate the unlabeled switches we call a UI.

But the operator is changing. We are entering the era of the computer-use agent—AI assistants that don't just suggest code, but actually navigate interfaces to execute tasks. These agents are currently staring at our storefronts and, much like my mother-in-law at my coffee bar, they are texting us for help.

The AI isn’t broken. Our definition of "well-built" is.

The semantic gap between humans and agents

For a long time, we treated "clean code" as a matter of internal legibility—making sure the next engineer could understand the logic. We treated accessibility as a compliance checkbox. We treated analytics as something we'd bolt on later with a few third-party tags.

The rise of AI agents has exposed the gap between these silos. You might have your "Add to Cart" button labeled somewhat semantically, but what about your color swatches? What about sizing or other variations? Can an AI agent understand what makes one product image different from the next? Can it filter your products on product lists and use your site search/navigation?

We’ve been accumulating a specific kind of phantom debt: the hidden cost of building interfaces that are only understandable to humans. While this debt was manageable for human operators, the truth is, it has been clogging pipelines for years. Test automation. Support Engineering. Performance triage. Downstream warehouse data science for model building. All these efforts have been harder than they ever needed to be because the data was noisy and the intent was hidden.

But now, there’s a new operator. For computer-use agents, this debt isn't just a hurdle; it’s a showstopper. If the agent can't distinguish between a "Small" and a "Large" because the text is buried in an unlabeled tooltip, or if it can't navigate your faceted search because the filters don't broadcast their active state, the agent fails.

One pass, multiple consumers

Making your app understandable to an AI agent is the exact same work required to make your analytics meaningful, your tests stable, and your production observability actually useful.

We tend to think of these as four separate engineering tickets:

QA: "Fix the CSS selectors so the tests stop flaking."
Analytics: "Add event listeners so we can see why conversion dropped."
Accessibility: "Add ARIA labels for screen readers."
AI/Innovation: "Structure the DOM so the agent can find the checkout flow."

In reality, this is one engineering effort.

When you decorate a component with stable, semantic data attributes, you are creating a single source of truth for every machine consumer. This isn't just about a label; it's about a structured contract. Instead of forcing the machine to guess based on a class name or an aria-label, you broadcast a full schema.

<button data-fs-element="add-to-cart-button" data-fs-state="ready" data-product-id="sku_9921_crimson" data-price="45.00" data-currency="USD" data-variant="Crimson" data-size="Large" data-fs-properties-schema='{ "data-fs-state": { "type": "str", "name": "state" }, "data-product-id": { "type": "str", "name": "productId" }, "data-price": { "type": "real", "name": "price" }, "data-currency": "str", "data-variant": "str", "data-size": "str" }' class="btn_x92j_active" > Add to Cart </button>

In this structure, the test suite uses data-fs-element to find the element reliably. The analytics engine captures the properties defined in the schema without a single line of custom tracking code. Granted, Fullstory is perfectly optimized to ingest this type of semantic decoration. And the AI agent? It finally has the "sight" it needs to understand exactly what clicking this button will do, including the specific SKU and price point it’s interacting with.

It’s the architectural equivalent of putting a label on the grinder that says "Step 1: Weigh 20g of beans."

The performance flywheel: AI-agent observability as an optimization

The immediate technical objection is: "Does this bloat the DOM?"

Adding data attributes to existing elements has a near-zero impact on browser parsing speed or memory footprint compared to the massive JavaScript bundles most storefronts ship today. In fact, by 2026 standards, the heaviest part of your app isn't your HTML; it's the client-side logic trying to figure out what’s happening in an unobserved UI.

Semantic decoration actually improves performance by enabling high-fidelity observability.

When your storefront is a black box, you diagnose performance issues using broad, blunt metrics like LCP or TTI. But those don't tell you why a user abandoned a cart. When you have a semantic layer, you can correlate technical performance (like a slow API response) with specific component states (like a button stuck in a loading transition).

You stop optimizing in the dark. You start fixing the 200ms delay that actually matters to the AI agent’s success rate, rather than shaving 10ms off a component the user never sees.

Component state: the missing layer for agent-ready UIs

If stable naming is the "who" of your UI, component state is the "what."

Most digital storefronts today are built with components that are functionally black boxes to the outside world. A checkout button might be in a loading, error, or ready state, but that information is often trapped inside a private React hook.

If an AI agent clicks "Submit" and nothing happens for three seconds, it needs to know why. Is the app hung? Or is the PaymentForm component currently in a processing state?

"Well-built" now means that components must broadcast their state to the DOM. If a component is loading, it should have a data-fs-state="loading" attribute. This isn't just for the AI. It's for the next time you're debugging a production issue at 2 a.m. and need to know exactly what the internal state of the app was at that millisecond.

Scaling the solution

This is a shared component library problem, not a feature-team problem.

If your organization uses a design system or a centralized library of UI components, the cost of this semantic pass is actually quite low. You update the base Button component once to accept and render a data-component prop.

You build the AI sight into the foundation. Once the base components are instrumented, every new feature your team ships is born AI-ready. It scales for free across every feature your team ships.

The new definition of "well-built"

We have to move past the idea that a storefront is just a visual layer sitting on top of an API. In an AI-native world, the UI is a data structure.

A well-built agent-friendly website in 2026 is defined by:

Identity: Every interaction point has a stable, human-readable name.
Transparency: Components broadcast their lifecycle and error states to the DOM.
Context: Actions carry structured business data (e.g., product IDs) as attributes.

The brands that will win the next decade aren't the ones with the flashiest Gen AI chatbots. They are the ones who did the "boring" work of cleaning up their digital experience data. They are the ones who realized that if you want AI to generate business results, the AI has to be able to see what it's doing.

Ditch the wand. Embrace the lab coat. It’s time to start labeling the grinders.

→ Ready to see how Fullstory can help you transform your digital storefront into an AI-ready, agent-friendly experience? Request a demo here.

Predicting customer churn: What statistical models miss

Fullstory — Tue, 14 Apr 2026 11:00:00 GMT

Key takeaways

Churn prediction has two layers: statistical signals (login gaps, payment failures, NPS drops) and behavioral signals (rage clicks, failed forms, incomplete flows). Behavioral data gives earlier warnings.
Churn rarely starts with a decision. It starts with friction that builds over weeks, often before a customer contacts support or hits cancel.
Behavioral analytics is the missing data layer most churn models ignore, with patterns spanning onboarding, feature adoption, and billing flows.

Your churn prediction model flags an account as "at risk." The health score dipped below 40. The dashboard turns red. By the time you see it, the customer already hit cancel three days ago.

Models predict who might leave. They rarely show what pushed them out, or when the friction started. Seeing those signals early enough to act is what separates churn prediction from churn prevention.

What is customer churn (and why prediction alone isn't enough)?

Customer churn measures the rate at which customers stop doing business with you. In subscription and usage-based SaaS, that typically means a cancellation, a non-renewal, or sustained inactivity, often defined as no engagement for 90 consecutive days.

Take a $10M ARR SaaS company as an example: a steady 2% monthly churn rate can erase most net new bookings. Even with healthy acquisition, you're running on a treadmill.

Most churn models focus on whether someone will leave in the next X days. That's useful. Knowing what's pushing them out, and where in the journey the friction started, is what makes prediction actionable.

Lagging signals tell you what already happened:

Cancellations and closed-lost renewals
Negative CSAT scores
Support escalations

Leading signals show you what's coming:

Onboarding drop-offs and stalled flows
Repeated errors on key screens
Time spent stuck without progression

Teams who only track lagging signals are reacting. Leading signals give you time to act.

Types of churn product leaders should track

Not all churn is equal. Predicting each type requires different data and different responses.

Voluntary churn happens when customers actively decide to stop using your product, often after a long period of accumulated friction or poor perceived value. A three-seat startup plan that cancels after failed onboarding is a classic example.

Involuntary churn comes from payment failures: expired cards, billing address mismatches, declined transactions. Predictable from billing events and often fixable with real-time alerts.

Behavioral or engagement churn captures customers who are still paying but showing sustained drops in usage. Logins fall 60% over two months. Core feature usage disappears. This is where behavioral prediction matters most because the signals show up weeks before any model fires.

Revenue churn and contraction covers downgrades and seat reductions that follow months of under-usage. These patterns often precede full cancellation and should be tracked alongside logo churn.

Build products users love, not products users fight. Fullstory's SaaS guide shows how behavioral data turns every interaction into clearer insights, smoother experiences, and measurable business impact.

How churn prediction works: statistical vs behavioral signals

Churn prediction combines patterns in historical customer data with forward-looking indicators. Effective models use machine learning to identify which customers are likely to leave, but the quality of the signals they produce depends on the data you feed them.

There are two distinct layers: statistical signals and behavioral signals.

Statistical signals

These are the metrics most churn models rely on:

Days since last login
Number of feature uses per week
Plan type and tenure
Support ticket volume
Payment status and history

Statistical signals are easy to aggregate into risk scores. They also tend to lag behind real frustration by days or weeks.

Behavioral signals

Behavioral signals are the micro-interactions that reveal frustration before users ever articulate it:

Repeated failed form submissions
Rage clicks on disabled buttons
Looping between the same 2–3 pages
Abandoning flows after an error
Long idle time right before exit

A cancellation notice is a lagging signal. Stalled onboarding checklists, repeated password resets, or aborted billing updates are leading signals, visible days or weeks before any risk score turns red.

The challenge with behavioral signals is capture. Most product analytics tools only track events you explicitly define. Anything you didn't think to instrument stays invisible: the rage click on a broken button, the user who hit an error and quietly left. Fullstory's Fullcapture addresses this by indexing complete behavioral context across web and mobile without manual tagging. Teams can retroactively surface signals they didn't know to look for, without re-instrumenting a single event.

Once that data exists, StoryAI analyzes interaction streams to find patterns tied to churn risk. A pattern like "users who encounter a specific integration error twice within a week cancel at measurably higher rates within 30 days" surfaces automatically. CS teams no longer need to notice a string of cancellation calls before anyone investigates.

The best churn prediction strategies combine both layers: statistical signals for breadth, behavioral data for depth and timing.

Key behavioral patterns that signal early churn risk

Certain product-embedded behaviors consistently precede churn in SaaS. These patterns give you upstream visibility that aggregate metrics miss.

Onboarding breakdowns: Users who never complete sign-up flows, skip key configuration steps, or stall at the same screen within their first 7–14 days. Never inviting teammates, never importing data, never completing setup: these tend to signal eventual churn.

Dead-end navigation: Users bouncing between pricing, settings, and help content without progressing. Repeatedly hitting 404s or empty states. This usually signals confusion or unmet expectations, often triggered by a product update or unclear UX.

Friction-heavy forms and flows: Multiple failed attempts to submit checkout, upgrade, or settings forms. Repeated clicks on disabled elements. Long dwell times followed by exit on payment or permissions screens. Each one compounds frustration before any support ticket gets filed.

Feature disengagement: Previously active users who stop using the features tied to retention. Dashboards go uncreated. Projects go unlaunched. They still log in, but the engagement that predicted staying is gone.

Support-adjacent behaviors: Spikes in frustration signals before ticket creation: rage clicks, rapid back-and-forth navigation, repeated attempts to access help. Many users who churn never file a ticket. The signals were there anyway.

Behavioral analytics: the missing layer in most churn models

Behavioral analytics captures and analyzes every in-product interaction to understand what users actually do: the clicks, hesitations, form attempts, and navigation loops that usage metrics skip.

Traditional product analytics counts events you manually define. Behavioral analytics captures full interaction streams. That difference lets you pinpoint the specific friction that precedes cancellation, rather than logging that something went wrong at the account level.

Consider these use cases:

Detecting an onboarding step that silently fails for a subset of new users, invisible to aggregated funnel metrics but obvious in session-level behavioral data
Surfacing a permission flow causing admins to abandon setup before inviting their team
Correlating a new UI rollout with higher dropout from power users

Modern behavioral analytics platforms feed these signals into existing churn models or customer data platforms, giving data science teams richer context without rebuilding everything. Teams get better prediction accuracy and root-cause insight in the same system.

Where Fullstory fits in your churn prediction stack

A behavioral data platform like Fullstory complements existing churn models instead of replacing them. It acts as the upstream layer most teams are missing.

Fullcapture indexes complete behavioral context across web and mobile apps without manual tagging. Teams can retroactively ask new questions about churn drivers without re-instrumenting events. Product teams can layer that behavioral context alongside usage frequency, support volume, and payment status for a richer risk view.

StoryAI agents can analyze interaction streams to surface patterns connected to churn risk. These patterns might reveal that users who encounter a specific error twice within a week cancel at measurably higher rates within 30 days.

CINC used Fullstory to find the mobile friction that was driving churn. Resolving those issues moved from months to days: time to resolution dropped 25% and customer satisfaction improved 20%.

Behavioral data from Fullstory flows into warehouses and churn models via Activation, enriching existing risk scores and customer profiles. Fullstory provides the behavioral layer that makes those downstream systems smarter.

From prediction to prevention: turning signals into action

Predicting churn is necessary but not sufficient.

In-product: Trigger in-app guides, checklists, or contextual tips in onboarding, billing, and permissions flows when users hit early friction. Fix the problem before frustration compounds into a support ticket.

Human outreach: Route high-value accounts with repeated onboarding failures to customer success. Prompt account managers when key decision-makers go dark.

Systemic fixes: When behavioral analytics surfaces a problematic flow, prioritize engineering changes that reduce friction for every user who hits that flow.

Measurement: Track flow completion rates, time to value, and frustration signal volume before and after interventions. Churn rate alone won't tell you if it's working.

Churn starts long before the cancellation email

Churn isn't a sudden event. It's a sequence of unresolved friction points that compounds until leaving feels easier than staying. Every lost customer traveled a path through your product, filled with small frustrations that went unnoticed or unaddressed.

Teams who can see inside the product catch these patterns. Those relying on dashboards see them after the fact. Statistical signals tell you the symptoms. Behavioral signals tell you the cause.

Ready to see what your current models can't? Behavioral analytics shows you the friction that comes first.

Banking personalization: Real-time signals, real results

Fullstory — Tue, 07 Apr 2026 12:00:00 GMT

Key takeaways

Most banking personalization still relies on static data like demographics and past transactions, missing the real-time behavioral signals that reveal what customers are trying to do right now.
Three mechanics drive effective in-session personalization: identifying intent from behavioral signals, guiding customers without breaking their flow, and optimizing journeys using behavioral proof.
Behavioral data captured from live sessions (taps, scrolls, hesitation, rage clicks) fills the gap that demographic and transaction data cannot, enabling meaningful personalization in critical flows like account opening, KYC, and first deposits.

Most "personalized" banking still runs on stale inputs: demographics, product holdings, last month's transactions. The result is a mortgage banner shown to someone trying to resolve a declined card, or generic savings tips served to a user stuck on a document upload. Real personalization means adapting to what a customer is doing right now—inside the session, inside the flow. What follows covers the behavioral data gap behind generic experiences, then the three mechanics that actually move completion rates: intent identification, in-session guidance, and journey optimization. If you own digital channels at a bank or credit union, this is how you close the gap between what personalization promises and what customers experience.

What is banking personalization (beyond marketing offers)?

Most financial institutions stop at the easy version: segmented email campaigns, targeted marketing banners, a product recommendation engine. That approach treats personalization as a marketing problem.

Product-led personalization inside digital banking journeys is harder to build and more valuable to customers. This means adapting the account opening flow when someone hesitates at KYC. It means surfacing the right help content when a user rage-taps on a validation error during a loan application. It means recognizing that a customer scrolling quickly past fee disclosures has different needs than one who hovers over every line.

Static attributes like age, region, and product holdings rarely reflect current intent. Someone who holds a savings account might be trying to dispute a charge, apply for a credit card, or simply update their address. The data that matters is what they are doing in this session, across mobile banking apps, web banking portals, and authenticated product flows. Behavioral data platforms like Fullstory give product teams a complete view of digital behavior across these channels without manual event tagging, a foundation for personalizing customer experiences based on what customers actually do rather than who they are.

The behavioral data gap: why static data creates generic experiences

Walk through the typical data stack at a mid-to-large bank: core banking ledger for transactions, CRM for customer profiles, marketing automation for campaigns, and basic web analytics with predefined events. These systems answer "who the customer is" and "what they did in the past." They rarely answer "how they are struggling in this exact flow right now."

This gap produces tangible failures:

A mortgage banner displayed to a user clearly trying to resolve a declined card
Generic savings tips shown to someone stuck on KYC document upload
Loan upsell prompts during payment hesitations
Credit card offers pushed to users troubleshooting mobile login errors

Tag-based analytics miss unplanned behaviors — rage taps, repeated back-and-forth between fields, long idle times on a single step — because those events were never instrumented.

The result: one-size-fits-all interfaces, digital account opening abandonment that consistently runs above half of all applicants, and product teams reacting quarterly via aggregates instead of session-by-session.

The three mechanics of in-session banking personalization

Effective in-session personalization relies on three mechanics: intent identification, in-session guidance, and continuous journey optimization. All three depend on high-fidelity, privacy-first behavioral data captured from inside the web or mobile session: taps, scrolls, form interactions, navigation patterns.

Teams are moving away from weekly dashboard reviews toward automated friction detection that ranks issues by revenue impact. Banks that combine behavioral context with real-time orchestration tools can adapt flows as customers move through them, not hours later or in the next visit.

Mechanic 1: intent identification in critical banking moments

Intent identification means reading behavioral signals to infer what a customer is trying to accomplish in the current visit. Not what their segment profile suggests. Not what they did last month. What they are doing right now.

Specific signals to watch:

Repeated visits to the same loan interest rate section (rate shopping)
Quick scrolls past fees followed by long hovers on disclosures (concern about hidden costs)
Multiple failed attempts on document upload steps (KYC frustration)
Rage taps on validation errors or unresponsive buttons
Hesitation: 10+ second pauses on critical screens

These signals matter most in high-stakes flows: digital checking account opening, KYC completion on mobile, and first-time deposit setup. Banks are moving beyond static segments, using AI to analyze real-time behaviors and offer context-aware responses.

Fullcapture captures these signals automatically without manual tagging, so teams can retroactively analyze new flows as journeys change. Next Best Action models depend on accurate, real-time intent signals, and behavioral data is what feeds them.

Mechanic 2: in-session guidance and support without breaking the flow

Once intent and friction are detected, the experience should adjust in the same session, through contextual hints, dynamic UI, or timely human assistance.

Concrete examples:

Surfacing a tooltip explaining why an ID photo was rejected
Delivering personalized app experiences with explainers at the exact step where users stall
Triggering a live chat invite when a user toggles between terms and application pages multiple times

Fullstory Anywhere: Activation sends high-intent and high-friction signals to existing tools (chat, in-app guides, call center systems) so the right nudge appears at the right step. This isn't about replacing human bankers. Many consumers still value human interaction for complex or consultative decisions, making the human-digital handoff an important design consideration.

The key: guidance should feel helpful, not pushy. Tailor copy and UI to reassure users about security, eligibility, or time to complete. Behavioral session data tells you precisely where to intervene.

Mechanic 3: journey optimization using behavioral proof

The same behavioral data used for in-session guidance should feed continuous improvement of flows over weeks and months.

Product teams can review behavioral patterns to prioritize fixes by impact:

Where account opening drop-offs spike
Which KYC steps produce the most rage interactions
Which buttons attract dead clicks

Consider this example: a bank discovers that most abandonment in its checking application happens on the "employment details" screen. The team simplifies the fields from seven inputs to three, adds contextual help explaining why employment info is needed, and observes a measurable lift in completed applications within two weeks. AI-assisted analytics can automatically surface these high-friction steps and quantify their impact on completion rates, reducing the time teams spend on manual analysis.

Where in-session personalization matters most in banking journeys

Three journeys deserve particular attention: digital account opening, KYC completion, and first deposits or payments. These are high-abandonment flows where behavioral signals and real-time responses move the needle on completion and engagement.

Reduced abandonment, higher activation, and more engaged product usage tie directly to the revenue growth and customer acquisition metrics that matter to business leaders.

Digital account opening: reducing abandonment on mobile and web

Digital account opening is often the first impression of a bank's digital experience, and abandonment rates are consistently high, worse on mobile than web and especially steep at identity verification steps.

Behavioral patterns that signal trouble:

Users pinballing between marketing pages and application steps
Long pauses on "identity verification" screens
Repeated edits to address and employment fields
Back-navigation patterns that suggest confusion

In-session personalization helps: shortening the initial form for returning customers, pre-filling data where policy allows, showing a "time-to-complete" indicator once hesitation is detected.

Behavioral orchestration tools can trigger dynamic elements in the application UI, revealing a "Need help with documents?" link only when users stall on the upload step.

Before-and-after: a bank sees a sharp drop-off on step 3 of 5 in its checking application. The team uses behavioral analysis to simplify the fields and add context at the exact step where hesitation spikes, then observes a measurable lift in completed applications. The change costs two weeks of engineering time and no additional marketing spend.

KYC and identity verification: guiding customers through anxious steps

KYC is non-negotiable for compliance, but it is often where users feel the most uncertainty and privacy concern. Implementing personalization here requires careful handling of personal data under frameworks like GDPR and CPRA.

Behavioral friction signals to watch:

Multiple failed photo uploads (blurry, wrong format, file size)
Back-and-forth between FAQ and the KYC screen
Mobile users abandoning when asked for real-time camera access

In-session tactics that work: clarifying why information is required, providing examples of acceptable documents, offering alternative verification methods when repeated failure is observed, timing reassurance messages based on idle time.

Behavioral session analysis lets teams see exactly where confusion arose in anonymized sessions. The same signals also support behavioral data for fraud detection, making the data foundation do double duty.

Outcome: helping legitimate customers complete KYC more quickly while maintaining strong risk controls.

Deposits, payments, and card activation: winning early habit formation

The first deposit, first bill payment, and first card activation are critical moments where repeat usage habits form. Drop-off at bank-linking and activation steps is common, even among customers who completed the full account opening flow.

Hesitation behaviors to watch:

Users who reach "add external account" and then cancel
Cardholders who start activation but drop when asked for additional security codes
Customers who exit during bill pay confirmation screens

In-session personalization ideas:

Show a security reassurance at the exact moment of bank-linking ("Your credentials are encrypted and never stored")
Offer a quick explainer about how long external transfers typically take
Prompt "set up your first recurring payment" after a completed one-off payment

High-intent signals can also trigger helpful follow-ups after incomplete first deposit or activation attempts, tied to exactly where the user stopped rather than a generic campaign. Higher share of wallet, more active cards, and greater digital engagement within the first 30–60 days of a new relationship are the metrics that matter.

Building the data foundation for behavioral personalization

Strong personalization depends on privacy-first, comprehensive behavioral data, not just more of it.

The foundation requires capturing every interaction event across web and mobile: clicks, taps, gestures, form changes, navigation. And it requires doing this without engineers pre-instrumenting every possible event. Tagless behavioral capture allows teams to retroactively analyze new flows without re-instrumenting, critical for banks with rapidly changing digital products. This is what separates mature banking app analytics from traditional tag-based implementations: the ability to ask new questions of historical data without going back to engineering.

Privacy-first analytics is evolving from a nice-to-have into a compliance requirement. Banks must apply on-device redaction of sensitive fields, enforce strict access controls, and operate under frameworks like GDPR or regional equivalents. Consent management and data minimization are not optional; they determine whether personalization feels helpful or intrusive.

What a modern behavioral data foundation should provide:

Full coverage across web and mobile (every screen, every interaction)
Signal accuracy (precise timestamps, reliable event capture)
Privacy-by-design (on-device redaction, configurable exclusions)
Integration-readiness (APIs to CRM, AI tools, personalization engines)

Turning behavioral data into action with AI and orchestration

AI is most valuable when it works on high-quality behavioral data to surface friction patterns and opportunities automatically. AI-assisted analytics can summarize complex journeys, group similar problem sessions, and highlight the highest-impact issues for flows like account opening or payment setup.

Orchestration is the bridge from insight to action: streaming real-time behavioral context to AI customer service tools and personalization engines that can change the experience in real time.

Example loops:

Detect repeated failure on a mobile deposit step → update in-app guide copy the same week → monitor whether completion rates improve
Identify rage taps on a specific KYC screen → A/B test simplified copy → measure the completion rate change
Surface high-intent signals during card activation → trigger proactive chat invite → track reduction in support contacts

This approach supports human bankers rather than replacing them, surfacing where customers struggle so teams can intervene more effectively.

Measuring the impact of in-session personalization

Measurement is essential for earning internal support from risk, compliance, and finance stakeholders. Without clear metrics, personalization initiatives stay perpetually underfunded.

Core metrics to track:

Completion rates for key flows (account opening, KYC, first deposit)
Time to complete those flows
Abandonment rate at each step

Behavioral metrics that add nuance:

Frequency of rage taps on critical screens
Average idle time on conversion-critical steps
Volume of "can't complete" support contacts tied to specific journeys

Getting personalization right improves customer satisfaction, reduces churn, and makes cross-sell and upsell more relevant. The causal chain is measurable: fix the friction, watch completion rates rise, watch activation metrics follow.

Run controlled experiments on flow-level changes (adding contextual help vs. not) and use behavioral analytics to understand not only if conversion changed but why. Did rage taps decrease? Did idle time on the problematic step drop? Did support volume for that journey fall?

Example KPIs for roadmap discussions:

Deposit initiation completion rate
KYC step-by-step abandonment
Time from account open to first funded transaction
Active card rate within 30 days

From behavioral insight to banking experiences that work

Banks that close the behavioral data gap move from reacting to what customers did to responding to what they are doing. Static data identifies segments. Behavioral data identifies the customer in front of you, at this screen, right now. The mechanics (intent identification, in-session guidance, and journey optimization) are not separate projects. They run on the same data layer, and they compound.

See how Fullstory works with financial services teams.

4 Problems to solve with Fullstory and behavioral insights

The Fullstory Team — Tue, 28 Jan 2025 07:00:00 GMT

Many businesses collect mountains of data, but too often, it’s the wrong kind—or worse, that data goes unused. Behavioral data is the key to understanding and improving digital interactions, but having the right data isn’t enough. You need to know how to turn those insights into action.

What is behavioral data?

Every interaction on your website or app tells a story. Behavioral data captures these digital footprints, revealing not just what customers do but also how and why they do it. While traditional analytics show you surface-level metrics like page views and conversion rates, behavioral insights dive deeper into the user journey, uncovering patterns, friction points, and opportunities for improvement.

4 Challenges to tackle with behavioral insights

1. Reduce cart abandonment

The story is all too familiar: A customer fills their cart, starts the checkout process, then disappears. Traditional analytics might tell you where users abandoned the journey, but they don’t provide the insight needed to resolve the underlying checkout friction and stop it from happening again.

Was the user rage-clicking on a broken button? Maybe they were confused by a form, halted by a mobile-specific bug, or thrown off by a delivery estimate that made them second-guess their purchase. With Fullstory in your toolkit, you can see where and why you lost customers and stop history from repeating itself.

2. Fight fraud

As digital transactions continue to rise, so does the sophistication and frequency of fraudulent activities. Traditional fraud detection relies on rigid rules that can result in false positives and miss subtle patterns. Behavioral data offers a more nuanced approach. By analyzing how users interact with your site, you can spot suspicious patterns that might indicate fraud—whether it’s bonus abuse in iGaming fraud prevention or suspicious IP addresses in financial services. Then, you can set up customized alerts based on those suspicious activity patterns and make data-driven decisions about how to move forward.

Want to learn more? Discover how financial services businesses use Fullstory to fight fraud.

3. Personalize customer experiences

Traditional personalization relies on metrics like page views or purchase history to make broad assumptions about what customers want. This can lead to poorly timed interactions and generic experiences that frustrate or confuse users.

To implement meaningful personalization, you need context. Behavioral data shows exactly when and why customers get frustrated, which features they use most, and more. Armed with these insights, you can serve personalized promotions, offer targeted user support, and ensure users feel like humans with needs rather than metrics to optimize.

Real-life results: Learn how Pizza Hut's personalization efforts led to a 6.5% increase in total transactions.

4. Accelerate error resolution

Trust is fragile; every error, glitch, and broken interaction chips away at customer confidence. Our data shows that nearly 30% of clicks lead to an error. The longer it takes for you to find and fix issues, the more likely your customers are to jump ship.

Fullstory transforms error resolution in two critical ways. First, it enables proactive monitoring through rolling alerts that catch issues as they emerge, not after customers report them. Second, behavioral data provides the context teams need to act quickly, showing how and where errors affect user journeys.

Maximize the power of behavioral data with Fullstory

Behavioral data is the foundation of customer-centric decision-making, but collecting it is only the first step. Knowing how to turn those insights into action is where the real impact happens—and that’s where we come in. With Fullstory, you can bridge the gap between data and action.

Not sure where to start? Our comprehensive resource hub is packed with tips and best practices to help you:

Transform raw data into meaningful business outcomes
Create the personalized experiences your customers crave
Build a data-driven culture across your organization
Tackle critical challenges like cart abandonment and fraud head-on

Access the resource hub or request a demo to learn how Fullstory can help you solve your biggest digital challenges.

Nailing personalized app experiences in retail, travel, finance, and more

Dani Schenone — Tue, 08 Oct 2024 05:00:00 GMT

With 90% of app users dropping off within the first 30 days, mobile app owners in industries like retail, travel, financial services, and more face a steep challenge: maintaining user engagement and minimizing churn.

The pressure is on to provide seamless, bug-free experiences, as even a minor point of friction can quickly lead to user abandonment. But even more so, today’s app user wants you to know them and personalize their app experience accordingly.

Thankfully, Fullstory’s mobile analytics solution gives teams the data they need to deliver on the promise of seamless, personalized experiences. Below, we break down how Fullstory’s mobile app features can help.

1. Retail apps: Uncover friction to drive conversion

According to Statista, mobile commerce made up 60% of all global ecommerce sales in 2023. As the competition heats up, retailers must deliver on the promise of personalization. Fullstory for mobile apps can help retail data, product, and engineering teams:

Identify where customers drop off in the purchase journey: Event-based data tracks every user interaction, making it easy to find friction points during retail therapy.
Address user frustration in real time: Rage-click, forced restart, and dead-click detection highlights where users experience frustration, helping teams prioritize the fixes that make users happy again.
Optimize mobile checkout flows: With heatmaps and session replay, teams can identify confusing elements in the checkout process and errors experienced along the way–all to reduce cart abandonment.
Enhance performance with data-driven A/B testing: Fullstory’s segmentation tools help run A/B tests on new features or changes.

2. Travel + hospitality apps: Streamline the booking experience

Between 2016 and 2021, the portion of online travel sales via mobile devices grew from 36% to nearly 50% of all digital travel sales, making seamless mobile interactions vital for travel brands. Fullstory helps travel and hospitality teams:

Track complex user journeys across devices: From trip planning to post-booking management, Fullstory’s mobile analytics provides a clear view of where travelers drop off or encounter obstacles.
Optimize booking flows: With Fullstory’s conversion funnels, teams can find and fix bottlenecks that create a rough landing in the booking process.
Improve app performance in real-time: Through rage-click and error-click detection, teams can address issues before they escalate into negative reviews.
Personalize the travel experience: Fullstory’s journey mapping software allows teams to segment users and offer more tailored experiences based on individual behaviors and sentiment.

3. Food + beverage: Enhance mobile order efficiency

Mobile ordering is booming, with 67% of consumers in 2023 preferring mobile apps for food ordering. Fullstory's food and beverage analytics empowers teams to:

Pinpoint friction in the ordering process: By using heatmaps and session replay, teams can spot where users encounter difficulties when placing or customizing the ultimate Friday night feast.
Maximize engagement in loyalty programs: Fullstory’s event-based tracking helps teams to optimize loyalty rewards and promotions by understanding exactly how users interact with each element.
Address mobile payment issues: Checkout friction can lead to abandoned orders and hungry users, but Fullstory identifies where the payment process stalls, allowing teams to fix the issues quickly and address the hanger.
Troubleshoot real-time issues efficiently: Session replay offers a detailed look at bugs or lags in the app, helping digital teams resolve complaints quickly.

4. Financial services: Secure transactions and build trust

With over 68% of users engaging in mobile banking, personalization in banking is no longer optional. Fullstory helps financial services teams by:

Prioritizing app user privacy: Fullstory’s platform is built with data privacy compliance in mind, offering robust encryption and security features for sensitive financial data.
Improving the onboarding experience: Fullstory’s tools make it easier to identify friction in account setup, ensuring smooth in-app onboarding for new users that builds trust.
Optimizing in-app transactions: Through funnel analysis, teams can detect and correct issues, ensuring a seamless transaction experience while maintaining strict privacy controls.
Proactive customer support: Detailed session replays allow faster support resolution without compromising security.

5. Gambling + sports entertainment: Drive engagement and reduce churn

With a projected market volume growth of $132 billion by 2029, brands in this industry have a big opportunity to win. Fullstory helps digital teams win their next hand by:

Comprehensive engagement tracking: Fullstory’s cross-platform tracking enables teams to understand how users move between mobile games, sports betting, and digital events.
Identifying high-value users: Fullstory’s segmentation features allows digital teams to identify VIP users and create personalized banking experiences tailored to their behavior.
Preventing user churn: Session replay and frustration signal monitoring allow teams to spot where users get frustrated and drop off.
Improving app performance: Real-time app performance monitoring through Fullstory’s app analytics tool ensures fast, responsive experiences in high-stakes environments.

Choose the right mobile app analytics platform

Selecting the right mobile app analytics platform is serious business that can either make or break your ability to deliver a seamless and personalized user experience. Here’s a checklist to guide your decision:

Highly-performant session replay: Does the platform offer detailed session replay to give teams a clear understanding of the user experience–while prioritizing the end user? Is it a recording of the app (oh, no!), or screenshots (oh, my!)?
Heatmaps for user behavior: Can you visualize how users interact and scroll within your app? Or are you left in the dark?
Industry-leading frustration signals: Does the platform detect rage-clicks and error-clicks? What about force restarts?
Real-time, app-specific data and analysis: Can your platform monitor real-time interactions and provide instant feedback on issues? Does the platform specialize in mobile app insights?
Advanced segmentation: Does the platform allow segmentation based on user behavior to enable personalized experiences? Or are you getting only transactional and demographic data segmentation?
Event-based data tracking: Can the platform capture every interaction, from taps to swipes? Are you suffering from a lack of retroactivity?
Data privacy compliance: Does the platform ensure data privacy with encryption and adherence to regulations like GDPR or CCPA? Or is it jeopardizing app store terms?
Cross-device tracking: Can the platform track users across different devices seamlessly? Or are there holes in the user’s journey?

Fullstory is your go-to for mobile app analytics

Fullstory’s mobile app analytics solution offers the tools digital teams need to uncover friction points, enhance user journeys, and ensure high-quality app experiences. From retail and travel to financial services and more, Fullstory delivers the insights teams need to stay competitive.

Want to create mobile app experiences your users will love? Fullstory’s mobile analytics can help you spot issues, smooth out user journeys, and deliver the personalization that keeps customers coming back. Check out what Fullstory can do for your app.

4 ways Fullstory helps finserv leaders build better digital experiences

The Fullstory Team — Fri, 02 Jun 2023 04:00:00 GMT

Both the finance and insurance industries are fast-paced, competitive, and have an increasingly digital footprint.

Data highlighted in a recent Forbes article shows that 78% of adults in the US prefer to bank digitally via a mobile app or website. As that trend continues to grow, organizations that aren’t focused on digital initiatives are likely to be abandoned.

We dive into four ways that Fullstory customers in the finserv and insurance spaces are using the platform to build better experiences—sometimes in areas that go above and beyond the role of traditional analytics.

4 real-world use cases for DX analytics in the financial services industry

Fine-tune fraud and bot detection

Protecting against fraud is crucial for any online interaction—but it’s especially important when dealing with financials and banking data.

Finicity is a B2B open banking platform and financial data aggregation tool that allows customers to safely permission their financial data. In addition to delivering a 15% increase in conversions and an 80% reduction in ticket resolution time, Fullstory was able to help them bolster their existing fraud prevention measures.

Finicity uses Cloudflare to detect and block malicious activity like DDoS attacks, malicious bots, and other intrusions.

As a second line of verification, the company uses Fullstory to identify “false positives,” or legitimate interactions automatically blocked by Cloudflare, enabling them to make sure all their connections are successful.

Learn more about how Finicity enhanced its customer experience with Fullstory.

The pan-European pet insurer and pet health services provider Pinnacle Pet Group (PPG) covers more than 970,000 pets—and delivers peace of mind to their owners.

Fullstory was able to help PPG achieve a colossal 200% increase in conversions for its Everypaw subsidiary and provided a unique method to eliminate bot activity.

How did PPG reach a 310% increase in sales?

Estimates vary, but some sources report that bots make up over 60% of total internet traffic, with the majority being so-called “bad bots” like web scrapers, spam bots, or click bots. PPG utilizes Fullstory’s user segmentation functionality to detect bot activity patterns—like sudden traffic spikes or activity from unusual locations—and remove those sessions from key conversion rate reporting.

Teams using behavioral data to reduce churn often find that conversion rate optimization for financial services requires fixing the measurement layer before running a single test.

Effectively connect with different audiences

Australia-based Jacaranda Finance helps credit-worthy customers access fixed-rate personal and car loans and sets themselves apart with 24/7 customer service. After building a successful brand handling small loans, the company is moving upmarket, requiring them to speak to a new customer base.

After overhauling their loan application form to target these new customers, Jacaranda utilized Fullstory’s digital experience analytics tools to analyze user response on a granular level. Subsequent small improvements to the application form have led to a 20% increase in conversions.

Learn more about Jacaranda’s success with Fullstory.

With Fullstory, Finicity is able to drill down to assess customer health. The company builds a templatized Fullstory Dashboard for each customer account, allowing them to see how the customer moves through funnels and precisely what issues they may encounter.

Figure out what to test for

It’s never been easier to collect a whole range of data points—but it can still be difficult to realize where the actual pain points are.

Here are three Fullstory customers on how the platform helped their brands identify issues or areas of improvement they were previously unaware of:

“Fullstory enables us to identify those small things—those little experience unknowns—that will have an outsized impact on our KPIs,” says Jacaranda Head of Marketing Nick Yang. “In the old days, if a user abandoned the application form, we’d sit at our desks and guess the reason—or worse, we’d have to call them and ask. With Fullstory, we can see exactly what we need to do to make the user journey better.”

“Our marketing team has taken full advantage of Fullstory’s conversion rate optimization program,” says PPG Marketing Project Lead Nicki Lesirge. “It has allowed us to come up with a data-driven hypothesis regarding the best tests to run to increase our conversion rate.”

Wellstar, a large health system based in Marietta, Georgia, uses Fullstory to eliminate friction for patients across more than 300 office locations.

“Without Fullstory, we found it difficult to architect all the different analytics platforms and recognize hidden patterns that weren’t obvious to look for and build for,” says Rob Zanin, Wellstar’s Executive Director of Digital Consumer Products. “Fullstory allows us to spend less time doing retrospective setup so we can focus on future work.”

Detect issues at the source

Allowing you to see exactly what an individual user sees, Fullstory’s Session Replay is the closest you can get to being in your customer’s shoes.

Jacaranda uses Session Replay as an invaluable customer service tool—when faced with a complaint or question, their support team can quickly locate the exact issue and provide fast, hassle-free support.

PPG’s marketing team uses the feature to investigate bugs and user issues themselves instead of using expensive dev time. Session Replay allows team members to fully document what’s happening and determine whether the fix requires team involvement in a fraction of the time.

Curious about how Fullstory can improve your digital experience? Request a free demo today.

Insider Voices: Closing retail's biggest gaps with Emily Zerbe

Emily Zerbe — Tue, 21 Apr 2026 06:00:00 GMT

Retail teams often focus on the what: declining conversion rates, abandoned carts, and incomplete checkouts. But many overlook the critical why: why are users experiencing friction?

I recently sat down to discuss common blind spots that cost brands revenue and loyalty and how to address them.

Teams are stuck on the "what" and skipping the "why"

Conversions dropped. Cart abandonment is up. Users aren't completing checkout. Retail teams can rattle off the metrics, but I see a consistent pattern: they're not digging into the root cause. Is it a button that doesn't work? A mobile form that's too long? A promo code that was never set up correctly? The quantitative data tells you something's wrong. Behavioral data tells you what to fix.

Small frustrations add up to big revenue losses

User frustration in retail is rarely a single catastrophic moment. It's a death by a thousand cuts. A rage click here, a dead click there…the shopper might still convert today, but the data shows they come back less often and spend less over time. The brand is taking damage beneath the surface, and without tools designed to capture frustration signals, most teams never see it.

I worked with one retailer that added a carousel to their homepage and wanted to know if users were engaging with it. When her team dug into the data, they found the click area around the pagination arrow was far too small—users were trying to interact with it and missing. Fullstory's dead click detection surfaced the problem immediately. Without it, the team would have assumed the carousel simply wasn't resonating.

Shoppers may be getting lost before they find your products

Everyone optimizes product description pages. But product listing pages—the pages that lead shoppers to product detail pages—are consistently neglected. They're often just a grid of images with clunky filters and no real storytelling. Layer on search that can't handle a single misspelling, and shoppers get lost before they ever find what they're looking for. That's a massive top-of-funnel leak that most teams aren't paying attention to.

Real-time behavior beats historical data

My advice on personalization is simple: don't be creepy. Retailers don't need years of historical data to deliver relevant experiences. If a shopper is browsing black dresses right now, recommend more black dresses right now. Real-time behavioral signals are powerful and they sidestep the privacy concerns that come with mining deep purchase histories.

Loyalty won’t survive a fragmented experience

Shoppers don't think in channels. Online, in-store, mobile—to shoppers, all of these channels are just the brand. So when a guest checkout purchase doesn't sync to a loyalty account, or an in-store associate can't ship an out-of-stock item, it doesn't feel like a "system limitation." It feels like the brand doesn't have it together. And it has real consequences for repurchase behavior and long-term loyalty.

The biggest quick win? Fix mobile.

The single most impactful recommendation I can make is: optimize for mobile. Mobile traffic—both native and web—makes up a massive share of retail engagement, and yet one experience is almost always underfunded compared to the other. I've watched countless sessions of shoppers struggling to complete checkout on mobile, bouncing up and down the same screen. It's fixable. And it's costing brands real money.

Friction is fixable with Fullstory

Most retail teams aren't short on data. They have dashboards full of conversion rates, bounce rates, and abandonment metrics. What they're often missing is the behavioral layer underneath—the context that explains why those numbers look the way they do. That's the gap between knowing you have a problem and knowing how to solve it.

Fullstory bridges that gap, surfacing the friction that traditional analytics miss so retail teams can see what's happening, understand why, and prioritize the fixes that actually move the needle.

Ready to see it in action? Watch a demo to see how Fullstory helps retail brands like yours.

Insider Voices: Solving SaaS blindspots with Addison Price

Addison Price — Mon, 20 Apr 2026 06:00:00 GMT

SaaS teams have more data than ever before but still struggle to answer fundamental questions about user behavior. They can see what happened but they can't explain why.

I recently sat down to talk about the problems that come up in nearly every SaaS conversation and what teams can do to close the gaps.

Missing context costs organizations time and customers

There are three common problems that surface in nearly every conversation with SaaS companies:

Marketing teams can't pinpoint what makes their campaigns successful.
Product teams can't measure adoption in a way that truly reflects how different customer segments use their product.
Teams across the board can't proactively find and fix issues before they lead to churn.

These are critical, board-level questions that demand answers. When they go unanswered, the cost is time—time spent troubleshooting, time spent working with customers to resolve issues, and all the while, customers have more time to consider other options. The ability to proactively understand the "why" behind the "what" is what sets you apart from the competition.

Unify your teams with a single source of truth

Every team wants to improve the user experience. But when each team is working from different data sources, they end up with different definitions of what good looks like and use different metrics to back it up. The companies that succeed at experience-led growth eliminate those silos. When everyone is working from a single source of truth for behavioral data, it eliminates the arguments over conflicting data sources and allows teams to move in the same direction at the same pace.

Deliver exceptional experiences with the right data at the right time

From a lightning-fast homepage load to adding a new feature or submitting a support ticket, there are critical moments throughout the entire user journey. Teams across your organization play a role in each of these moments, and they need the right behavioral data to act at the right time.

Fullstory's own customer support team leverages the session summary API to get full context of what a user experienced before submitting a ticket. That means faster resolution for the customer who reported it and the ability to prevent the same issue from hitting other customers down the line.

Understand what your users do, not just what they say

SaaS teams invest countless hours in user interviews and qualitative research, but often come away with only a handful of useful insights. This process is time-consuming, expensive, and limited by what users can articulate.

Behavioral data fills this gap by showing you what users actually do, not just what they say they do. Product teams that harness behavioral data to drive their decisions deliver better experiences, and better experiences lead directly to higher retention and more expansion revenue.

Get ahead of churn by focusing on adoption

At its core, churn often comes down to a lack of adoption. SaaS companies struggle to get customers to fully embrace their product, and by the time they notice a drop in adoption, it's often too late.

Fullstory empowers teams to not only track adoption but also to get alerted when it begins to decline and to segment that data by customer type. This means you can see whether the problem is concentrated in your enterprise accounts, your growth segment, or both, and act accordingly before it turns into a cancellation.

Close the gap between data and decisions with Fullstory

SaaS teams aren't short on data. They have dashboards, analytics platforms, and reporting tools stacked on top of each other. What they're often missing is the behavioral layer underneath — the context that explains why those numbers look the way they do. That's the gap between knowing you have a problem and knowing how to solve it.

Fullstory bridges that gap, bringing together behavioral data and AI-powered insights so teams can see what's happening, understand why, and prioritize the fixes that actually move the needle.

Ready to see it in action? Request a demo to learn how Fullstory helps brands like yours.

The two sides of AI: Reactive and proactive approaches explained

Addison Price — Tue, 04 Feb 2025 06:00:00 GMT

Sit through any product demo today and you’ll hear “AI” mentioned at least ten times. And while some instances might just be buzzwording, there’s ultimately good reason for the excitement.

From intelligent chatbots capable of providing instant customer support to tailored product recommendations, AI and UX are evolving together to transform online experiences in powerful ways. That transformation is especially powerful when AI models are applied to behavioral data. Those paying close attention in those product demos may have started to notice two distinct approaches to AI, shaping how businesses engage with their audiences:

Reactive AI focuses on analyzing past behavior to surface insights about what’s already happened.
Proactive AI looks forward, using historical data to predict what’s coming next.

Reactive AI

Reactive AI focuses on making complex data more digestible. It enhances usability by transforming an overwhelming pile of user event data into a metric of your choosing.

Example: A website owner asks an AI chatbot, “How many conversions did I have yesterday, and what friction points did users encounter?” The AI responds by synthesizing this data into actionable insights: it generates a conversion metric and summarizes the user experience, highlighting key friction points along the conversion journey.

This is obviously a powerful workflow that allows you to understand what's going on in your user experience. The drawback here is that the analytics you’re getting are things that have already happened. If the metric generated from the query surfaces a problem, you’re now scrambling to get a fix pushed out as quickly as possible to stop the bleeding.

Proactive AI

Proactive AI, on the other hand, aims to anticipate user behavior based on behavioral data patterns, all without a user explicitly stating their intention or asking for help (through a website chatbot, for example). It transforms what were once just data points—traditionally visualized as dashboard trends—into predictive insights that empower real-time decision-making. Example: A website owner sends all their behavioral data into a data warehouse and utilizes a behavioral data model that identifies patterns linked to conversions. The model receives a real-time data feed from a user's session and makes a prediction of the outcome of the user’s session. Based on these predictions, a signal can be sent to a CMS so that they can personalize the user’s experience dynamically. For instance, offering the user a discount code at a critical moment in their checkout flow or recommending products to increase their likelihood to purchase based on behaviors exhibited by similar users. As with Reactive AI, historical data is also important in this context, but instead of reacting to the data in a more delayed manner, you’ve got the power to influence the success of your users’ visits in real time.

The best of both worlds

There are lots of behavioral data providers out there today that have put their stake in the ground by focusing solely on reactive uses of AI. At Fullstory, we recognize that it’s not a matter of one vs. the other; it’s both. In fact, our customers use both to serve a large range of use cases. Reactive AI works well for analyzing trends and finding strategic opportunities to adjust the user experience, like redesigning how customers move from their cart to a completed purchase. By using these two approaches, platforms in the behavioral data space can either react to the past or use it to anticipate the future, enabling businesses to enhance user experiences and drive meaningful outcomes.

Discover the possibilities of both approaches with Fullstory today.

Online gambling fraud prevention: Stay compliant with real-time detection

Fullstory — Fri, 22 Aug 2025 07:00:00 GMT

Online gambling operators face rising pressure to meet AML and KYC requirements while protecting players and driving growth. Fraud detection is still quite manual and time-consuming in many cases, making it difficult to act quickly. Operators need to spot risky behavior in real time without disrupting loyal players.

In 2024 alone, gambling and betting sites lost more than $1B to fraud, with rates rising 64% since 2022, evidence that the problem is escalating fast (GamblingIQ).

The challenge with identifying fraudulent gambling behavior

Most monitoring systems use static rules to flag potential issues. These systems often trigger too many false positives, making it harder for teams to focus on real problems. Delays in fraud detection don’t just mean missed opportunities; they create revenue loss and distort key performance metrics.

When KPIs like average deposit size or conversion rate suddenly spike, it may signal fraud rather than success. Leaders across product, operations, and the C-suite need confidence that growth is real, not risk in disguise.

Real-time behavioral fraud detection helps gambling operators protect legitimate players and stay compliant.

Common fraudulent behaviors in iGaming

Common fraudulent behaviors might look like:

Bonus abuse: Creating multiple accounts or using fake identities to repeatedly claim welcome offers or referral bonuses.
Multi-accounting: Operating several accounts to bypass limits, avoid detection, or exploit promotions.
Gnoming: Coordinated use of multiple accounts by different users to manipulate outcomes or distribute winnings.
Bot-like behavior: Using automated scripts or tools to place bets, redeem bonuses, or simulate real user activity.
Chargeback fraud (friendly fraud): Depositing funds, placing bets, and then disputing charges to reclaim money.
Money laundering: Moving illicit funds through low-risk bets and rapid transactions across multiple accounts.
KYC and AML evasion: Bypassing identity checks with false documents, synthetic identities, or account cycling.

These signs can appear subtle at first—frequent failed verification attempts, rapid switching between pages, abandoned deposits or bonuses, and other indicators that something isn't quite right.

On their own, these actions may not raise concern. But together, they can point to a larger issue.

Bonus abuse

Bonus abuse is one of the most prevalent types of iGaming fraud. Players may use fake identities, VPNs, or TOR browsers to create multiple accounts and repeatedly claim welcome offers. In some cases known as affiliate fraud, these fake accounts are tied to referral scams to game bonus payouts even further.

Watch for: Repeated use of promo codes, similar device/browser fingerprints across accounts, or sign-ups with slightly varied email addresses.

Multi-accounting

Players may create multiple accounts to bypass betting limits, evade self-exclusion policies, or avoid detection. This undermines responsible gambling protocols and pollutes your customer data.

Watch for: Overlapping IPs, shared payment methods, device reuse,rapid login-logout cycles, or sign-ups using temporary email addresses—a tactic QA teams sometimes adopt for quick testing.

Gnoming

A form of coordinated multi-accounting, gnoming involves multiple users colluding to manipulate betting outcomes or share winnings. It’s especially hard to detect without behavioral context.

Watch for: Synchronized activity between accounts, shared network indicators, or patterns tied to specific promotions.

Bot-like behavior

Fraudsters sometimes use automated scripts or tools to place bets, redeem bonuses, or mimic user behavior at scale. These bots can overwhelm systems with low-value activity or exploit betting mechanics—often hiding in plain sight without detailed behavioral data.

Watch for: Unnaturally fast movements, repetitive click paths, or sessions with no pause or hesitation—signals that become clearer when you can see the full user journey.

Chargeback fraud (friendly fraud)

Some users deposit funds, place bets, and then file a dispute with their bank to reclaim the money—leaving the operator to absorb the loss. This tactic, known as chargeback fraud or friendly fraud, is especially tricky because it comes from seemingly legitimate accounts.

Watch for: Disputed charges after recent wins, repeated chargeback behavior, or fast deposit-to-withdrawal activity.

Money laundering

Fraudsters use gambling platforms to wash illicit funds—often through small, low-margin bets or rapid transactions across multiple accounts. Known as layering, this method can go unnoticed without deep behavioral and real-time transactional insight.

Watch for: High-volume, low-risk bets, unusual deposit and withdrawal patterns, or account connections across regions.

KYC and AML evasion

To avoid identity checks or AML screening, some players submit fraudulent documents or cycle through new accounts. Failure to catch this behavior creates legal exposure.

Fullstory gives teams the building blocks to configure a dashboard that surfaces these markers in real-time, layering behavioral clues over your backend data so you can gauge risk instantly. Analysts can jump straight from an alert to the exact session replay to validate intent and prioritize the next move.

Watch for: Repeated document submissions, incomplete KYC steps, mismatched user and payment info, or unusual drop-off points in the verification flow.

Each of these behaviors is serious on its own. But when they start to layer, they can signal something bigger. The key is being able to connect the dots early, with enough behavioral context to act fast.

While fraud losses continue to climb, technology is making a difference. Up to 50% of operators are now successfully blocking fraudulent activity with advanced solutions. Still, many remain exposed, and traditional approaches alone aren’t enough.

Why operators trust Fullstory for iGaming security

Unlike traditional flagging systems, Fullstory helps target the right players instead of casting a wide net.

Teams can move beyond static rules by pairing behavioral data with existing fraud tools through Anywhere: Activation. Session replay lets analysts confirm intent and triage alerts, while AI-driven anomaly detection and behavioral biometrics reveal patterns traditional systems miss. Real-time monitoring connects fraud signals with digital behavior for faster, more accurate action.

Built on Google Cloud’s AI-driven infrastructure, Fullstory efficiently processes large volumes of behavioral and transactional data, helping operators act faster and stay compliant with responsible gambling regulations.

The impact: Lower fraud losses, stronger AML compliance

By moving from lagging, blanket flagging systems to a behavioral data approach, operators can satisfy AML and KYC mandates in real time, spotting bonus abuse, chargebacks, and money-laundering attempts before they breach regulatory thresholds.

The safest bet? Detect iGaming fraud before it raises the stakes

Request a demo today.

Meet AI agents: What they are and how to spot them

Fullstory — Fri, 31 Jan 2025 06:00:00 GMT

AI agents aren’t some far-off, sci-fi imagining, they’re here, shaping how we interact with the internet. Tools like Open AI’s Operator or Google DeepMind’s Project Mariner are navigating sites like yours, executing tasks, and reshaping online interactions.

Brands have spent their entire online existence optimizing for human users, designing experiences around the way people browse, shop, and engage. But AI agents don’t browse; they execute. They don’t hesitate or get distracted by another tab; they move with one goal in mind. If your site isn’t built to recognize and adapt to this shift, you could be missing valuable opportunities, like making your site more accessible to those that will rely on operators, or opening yourself up to security risks.

This shift is being driven by the rise of the agentic workspace, a new era in which humans act as conductors, defining objectives while AI agents execute manual tasks across various platforms and websites. In this article we’ll break down how AI agents are different from other site visitors, and equip you with ways to identify them when they land on your site.

The new kid on the block

Operators stand apart from bots and humans, but take cues from both. Bots follow scripts, sticking to set paths like search engine crawlers or spam tools. Operators, on the other hand (other keyboard?), are more advanced, adapting, learning and sometimes even mimicking human decisions. Humans, of course, are the wild cards, clicking around unpredictably, getting distracted, and engaging in ways that AI simply can’t replicate.

Supportive operators (or “good” operators for non-tech readers) include AI-driven customer assistants, research bots, and automation tools that enhance UX by making interactions smoother and more efficient.
Disruptive operators (“bad” >:( ) are data scrapers, competitor tracking bots, and fraud-motivated automation tools that manipulate engagement metrics or steal proprietary data.

With this in mind, let’s break down how to detect these AI users.

Signals and tips on detection

Behavioral patterns and anomalies

AI agents move differently from humans. Businesses can detect AI-driven activity by looking for patterns like:

Blazing-fast interactions: AI agents can fill out forms, click links, and complete transactions at speeds no human could dream of.
Repetitive behavior: While humans explore, AI follows direct, efficient paths—repeating the same actions over and over.
Suspiciously consistent sessions: Either too short or eerily uniform, AI sessions lack the natural variation of human browsing.
Ignoring distractions: No time for ads, pop-ups, or banners—AI agents focus only on the core functionality of a page.
Skipping optional steps: They breeze through forms and checkout flows, completing tasks in record time.
Unusual navigation paths: AI may jump unpredictably from one section to another in ways that make zero sense compared to typical user behavior.

Checking request IP address and metadata

Most AI agents leave behind digital breadcrumbs in their HTTP requests. You can spot them by analyzing:

Non-standard user-agent strings: AI tools often have identifiers that don’t match typical consumer browsers. Unlike traditional web crawlers, which usually announce themselves and follow guidelines like robots.txt, AI agents don’t have a consistent standard for identification—though ideally, that will change as the industry evolves.
Data center IP ranges: Requests originating from known cloud providers often fall within publicly documented IP ranges, signaling that the request is coming from a data center rather than a residential location. While not all requests are from AI agents, they indicate non-consumer traffic.

Bot detection tools

Traditional bot detection still plays a role in identifying AI-driven activity. Businesses can use:

CAPTCHAs and reCAPTCHAs: Identify non-human agents by introducing challenges that are difficult for bots but easy for humans.
Bot management platforms: Use a service to analyze traffic patterns and flag potential bots.

Monitoring API usage

AI agents can bypass browsing webpages altogether and interact with brands through APIs. Keep an eye out for:

High request frequencies: AI agents send far more requests than a human could.
Access attempts on endpoints not set up for users: AI might try to reach internal APIs that regular visitors wouldn’t use.
Odd or unexpected request payloads: AI-generated interactions may not align with typical user data.

Ethical flags by agents

With the rise of AI adoption, some developers are building in transparency measures. That can look like:

AI agents that openly identify themselves in HTTP headers.
Self-disclosing AI activity in logs: Some AI services provide information about their interactions for transparency.

Traffic and log analysis

Your website logs are a data goldmine. By analyzing them, you can:

Spot sudden, unexplained traffic spikes: AI activity often results in unusual traffic surges.
Identify repetitive access patterns: Look for clusters of near-identical requests from the same IP range, bursts of activity at fixed intervals, or repeated actions using similar information. For example, a credit card company recently detected 10,000 bot-driven prequalification applications originating from a single city. The bots used the same IP ranges, submitted applications in bursts of ~250 per IP, and followed identical interaction patterns—both in timing and the data they provided.

Our response to AI agents

Fullstory is keeping a close eye on AI agents and the way they’re changing digital experiences, and we’re actively working to help businesses stay ahead. Our engineering teams are exploring faster ways to detect AI-driven interactions, giving you a clear breakdown of who, or what, is on your site. Whether it’s differentiating helpful automation from disruptive bots or preventing AI-driven noise from skewing your data, we’re focused on making sense of this shift—so you don’t have to.

Keep up with the latest in AI and behavioral data with our curated resources.

Description vs. diagnosis: The gap in the employee productivity market

Brent Kinman — Wed, 15 Apr 2026 06:00:00 GMT

Employee productivity solutions have a measurement problem. Organizations have more tools and data than ever before, but those tools were built to measure specific things, and they consistently miss the bigger picture. The result is that organizations are making decisions about performance and productivity with partial information, often without knowing it.

The primary goal of employee productivity initiatives is straightforward: to increase the value generated per unit of labor. Typically, these efforts center on two main areas of value:

Efficiency: Minimizing steps, reducing time, and lowering costs per outcome
Effectiveness: Adhering to standards and ensuring quality

Both matter. And both are being measured badly by tools built for only one of those dimensions or for a different purpose altogether.

Getting the diagnosis right is the hardest part of the problem

Organizations typically hunt for productivity gains across three domains: tools, processes, and people. While these pillars are deeply interconnected, the challenges in one domain routinely masquerade as problems in another. What looks like a people problem might actually be caused by inflexible software or ineffective workflows.

The interconnection makes diagnosis difficult. A broad solution aimed at the wrong domain doesn't just fail to fix the problem; it generates noise that makes the next diagnosis harder. The original friction keeps compounding, buried under the evidence of a failed fix.

You can’t find an effective solution without correctly identifying what you're actually solving for.

The market offers three answers. None of them is complete.

The market currently offers three distinct categories of employee productivity tools, each with genuine capability but also hard structural limits. A tool built for one domain can’t fix another, and deploying a targeted solution in any one area requires real understanding of the other two.

1. Employee experience analytics

Employee experience (EX) analytics treats the enterprise employee as a customer and internal software as the product. Tools like Pendo, WalkMe, and Nexthink capture how employees interact with software: click paths, navigation patterns, application performance, and sentiment. Fullstory has also historically been in this category.

These solutions see the interface between human and tool, but they can’t see whether any of that activity was necessary. A clean tool experience and a broken work process can coexist without a single signal appearing in the data. EX has no frame of reference for the work itself. It only sees the tool, the user, and the session.

Process mining

Process mining reconstructs end-to-end workflows by extracting event log data from ERP, CRM, and ITSM platforms. Tools like Celonis and SAP Signavio show where documented workflows diverge from reality, where rework loops exist, and where compliance breaks down.

But process mining only sees what happens inside those systems. The moment work moves outside them, it loses the thread. If an employee spends four hours in a spreadsheet before submitting a record in the ERP, process mining sees a four-hour gap. It can tell you that 23% of tickets require a rework step. It can’t tell you why.

Task mining

Task mining gets closest to the work itself, capturing keystrokes, clicks, window switching, and application opens across the desktop. Tools like Mimica and Skan use computer vision to cluster those events into identifiable tasks, surfacing the exact copy-paste sequences and application switching patterns that explain what process mining can only map.

The structural limitation is context. Task mining can tell you that an agent visited Salesforce six times during a single case but it can’t tell you whether that pattern is an anomaly, a best practice, or a symptom of a process failure three steps upstream.

Why assembling a stack doesn’t close the gap

The natural enterprise response to this problem has been to assemble. An EX analytics platform for tool instrumentation, a task miner for low-level activity capture, a process miner for higher-level workflow mapping. Three tools, three data models, three partial answers to one complete question.

But the data quality issues compound. The reconciliation falls to a person. And the insight that matters—not just that a process is slow, but why it is slow at this specific step for this cohort of employees—requires all datasets simultaneously within a single analytical frame. Assembled stacks do not produce that! They produce partial answers and leave a human in the middle doing the translation work. The combined stack designed to surface invisible, unnecessary labor ends up creating its own version of it.

The gap the market hasn’t closed

Today's employee productivity solutions were built to describe a problem, not explain it. They can tell you that a process is slow, that a tool has friction, or that a workflow has a rework loop. They can’t tell you why any of it is happening or where to intervene.

Description is not diagnosis. And the gap between the two is where enterprise productivity is being lost. Closing that gap requires something the current categories weren’t designed to provide: a single analytical frame that can see the tool, the work, and the context simultaneously.

That's a different kind of solution entirely.

Give your Adobe AI digital sight with behavioral intelligence

Jackie Ziznewski — Fri, 17 Apr 2026 06:00:00 GMT

We’ve entered the era of the intelligent digital experience. Businesses increasingly rely on AI to create adaptive, real-time customer journeys. Adobe’s platform is a foundational tool for this transformation, surfacing critical quantitative data like who clicked, what they bought, and where they dropped off.

But AI built on quantitative data alone is like a detective who can read the case file but can’t visit the crime scene. It has the facts, but it’s missing the human context.

The difference between a static map and digital sight

To become truly intelligent and predictive, your AI needs more than data; it needs sight. Your Adobe stack tells you what happened, but it can’t always tell you why. That drop-off in your conversion funnel isn't just a number; it’s a collection of real human experiences. Was the button broken? Was the promotional code confusing? Was the key information below the fold?

The problem runs deeper than missing context, though. Revealing and understanding customer interactions one event at a time is insufficient for AI. Think of it like a Lite-Brite: you can build a picture by poking through one point of light at a time, but the resolution is low, the speed is slow, and the full image never quite comes into focus. That's how most AI systems are experiencing your digital properties today: one tagged event, one tracked click, one logged transaction at a time. When your AI can only see a handful of isolated data points out of the hundreds of signals that occur in any given session, it isn't just working with incomplete information. It's working with a distorted picture of reality.

Without insight into the user's actual experience at that full resolution, your AI is essentially blind. It can react to data, but it can't anticipate needs or understand intent.

Fullstory provides the essential behavioral intelligence layer—the digital sight—that unveils the ‘why’ behind the ‘what’. By capturing the granular, high-fidelity ground truth of your customer's digital experience and feeding it directly into your Adobe AI, Fullstory transforms it from a reactive optimization engine into a strategic, predictive partner.

Here’s what that transformation looks like in practice.

1. Stop losing sales to friction

Without Fullstory: A customer gets stuck on a broken promo code button. They click repeatedly in frustration and leave. Hours later, Adobe Journey Optimizer (AJO) might send a generic "come back" email, but by then, the customer has already moved on to a competitor. The opportunity is lost.

With Fullstory + Adobe: The moment a user rage-clicks, Fullstory streams a "hesitant shopper signal" to AJO. This instantly triggers a "rescue journey," sending them a personalized message with a working code to save the sale in real time, while they are still on your site.

2. End wasted ad spend with smarter audience segmentation

Without Fullstory: Marketers target audiences based on crude metrics like page views or time on site. This leads to inefficient campaigns that target users who have no real intent to buy, wasting valuable marketing dollars.

With Fullstory + Adobe: Marketers can create hyper-targeted audiences in Adobe Experience Platform based on observed intent. Imagine building a segment of "users who showed 'high confusion' on the pricing page" or "users who engaged deeply with the ROI calculator." Every dollar of ad spend becomes smarter because it’s aimed at genuine behavior.

3. Move from insight to action in minutes, not sprints

Without Fullstory: The marketing team has a brilliant idea for an A/B test on a new feature. They file a ticket with engineering to get the new interaction tagged. Weeks—or even a full sprint—later, the event is finally tracked in Adobe Analytics, but the window of opportunity has passed.

With Fullstory + Adobe: The marketing team uses Fullstory's codeless UI to define a new behavioral event and streams it directly to Adobe Target. They can launch the A/B test the same afternoon, accelerating agility and innovation.

Enriching the entire Adobe ecosystem

This partnership makes your entire Adobe investment more valuable:

In Adobe Analytics, you can click directly from any report to a Fullstory session replay to instantly see the ‘why’ behind a drop-off.
In Adobe Experience Manager (AEM), you can use heatmaps to see which components are being ignored or causing "dead clicks," allowing you to optimize layouts based on real user gestures.
Adobe Journey Optimizer (AJO) leverages Fullstory's real-time frustration triggers to automatically activate support and discounts when high-value customers encounter friction.

The future is context-driven

To win in the AI era, you must close the context gap. By pairing Adobe’s powerful analytics with Fullstory’s behavioral intelligence, you give your teams and your AI the digital sight needed to anticipate customer needs, build durable loyalty, and remove friction before revenue disappears.

Stop making your AI guess. It's time to let it see.

→ Want to see behavioral intelligence in action? Request a demo here.

Application rationalization: Your answer to tech bloat and tool fatigue

Maggie M. — Tue, 03 Jun 2025 11:00:00 GMT

Your tech stack should be an advantage, not a drain. But often, IT teams end up managing bloated systems, scattered tools, and a mounting cost that limits growth.

This headache isn’t new. Over the last decade, the number of offerings for every tiny microtask in a workflow has boomed. And while that's not inherently bad, it does mean the chances of picking a puzzle piece that doesn’t quite fit your needs are high. That often leads to paying for tools that do the same thing, licenses no one uses, and systems that don’t play well together. In fact, almost one-third of software spend is wasted on unused licenses — a clear sign of how easy it is for tech stacks to get out of control.

And with worldwide IT spending expected to increase by 9.3% from 2024 (Gartner), there’s more pressure than ever to ensure that every dollar adds real business value — not more tech waste.

In our guide below, we’ll walk you through how to clean up your application portfolio so your team can spend less time untangling tools and more time getting the work done.

What is application rationalization?

Application rationalization is the process of reviewing, optimizing, and reducing your tech stack to ensure each application contributes to real business value. It’s less about cutting tools and more about making sure the right ones stay.

Beyond cost savings, rationalization gives teams a better experience as they tackle their work. When low-value or redundant apps are removed, you can reallocate resources to more impactful work. This process is a trademark of IT modernization and a commitment to forward thinking.

6 steps to effective application rationalization

Step 1: Identify needs and assess readiness

Kick-off rationalization by scoping the effort and getting leadership buy-in.

Align with strategic goals: Meet with business leaders to define key objectives—cost reduction, improved productivity, or innovation.
Gauge organizational readiness: Evaluate IT infrastructure, team capacity, and willingness to change.
Stakeholder survey: Develop a questionnaire to identify current pain points and determine how applications are used.

Step 2: Inventory your application ecosystem

Build a baseline inventory—without it, you’re flying blind.

Create a comprehensive list: Include app names, versions, vendors, and business functions. This can be done manually, via each team adding their tools to shared documentation, or automatically with software that logs each tool as employees use them.
Capture key metrics: Usage frequency, dependencies, user satisfaction, technical requirements, and support status.
Cross-check data: Validate inputs through interviews or surveys to ensure accuracy.

Step 3: Assess each app’s business value and technical fit

Not all applications are equal, and that’s okay.

Business value assessment: Identify apps critical to core functions or revenue generation.
Technical evaluation: Review stability, performance, scalability, and security.
Dependency mapping: Determine how each app interacts with others—identify potential bottlenecks or redundancies.

Step 4: Calculate total cost of ownership (TCO)

TCO isn’t just the sticker price—it’s the hidden costs that add up.

Direct costs: Track licenses, hosting fees, and vendor contracts.
Indirect costs: Consider maintenance, staff training, downtime, and upgrades.
Spot cost inefficiencies: Identify high-cost apps with low usage or minimal business impact.

Step 5: Score and categorize applications

Think of this as triage—decide which apps to keep, improve, or retire.

Develop a scoring model: Assign weighted scores based on business value, technical health, and TCO.
Categorization framework:
- Keep as-is: High-performing, essential applications.
- Modernize: Apps needing updates to improve functionality or security.
- Consolidate: Merge overlapping or duplicate tools.
- Retire: Low-value or outdated apps.

Step 6: Execute and monitor the application rationalization strategy

A plan is only as good as its execution.

Prioritize changes: Focus on high-impact applications and quick wins.
Change management plan: Develop a phased approach to minimize business disruptions.
Feedback loop: Continuously gather user feedback and performance metrics to ensure success.
Ongoing monitoring: Regularly review the application portfolio to maintain efficiency and alignment with evolving business needs.

Benefits of application rationalization (beyond cost savings)

While controlling spend is a big win, rationalization unlocks much more than budget line savings. When done right, it frees up time, removes friction, and connects teams with the tools they actually need to do great work.

More seamless workflows, fewer roadblocks

When employees are forced to navigate redundant apps, disjointed interfaces, or extra manual steps, employee productivity takes a hit. Rationalization streamlines how work flows across departments by eliminating low-value tools and focusing on those that actually support day-to-day tasks.

Result: Better focus, faster execution, and fewer “swivel chair” moments across platforms.

Stronger IT visibility and faster issue resolution

Modern IT teams need more than app audits—they need context. Rationalization—especially when powered by behavioral data—gives IT a clear picture of how tools are used, where friction exists, and what’s slowing people down. And it helps them resolve issues faster, often before a ticket is even filed.

Result: Fewer support delays, faster digital fixes, and more empowered IT teams.

A better digital experience for employees

Simplifying your tech stack does more than clean up clutter—it reduces frustration, shortens onboarding time, and improves digital employee experience. Less tool-switching means less mental fatigue and more flexibility to focus on meaningful work.

Result: A more productive, less distracted workforce—and a tech stack people actually want to use.

Control over risk and shadow IT

Unmanaged or unapproved apps, also known as shadow IT, can introduce serious security vulnerabilities and compliance concerns, complicating SaaS spend management. And IT leaders know it: According to JumpCloud, 68% of admins believe cuts to security budgets increase organizational risk. Rationalization closes those gaps by shedding light on the full application landscape, including the tools teams adopt on their own.

Result: Smaller digital footprint, fewer blind spots, and a stronger security posture.

Smoother integration during mergers and growth

When teams come together through M&A or other org changes, duplicate systems can create confusion. Application rationalization acts as a blueprint for aligning tools, eliminating overlap, and creating a unified foundation for scale.

Result: Less redundancy, faster integration, and easier expansion with a clean slate.

Pitfalls to avoid during rationalization

Application rationalization has massive potential—but without the right foundations, even well-intentioned efforts can stall out, or worse, backfire. These challenges often don’t appear until you’re deep in the process—here’s how to avoid them from the start:

1. No shared definition of success

Too often, rationalization efforts start with vague objectives like "cut software spend" or "clean up tools." Without clear metrics and alignment across teams, projects lack urgency and support.

Tip: Anchor the initiative to specific outcomes tied to larger business goals: reduced support overhead, faster in-app onboarding, cleaner workflows, etc. This increases buy-in and benchmarks progress.

2. Decisions made in isolation

IT and procurement might lead rationalization—but without input from end users, you risk sunsetting tools employees depend on and underestimating problem areas.

Tip: Engage functional leaders and frontline users early. Internal surveys or behavioral insights help surface hidden dependencies and real pain points.

3. Taking app logins at face value

Just because someone opened an app doesn’t mean they used it effectively or wants to keep it. Login activity is a weak signal of value—and can lead to false positives in your adoption scoring.

Tip: Use behavior-level data (like time in app, key action completion, or task journeys) to measure real engagement—not just access.

4. Undervaluing the support impact

Many rationalization efforts fail to account for the lift on IT support during and after changes. New tool rollouts, retirements, and updates generate questions, bugs, and delays.

Tip: Give support teams early visibility into what’s changing and where new friction might surface. If possible, track employee frustration or support tickets to spot where additional help is needed.

5. Treating rationalization like a one-time event

It’s easy to treat this as a single sprint: audit your tools, trim the excess, and move on. But without mechanisms for ongoing tracking, the same issues re-emerge within months.

Tip: Build ongoing visibility and evaluation into your tech management playbook. Use periodic reviews to align your stack with real usage and business changes.

How Slalom used Fullstory Workforce

Make your tech stack lighter, faster, and easier to use with Workforce

Application rationalization helps teams remove unnecessary complexity and focus on what matters. When the tech stack becomes easier to manage, the work becomes easier to do. Employees can stay productive, budgets stretch further, and IT has more control over security and spend.

Tools like Workforce make that progress possible. With insight into how employees interact with applications, IT teams can identify underused software, eliminate friction, and create a more focused digital environment.

Application rationalization is a practical way to reduce complexity, improve software usage, and free up budget. It helps IT teams create a focused, flexible environment that supports both employee experience and long-term business goals.

Optimizing employee workflows: How Fullstory Workforce drives AI adoption

LP — Thu, 31 Jul 2025 06:00:00 GMT

While the promise of AI for business growth is undeniable, many organizations find themselves grappling with a critical question: Are our teams embracing AI as part of their day-to-day workflows? It’s one thing to invest in cutting-edge AI solutions, but it’s another to see them integrated into processes and delivering tangible value.

Fullstory Workforce bridges the crucial gap between knowing AI is important and using it effectively.

AI adoption in the enterprise

Organizations worldwide recognize AI as a strategic driver of growth and competitive advantage. Surveys and industry trends consistently show high awareness and significant investment in AI initiatives, from generative AI for content creation to AI-powered automation for repetitive tasks. Yet, a pervasive "black box" often surrounds AI implementation and the impact on business outcomes.

Why do so many AI initiatives, despite hefty investments, fail to deliver their expected results? The challenges are multifaceted:

Lack of clear usage data: Leaders do not have visibility into how employees use AI.
Disconnect between strategy and execution: Leadership's choices around AI tools may not align with real-world employee workflows.
Resistance to change or inadequate training: Employees may be reluctant to adopt new tools or may not have the support needed to use them effectively.

The reality is that the true ROI from AI hinges on its embrace by the workforce. Without widespread, effective adoption, AI initiatives can quickly become piles of costly, underutilized assets.

Unlocking employee AI adoption with Workforce

Workforce goes beyond reporting on which AI tools are in a company’s tech stack; it gives unprecedented visibility into how employees engage with AI tools. It captures detailed behavioral data on how employees interact with digital tools, and shows:

Feature usage: Are employees using the full breadth of AI capabilities or just the basics?
Usage depth and frequency: Is AI woven into daily workflows or used sporadically?
Workflow integration: Do AI tools blend seamlessly with other apps, or cause friction?
Adoption barriers: Where do users get stuck, frustrated, or drop off in their AI interactions?

These insights make it easier to support employees with the right training, adjust tools to match real-world needs, and measure whether AI is driving meaningful results.

Practical applications of Workforce for AI-optimized workflows

Workforce can transform how your organization approaches AI integration, allowing you to:

Streamline onboarding for new AI tools: Understand where new users struggle and refine training to ensure rapid proficiency.
Identify "AI champions" and best practices: Spot the power users and learn from their successful engagement patterns to share across teams.
Reduce shadow IT and redundant AI solutions: Gain clarity on all tools in use, preventing costly duplication and unmanaged software.
Measure the ROI of AI investments through tangible productivity gains: Move beyond anecdotal evidence to concrete data on how AI improves efficiency.
Refine AI-powered workflows: Use insights to refine workflows, ensuring AI tools always align with employee needs and business goals.

Boost productivity with behavioral data

Workforce doesn’t just help you understand how employees engage with AI tools—it captures actionable data across every app your teams use daily. This rich behavioral insight provides a holistic view of the digital employee experience, allowing organizations to go a step further: fueling AI agents with real-world usage patterns. For example, brands can use the data provided by Workforce to identify repetitive, low-value tasks that consume significant employee effort. With this understanding, they can then strategically deploy AI agents to perform those tasks, allowing employees to shift their focus to higher-value, strategic projects. This not only boosts employee productivity but can boost the overall strategic output of the entire organization.

The future of work is AI-powered and Workforce-optimized

The promise of AI in the workplace is huge. But to see real results, companies need more than tools—they need insight into how those tools are used day to day. Workforce empowers organizations to maximize their AI investments by providing actionable insights into real-world employee engagement.

To learn more about how Workforce can help your company understand employee tool usage and optimize workflows, check out this video: Fullstory Workforce: Give your team the freedom to focus.

Digital employee experience (DEX): Happier teams = happier customers

Kailey Boucher — Wed, 17 Sep 2025 12:00:00 GMT

Technology plays an important role in almost every job, whether it's a customer-facing or an office-based position. When you use digital tools appropriately in the workplace, they can greatly improve productivity. However, confusing user interfaces and complex workflows can lead to errors, frustration, and reduced morale.

If you’ve ever found yourself fighting with older tools like Lotus Notes or Remedy, you’ll understand how much of a difference a user-friendly UI makes. That's why businesses are starting to focus on their digital employee experience (DEX).

DEX refers to the interactions employees have with workplace technology. It accounts for the performance of employees' devices, applications, and networks, along with how intuitive, accessible, and effective the user feels those tools are. Good DEX helps employees stay informed and productive, which makes them feel supported.

If the employee tech stack is an afterthought, and team members find themselves wrestling with legacy IT or new systems that are poorly integrated and not well-documented, it could create a poor digital employee experience.

Not only does poor DEX cause more work for your IT department as it supports confused and frustrated users, but it also leads to reduced employee morale, lower retention rates, and poor customer experience, as your team's productivity and effectiveness decline.

What is digital employee experience?

Digital employee experience refers to how your employees interact with the digital tools, technologies, and platforms provided by your organization. This includes the tools they use in their day-to-day workflows.

Every digital technology an employee interacts with is a part of their DEX, including:

Employee training platforms
HR tools for onboarding/leave requests
Internal communication tools
Company-managed mobiles and other devices
VPNs and credential management tools

The digital employee experience goes beyond the user experience of individual applications or tools. It considers how an employee interacts with all the tools used by an organization in a given day.

As Software as a Service (SaaS) platforms become common, filling roles ranging from office suites to CRM and ERP tools, digital workflows are becoming increasingly complex.

If you have employees who work remotely, that adds to the challenge since they rely on IT even more, and need to pay more attention to security. Add in the expectation that employees will use artificial intelligence (AI) and automation for routine tasks, and DEX gets even more challenging.

Employees may find themselves working on the go with a laptop or mobile device, and they often have to juggle a VPN and multiple login credentials. Investing in training, documentation, and systems to simplify the management of these tools is crucial for creating a good digital employee experience.

Measuring the digital employee experience

Taking the time to rethink the digital employee experience is a good investment in your organization's future. However, quantifying the digital employee experience can be challenging because it's so subjective. Many companies are aware of the challenges a complex digital environment can pose, but struggle to evaluate their efforts to improve it.

Evaluate your DEX by considering metrics from productivity analytics, such as:

Time to onboard new hires
Support ticket resolution times
Employee retention/turnover
Tool adoption rates
Workflow friction

One useful way of finding areas for improvement is to track tool adoption rates in your workplace. This can be helpful for more than evaluating the digital employee experience, especially in medium-sized or large organizations, since it can highlight things like:

Shadow IT practices
Unnecessary task switching
Friction points in your main workflows
Security risks

There are four key DEX challenges to address, and having an awareness of the metrics and issues listed above can help you understand these challenges and find ways to address them in your organisation.

Challenge one: Burnout and the "happiness" factor

Confusing, slow, or error-prone workflows and repetitive tasks can lead to employee burnout. In 2024, poor employee engagement cost organizations $438 billion in lost productivity. How much revenue are you leaving on the table due to a poor employee experience?

Streamlining digital workflows so employees spend less time fighting with digital tools and more time delivering a positive customer experience improves morale and customer satisfaction.

A good example of this is emerging AI technologies. Rather than simply providing AI tools and mandating that employees use them, track how people engage with AI and use that information to refine your training.

Another example of this is confusing SaaS tools. If you use Salesforce in your organization, you’ve probably noticed that it’s powerful, but confusing, especially for new team members. Having visibility into how people use tools like Salesforce, a core function of employee productivity analytics, can help you improve your training and streamline workflows in the platform.

Discover how Fullstory helped a Fortune 10 Insurance company improve its Salesforce workflows.

Challenge two: Tool juggling and workflow friction

SaaS sprawl means many companies have complex workflows that combine legacy tools, desktop applications, and SaaS platforms from multiple developers. If these tools don't seamlessly integrate, it can require multiple logins and time-consuming duplication of data entry.

According to a report from Ivanti, 57% of office workers say they feel stressed by the number of tools they use each day. Inefficient workflows can cause unnecessary errors, reduced productivity, and an increased risk of employee burnout.

Streamlining your technology stack can have numerous potential benefits:

Increased productivity
Increased customer satisfaction
Reduced error rates due to less duplication of work
Improved employee morale
Lower costs, as you're paying to manage fewer applications

Application rationalization helps remove bloat from your tech stack. In many organizations, unused or duplicate tools account for as much as one-third of the software spend. Removing those redundancies saves the company money while freeing employees to focus on delivering a better customer experience.

Challenge three: Onboarding in a fragmented environment

Have you ever recruited someone and felt frustrated at how long it takes to find the right person and then bring them up-to-speed? Around 66% of job hunters say that a streamlined and efficient recruiting process makes them more likely to accept a job offer.

In a complex or fragmented environment, ramp-up times can be much longer. This is particularly true when hiring remote workers since they'll need to complete their onboarding digitally.

A good DEX helps greatly with onboarding and producing confident, productive employees:

Employees are less likely to feel overwhelmed during their first few days
New starters can hit the ground running, being productive quickly
Remote workers can find everything they need with minimum handholding
Employee retention improves when people feel confident in their work

By tracking tool usage and collecting feedback from employees, you can identify areas where new hires struggle and use this information to adjust your onboarding process, which accelerates proficiency, helps reduce staff turnover, and provides customers with a better and more consistent experience.

Challenge four: SaaS waste and hidden costs

Overlapping apps cause duplicated effort. Forgotten renewals of unused tools can drain your software budget, and shadow IT can inflate costs, create security or business continuity issues, and lead to undocumented, further fragmented workflows.

Complex workflows confuse and frustrate employees, leaving IT teams with limited visibility and an inability to provide optimal support. The customer experience also suffers, as disconnected systems can lead to delays, lost requests, errors during hand-offs, and inconsistent customer service.

Managing SaaS spend offers more than cost savings:

Streamlined workflows save time
Improved customer service due to lower error rates
Better customer response times
Empower employees by giving them everything the need in one tool

One major food and beverage company found approximately $2 million in annual savings when it investigated its SaaS budget. Rationalizing their stack also helped reduce tool switching, resulting in better customer response times.

Why better DEX improves customer experience

The digital employee experience is one aspect of the overall employee experience, but it impacts all areas of their day-to-day work experience. When your employees aren't struggling with the stack they're working with, they'll be more confident, productive, engaged, and happier.

This has a ripple effect, as confident, engaged employees are better equipped and more likely to provide a high level of service. When an employee feels supported by their digital environment, they'll:

Provide more empathetic customer service
Be more productive
Be better equipped to solve complex problems
Make fewer mistakes
Be less likely to burn out

When you enhance the digital employee experience, you create an environment where employees can offer a smoother customer experience with fewer errors and better service. This builds trust in your brand and fosters long-term customer relationships.

Useful tools for improving your DEX

There are many useful tools to help companies of all sizes improve their DEX. Some tools to consider deploying within your organization include:

Fullstory Workforce focuses on the actual in-app behavior to identify and resolve workflow friction and improve software adoption.
1E: This endpoint management solution lets you monitor, troubleshoot and manage endpoints without having to resort to privacy-invading techniques such as screen sharing.
Nexthink: Monitor device health, application performance and network connectivity. Get real-time analytics and insights into the digital experience of your employees, whether they’re working from home or in the office. Nexthink even lets you collect employee feedback directly.
ControlUp: Using ControlUp, your IT support teams can find and fix problems with remote devices, and manage relatively difficult-to-troubleshoot issues such as high CPU usage or poor network performance.
Lakeside: See information about the health and performance of your employee’s devices. This AI-driven solution helps your IT team be more proactive by flagging up issues before your employees do, so they can push patches or fix them remotely.

Boost your digital employee experience

When you first start examining the digital employee experience in your organization, streamlining it can seem like an insurmountable task. However, every DEX challenge presents an opportunity to improve your business at several levels.

At Fullstory, we offer a variety of tools to analyse and improve the digital employee experience. Workforce gives you detailed insights into how your employees are using (or not using) the tools you’ve given them. Armed with this information, you can streamline your workflows, reduce unused licenses, save money and improve employee morale. To learn more about how we can help you boost your DEX, watch a product tour today.

Powerful AI that’s built in, not bolted on

Dani Schenone — Tue, 09 Dec 2025 05:00:00 GMT

The flood of digital behavioral data is bigger—and more overwhelming—than ever.

Businesses generate 70% more data year over year, yet 74% of organizations struggle to turn it into action. Instead of fueling smarter decisions, data often creates more noise. Critical insights get buried. Opportunities slip away before teams can act.

AI promises to help, but not all AI is created equal.

In the rush to slap AI-powered onto everything, many analytics platforms have taken shortcuts — dropping in an AI assistant and calling it innovation. But a simple chatbot isn’t enough. Real impact happens when AI isn’t just bolted on, t’s built in.

That’s why we created StoryAI. While others build one-off AI tools, we’ve built an AI-first experience — one that turns raw data into real intelligence, embedded directly into workflows where teams need it most.

Rather than a single AI feature, StoryAI is a system of AI agents that work together across Fullstory — surfacing insights, predicting behavior, and identifying opportunities before you even know to look. Whether it’s Summaries condensing mountains of session data, Ask StoryAI delivering context-rich answers, or Opportunities doing the investigative work for you, StoryAI transforms decision-making from reactive to proactive.

When your AI agents can surface insights, predict behavior, and trigger actions from the same behavioral data, you're working inside an agentic AI workspace, not just using point tools

The problem: Too much data, not enough time

Analytics was supposed to make decision-making easier. Instead, it has become a bottleneck.

Product, engineering, data, and IT teams spend more time chasing answers than acting on them. And in a world where every second of friction costs conversions, waiting isn’t an option.

With StoryAI, it doesn’t have to be.

The shift: From information to action

Companies don’t need more reports. They need clarity. They need the right insights at the right time.

AI isn’t just about automation. At Fullstory, it’s about acceleration. Our customers don’t need AI to handle tedious tasks—they need it to speed up their results.

We’re in the business of removing complexity, not adding it. Fullstory already translates raw data into clear, actionable steps. Now, we’re taking it even further.

Imagine spotting why users drop off before it impacts revenue. Imagine answering complex business questions in seconds, not days. Imagine having the foresight to prevent churn, optimize conversions, and grow faster without sifting through endless dashboards and replays.

This isn’t the future of analytics. It’s happening right now.

The result: AI-powered insights that drive real impact

Data should work for you—not the other way around. StoryAI doesn’t just simplify analytics; it unlocks what’s possible with the data you already have.

StoryAI is a smarter, faster way to understand what’s happening in your customers’ digital experience, why it matters, and how to take action.

StoryAI bridges the gap between information and impact—helping teams move faster, make smarter decisions, and create seamless digital experiences.

Here’s how:

Summaries: Cut through the noise instantly

For most teams, reviewing session data is a time-consuming, manual process. StoryAI Summaries takes multiple user sessions and condenses them into key takeaways, helping teams understand behavioral patterns at a glance, without sifting through endless replays. And with an API, you can use a custom prompt to send pertinent events within a session to any place you need (BigQuery, support bots, call centers, etc.).

StoryAI’s multi-session Summaries have transformed how we work. No more hours of replays—Fullstory's StoryAI instantly pinpoints where our users struggle, saving us time, improving our decisions, and helping us fix friction to give our customers the best experience possible.
John Serrano, Manager IT Digital Operations at JetBlue

The bottom line:

Faster insights
More focus
Better decisions

Opportunities: Spot hidden friction before it costs you

Many teams don’t discover problems until revenue takes a hit. StoryAI Opportunities proactively surfaces areas for improvement backed by industry expertise on what matters—whether it’s an unexpected dropoff, a usability issue, or a growth opportunity.

The bottom line:

Fix pain points you didn’t know about
Identify conversion blockers and act
Slash churn

Ask StoryAI: Answers that find you first

Teams lose hours chasing the “why” behind user behavior. Ask StoryAI changes that.

Instead of waiting for you to dig through metrics and replays, Ask StoryAI sees the context of what you’re currently viewing in Fullstory, understands your goals, and surfaces the most relevant insight right when you need it. It acts like a proactive analyst built into your workflow, anticipating follow up questions and helping you uncover the reason behind user behavior without starting from scratch.

Teams can ask questions like:

Why did conversion drop this week?
What frustrations are slowing users down during onboarding?
Where are repeat customers struggling most on web versus app?

The bottom line:

Clear answers, ready when you are
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Your behavioral data, working for you—starting now

The future isn’t about collecting more behavioral data — it’s about putting that data to work.

With StoryAI, you’re not just keeping up with digital complexity; you’re turning it into your advantage. No more wasted hours. No more missed opportunities. No more unanswered questions standing in the way of impact.

Just clear, actionable insights, delivered exactly when and where you need them.

Request a demo and start transforming your behavioral data into real business results.

How to build lasting value in a world of ephemeral AI agents

Lee Dallas — Tue, 31 Mar 2026 06:00:00 GMT

I recently spoke with a leader in a very large services organization about their experience adopting AI. They told me they struggle most when, even before their latest AI project is operational, a new technology arrives and the team is pushed to pivot and try to take advantage of it. This kind of self-disruption is an all-too-common occurrence nowadays. It’s like building a house where the foundation keeps changing shape before you can get the roof on.

The battle to finish what you start is being lost to the fear of missing out.

All your estimates are wrong

In my last post, I suggested that quality data is the foundation for successful agentic initiatives. With this principle in mind, how do you address the ephemeral nature of agents? They come and go so quickly. Investments in development and process change hardly have time to take root before they are eclipsed. Tried-and-true project estimation and budgeting methodologies fail.

Years of education and experience running IT projects did not prepare you for this pace.

There is a line that I call the “ROI horizon.” It’s the point in the timeline of a technology lifecycle where it is possible to measure the outcome of the effort rather than just the cost. If your effort does not survive long enough, that is a line you may never reach.

Understand the AI agent lifecycle

Hyperscalers and model creators have given us powerful tools that allow us to iterate at warp speed. The pseudo-technical are now empowered to create highly capable components that contribute to our work and make us more efficient. I am constantly called aside by people to see the new Gem (custom AI agent) they created. It truly is amazing. Except there is a challenge.

That thing we once called "knowledge work” is not stagnant. It evolves constantly with economic conditions, org structures, new technology, and sometimes, yes, even the weather.

Our systems have been codifying business rules into applications that govern processes—that is, the work itself—since the dawn of IT. Applications assume the responsibility of recording what happens and moving things from one step to the next. But there has always been a flexible and reactive actor in the workflow: the human. Early on, if you had an accounts payable system and an order processing system, the very first integrations were in the form of keystrokes—until it became financially beneficial and technically feasible to make the machine do it alone.

This was a slow and expensive evolution.

Those humans had careers that spanned decades. Agents allow us to take over tasks that would take humans exponentially longer to finish—tasks requiring skills that would take them years to learn. But unlike humans, most of the agents that we create today are capable but not truly adaptive. We use them and are amazed, but in just a few weeks, something changes. Adaptation is necessary, and more often than not, we replace the agent we created six months ago with a completely new one.

Do you even bother to measure the return on investment for something that you have already replaced? How sure are you that it was worth it?

The AI agent lifecycle forces leaders to rethink all aspects of business optimization. Integrating two systems can now happen in hours instead of months. But you are also likely to replace that solution much more quickly. This not only disrupts how the work gets done, but perhaps more importantly, it overturns the calcified enterprise budgeting cycle.

The data layer is the long game

From an investment perspective, it makes sense to first ensure that the data foundation is solid. Next, you should focus on that data architecture’s support for the rapid evolution of the agents that feed on it. This is where Model Context Protocol (MCP) has come into play. MCP becomes the flexible gateway that the constantly mutating soup of agents uses to access the data that they need. The agents change, the access layer becomes more fluid, and the data provides stability as it is created and moved through its various states through an evolving business process.

Probabilistic interactions age agents faster

Not all agents age at the same rate, and understanding what accelerates their obsolescence can help you prioritize where to invest.

A given agent tends to live longer when it is dealing with a deterministic flow. Automating well-understood, stable fragments of mature workflows produces the types of agents that naturally become incorporated into core systems. The ability to handle probabilistic scenarios is the most interesting and transformational.

We constantly hear promises of the magical thinking agent that will replace your analytics staff. It is not likely to be effective in the near term. The rate of change in the conditions in which they exist shortens their viability. Be wary of solutions that overpromise on autonomous analytics and underdeliver on an AI-ready data architecture.

The bottom line

At Fullstory, we are investing in the concept of agentic workspaces to address a variety of key jobs to be done. It is an inevitable evolution, but rest assured, we are building that capability on the foundation of Fullcapture.

We began by launching Fullstory’s StoryAI to put a variety of AI capabilities into the hands of our customers. Our next leg in the agentic race is opening our data up so that it is consumable by agents across our customers’ ecosystems as they evolve.

Fullstory MCP and other developer-focused initiatives, like our Fullstory Skills Repository, are both enabling the evolution of agents that you and our partners will create with Fullstory data.

These efforts are the critical pillars you need for analytics that will live long enough to see their value realized. Agents—especially those created by individuals—are ephemeral. Built in hours today. Quickly replaced tomorrow.

Embrace the pace of change. Invest in a stable, data-driven foundation. Measure the value of your efforts from the outcomes. Accept that self-disruption is part of the formula now, but endeavor to push it "above the data line" to extend the lifespan of your investments past the agentic ROI horizon.

Ecommerce conversion funnel optimization: Streamline your checkout journey

Madison Eubanks — Tue, 16 Apr 2024 04:00:00 GMT

Your ecommerce conversion funnel—that is, the path that visitors take to make a purchase—is perhaps the most important workflow on your site. Baymard Institute found that the average large-sized ecommerce site can increase overall conversion rate by over 35% by making critical improvements to the checkout flow. Clearly, finding and resolving any snags that are there should be a top priority for ecommerce pros.

While a best-in-class behavioral data platform like Fullstory proactively surfaces critical insights around the purchasing process, it’s up to you to take steps to get the most from your data–which means understanding how to build useful, scalable conversion funnels inside your analytics solution.

Here are some actionable tips for creating effective, optimized conversion funnels in your analytics tool.

The advantages of using Ecommerce conversion funnels

Your job is to keep your digital Ecommerce experience smooth, easy-to-navigate, and issue-free.

Building and analyzing conversion funnels can help you identify where users are hitting the biggest hiccups that are causing them to drop out of key workflows, like a dead button inside the checkout flow.

Additionally, funnel analysis can help you understand if page performance is an issue, or if problems impact certain segments of customers more than others (like those shopping on Chrome versus Safari).

Identifying issues like these through funnel analysis enables you to understand which issues are having the greatest effect on customer experience and then prioritize where your engineering team should focus their attention.

When Eommerce conversion funnels are useful for improving your checkout experience

When should you turn to funnel analytics to uncover an opportunity in your Ecommerce digital experience? Here are a few examples:

Identifying “unknown unknowns”

Often, when looking for flaws in your Ecommerce experience, it can be difficult to know where to begin. Building a broad funnel for the most vital workflows on your site—the steps in your checkout flow, in particular—gives you a starting point from which to find more granular improvements.

Testing a hypothesis

Perhaps you’ve been poking around in a retail and ecommerce analytics platform and you’ve come up with an idea for a way to improve a page on your site. For example, you’ve hypothesized that adding a checkbox where users can say “shipping address is the same as billing address” will get 5% more shoppers to the next stage of checkout. You can test this change on a segment of users, and then compare how they flow through the funnel versus those who don’t see the checkbox.

Measuring negative impact

Conversion funnels are most useful for helping you understand which elements of your site are having the most negative impact on the customer experience, rather than elements that might be having a positive effect. For example, Fullstory customer Moosejaw conducted an audit of their checkout funnel and found that a seemingly minor error—how address verification messages were displayed—was having an outsized negative impact on conversions.

Best practices for building effective conversion funnels

Your conversion funnels are a powerful tool for improving your ecommerce conversion rate optimization. There are a few simple steps you can take to ensure they're highly effective. Here are five best practices for getting the most out of your conversion funnels:

Conversion funnels need at least two events, but including four to six events in your funnel will ensure that it provides a clear, thorough look at how users move through the series of steps.
Make sure the first step in your funnel isn’t too broad. Users can fall out of a funnel at any time–even after the first step–so you want it to be narrow enough to find meaningful user data. For example, “Visited my homepage URL,” might be too broad to be a useful first step in your funnel, but “Visited the URL containing /summer-styles” is a happy medium.
If you notice an exceptionally large drop-off between two steps in your funnel (like between “Clicked ‘Add to cart’” and “Viewed cart”), consider breaking your funnel into two separate funnels to get a more granular view of what might be causing a dramatic drop-off rate.
Choose a date range for your funnel that’s appropriate to what you want to measure. For example, if you want to see the impact resulting from a change you implemented recently, set the date range to “This past week.”
Try narrowing your funnel analysis to a single attribute to understand how different cohorts of users might be experiencing your site. For example, look at mobile shoppers versus desktop shoppers, or those browsing on Chrome versus those browsing on Safari, to see if there are any notable trends.

For more tips on building effective funnels, check out this blog post on using funnels to understand user behavior. →

How Fullstory takes conversion funnels to the next level with Conversions and Revenue Opportunities

Many analytics solutions offer the ability to build and analyze conversion funnels, but Fullstory enables Ecommerce pros to go beyond traditional funnel analysis with two powerful funnel enhancements: Conversions and Revenue Opportunities.

Built by our team of data science experts, Fullstory Conversions uses machine learning to analyze different cohorts of users as they pass through your key funnels. By showing exactly when, where, and why users are dropping out of those funnels, Conversions enables digital Ecommerce teams to prioritize issues based on business impact.

Plus, Revenue Opportunities allows users to define and select Revenue Events that represent specific business transactions, and correlate the projected annualized revenue lost to specific issues in your funnel.

Knowing how much revenue is associated with a particular issue enables Ecommerce teams to intelligently and confidently prioritize issues in order of business impact.

To learn more about making high-impact user experience improvements with Fullstory, talk to one of our experts.

User retention rate: How to calculate it & why it's critical

Fullstory — Wed, 22 Feb 2023 07:00:00 GMT

If you’re working on an app or a website, understanding your user retention rate is essential. Without this information, it’s impossible to tell whether you’re on course for continued growth or whether you’re starting to lose users.

But how do you calculate retention rates, and why are they so important to your team's success? Here’s everything you need to know.

Key takeaways:

User retention rate measures the number of users who are continuing to use your app, website, or service after a given time period.
Customer retention rate (CRR) measures the percentage of paying customers a business maintains over a specific period, reflecting its ability to encourage repeat business.
Brands that want to increase their retention rate should identify at-risk customers and reach out to them before they turn elsewhere. They can use customer feedback to improve the customer experience and retain more users.

What is user retention rate?

User retention is when users or customers continue to use a product or feature. Therefore, user retention rate is the rate at which a platform retains its user.

In other words, user retention retention rate measures the number of users or customers a brand retains over a given time frame. This is written as a percentage of total customers who remained with the brand throughout the time period.

For example, if your app starts the month with 100 users and ends the month with 80, your app’s retention rate would be 80%.

In practice, it’s likely you’ll attract new users even as current active users drop off, which can cause your customer base to grow even with a low retention rate.

Let’s say your app started the month with 100 users. 20 users delete the app during the month, but 40 new customers sign up, leaving you with 120 users.

If you only used the raw number of customers, you’d have an app retention rate of 120% – a literal impossibility. You need to subtract the number of new customers from the end-of-month numbers to get an accurate calculation.

User retention rates are essential to projecting future growth. If your retention rate is higher than your rate of user acquisition, your customer base is growing. If it’s lower than your acquisition rate, your customer base is shrinking.

Finally—though user retention rate and customer loyalty are related, they are not the same thing.

Businesses use their retention rates to determine three things

What value are they providing to their existing customer base?
Is their customer base and rate of growth sustainable?
How loyal are their customers?

Some companies take an alternate approach. Instead of tracking their retention rate, they track their rate of customer churn/attrition.

How do you calculate user retention rate?

The formula for retention rate calculations is straightforward. First, you need to determine a given period of time to calculate over. Most companies measure customer retention on a monthly or annual basis. After that, you do the math:

User Retention Rate = [(CE - CN) / S] x 100

In this formula, CE is the number of users at the end of the time period, and CN is the number of users who signed up for subscriptions during the time period. S represents the number of users at the beginning of the time period.

Let’s do the math for our example above. Our hypothetical app had 100 users (S) at the beginning of the month, with 40 new users (N) and a total of 120 remaining (E) at the end of the month.

So the equation would be: [(120 - 40)/ 100 ] x 100 = 80%

Incidentally, our rate of new customer gain (40%) was less than our retention rate, which means that our growth is sustainable.

Why customer retention rate is so important to track

Customer retention rate is critical for businesses to understand their performance. Think of it like your users voting with their wallets. The higher your retention rate, the happier users are. On the other hand, a low customer retention rate tells you you’re off track.

The reason this is so powerful is that it gives you insight into customer sentiment that you can’t get by tracking other behaviors. People may lie on a customer service survey or ignore it altogether.

Customer retention rate can also be used to track customer reaction to changes in the service. For example, you could divide your customers into two cohorts. One cohort gets a major update to the app, while the other keeps using the old version.

By comparing the retention rates of the two cohorts, you could find out whether or not people liked the update. If they did, you can roll it out to all users. If they didn’t, you know it needs some more tinkering before it goes out. This kind of cohort analysis is key to many development strategies.

Learn more: Tracking user retention with Fullstory

What is a good user retention rate?

There’s no single percentage that qualifies as a “good” retention rate. Some types of mobile apps have low retention rates, and others have high ones. Look at retention rates for similar apps or companies and then use those digital product benchmarks to evaluate your own performance.

A lot depends on the nature of the app or service. A smartphone app takes some time and commitment to install. An ecommerce site, on the other hand, may see a lot of one-off orders.

Similarly, a new app or service should expect to see a lower retention rate. Many people will just be testing the waters at this phase, and won’t be as prone to sticking around. The important thing is to make adjustments and improve the rate over time.

Finally, it depends on what you’re trying to do with an app or service. If it’s a trial, you may not care about retention rate. If it’s something you’re trying to scale, retention rate will be very important indeed.

Retention rate vs. churn rate

Churn rate is the opposite of retention rate. Instead of calculating the percentage of customers who remain over time, you calculate the percentage who leave.

Compared to user retention rates, churn rates are easy to calculate. You just use the following formula:

Customer Churn Rate = (C / S) x 100

In this formula, C is the number of churned users and S is the number of users at the start of the month.

Let’s plug in the numbers we used in our earlier example:

(20 / 100) x 100 = 20%. Our brand has a 20% churn rate.

Like most inverses, churn rate doesn’t tell you anything new. It’s just another way of expressing the same information: 80% of users have stayed, and 20% have left.

Other key customer retention metrics

Besides retention rate and churn rate, there are many other important metrics that you should be aware of. Let’s go over the most significant.

Revenue churn

Revenue churn is a measure of how much recurring revenue you’ve lost across a particular time period. This is similar to customer churn in that it represents a loss, but it’s different because it doesn’t count individual users—it counts dollars.

Revenue churn isn’t always the strongest performance signal. For example, retail businesses expect to earn less money in January and February than during the holidays, so revenue churn isn’t necessarily a sign of poor performance.

But in other cases, it can be incredibly powerful. SaaS companies with tiered pricing models rely on revenue churn to account for downgrades. If a bunch of customers move from the “Platinum” tier to the “Silver” tier, there is no loss of customers. But the loss of revenue could be very significant.

Along the same line, low revenue churn could put high customer churn into context. If you’re keeping your most valuable customers and losing the ones who spend less, your business model may still be viable.

Net Promoter Score

Net promoter score (NPS) is a metric for tracking customer sentiment. A brand asks customers to answer a single question. On a scale from 1 to 10, how likely are they to recommend a product, service, or company?

Respondents are then sorted into three categories. “Promoters” are the most enthusiastic, and give ratings of 9 or 10. People who provided ratings of 7 or 8 are considered “passive,” while anyone who answers 6 or less counts as a “detractor.”

All of the results are aggregated, and the average number is the NPS score. This gives you a quick snapshot of how your customers feel. You can also ask a similar question for a particular aspect of your business, such as the onboarding experience.

Keep in mind that NPS scores may skew negative. Angry customers are more likely to answer a survey than satisfied ones.

Repeat purchase rate

Repeat purchase rate (RPR) is the percentage of customers who make a second purchase from your company. The formula is very simple:

Repeat Purchase Rate = (R / C) x 100

R represents the number of return customers, while C represents the number of total customers.

If you want to go even further, you can calculate the customer loyalty rate. This is the percentage of people who made even more purchases – exactly how many is up to you.

Finding your most loyal customers can help you pinpoint marketing to those people. You can also look at what features those customers are using, and see if you can drive other users to those features, as it might indicate low user friction.

Customer lifetime value

Customer lifetime value (CLV) is exactly what it sounds like. It’s a measure of how much profit your average customers contribute to your business over their lifetimes.

This is a tough number to measure, and you can only ever get an estimate. Without a crystal ball, there’s no way to know for sure how much someone will spend with your brand in the future.

There are also several calculations. One of the most popular is:

Customer Lifetime Value = (O x R) - C

In this equation, O represents the average order value, and R is the repeat purchase rate. C is the customer acquisition cost. That’s how much you spend in marketing and other expenses to gain the average customer.

Knowing your CLV can be useful when trying to decide how much to spend on customer retention. If your CLV is $100, it doesn’t make sense to offer a one-time signup bonus of $150.

How to improve user retention rate

Implementing best practices to increase user retention rate if often business critical. When analyzing ways to make improvements, always be sure to analyze both qualitative and quantitative data to ideate solutions. A few top tips include:

Make your onboarding UI as simple as possible
Build a straightforward, intuitive user interface
Use cohort analysis to test major updates
Send prompts to inactive users to get them to return
Survey churned users to find out why they left
Make continuous improvements and bug fixes
Use hooks and gamification to keep users engaged
Implement product tours to better educate users

Redefining trust in financial services: From reactive defense to proactive intelligence

The Fullstory Team — Mon, 30 Mar 2026 06:00:00 GMT

In financial services, trust is the ultimate currency. But traditional methods of building this trust are struggling to keep up in the digital-first era, where people expect fast, seamless, and secure interactions.

To avoid losing customers, financial organizations must move from a defensive posture to a proactive one. They must shift from asking "what happened?" to knowing why it's happening and stopping it before it impacts the customer or the bottom line.

Achieving this requires real-time behavioral insights.

From point-in-time checks to continuous behavioral monitoring

Historically, financial institutions have relied on point-in-time checks like credit pulls or login verifications which provide only a keyhole view of a customer. This approach overlooks the broader context of user behavior. Without a comprehensive understanding of how customers interact with their services, organizations run the risk of missing critical signals that indicate potential issues or changing needs.

The strategic shift: Leading firms are moving to continuous behavioral monitoring, analyzing a user's "digital body language" throughout their entire session. This requires a platform that captures every click, scroll, and hesitation to establish a baseline of normal activity.

How Fullstory’s StoryAI enables this: StoryAI provides the engine for this shift. It automatically analyzes real-time user interactions, flagging deviations from established patterns. For example, if a user's navigation or typing velocity suddenly changes mid-session, StoryAI can surface this anomaly as a potential indicator of account takeover, allowing security teams to intervene before a fraudulent transaction occurs without adding friction for legitimate customer.

Combating synthetic identity fraud with behavioral intelligence

Synthetic identity fraud, where criminals create new identities from mixed data, is the fastest-growing financial crime in the U.S., costing lenders billions annually. These sophisticated threats easily bypass traditional, rule-based fraud systems.

The strategic shift: The defense can no longer be based on static data. Institutions must leverage AI to detect the subtle behavioral anomalies that signal a fraudulent actor, even when their credentials appear legitimate.

How StoryAI enables this: While rule-based systems look for known bad patterns, StoryAI identifies unusual behavioral signals. It can detect when a session exhibits frustration signals like rage clicks during an application, or navigational patterns inconsistent with a real user. By automatically identifying and surfacing these high-risk sessions, StoryAI empowers fraud teams to uncover threats that legacy systems miss entirely.

The regulatory shift: proving fairness with explainable AI

As regulators move from gatekeepers to guides, they increasingly require firms to demonstrate fairness and transparency. It's no longer enough for an algorithm to make a decision; you must be able to explain why.

The strategic shift: Institutions must build their digital experiences on a foundation of complete, replayable data. This provides an objective, empirical record of the user journey, which is critical for justifying automated decisions to both customers and regulators.

How StoryAI enables this: StoryAI provides the "why" behind the user experience. If a customer abandons a digital onboarding process, for example, it’s not enough to know they dropped off. StoryAI can pinpoint the exact cause—like a confusing UI element or a broken form—with a pixel-perfect session replay. This irrefutable evidence allows firms to prove that friction, not bias, was the cause of user error, providing a crucial layer of explainability and protecting against regulatory risk.

Fullstory is building the future of digital trust

By embedding proactive, AI-driven behavioral intelligence into their operations, institutions can move beyond a reactive security posture. With a platform like StoryAI, which automatically identifies, quantifies, and prioritizes experience issues, you can eliminate friction, protect against sophisticated fraud, and build the secure, transparent digital experiences that customers now demand.

To see how StoryAI can help your financial services organization build trust, get a demo here.

Qualitative vs. quantitative data in research: what's the difference?

The Fullstory Team — Fri, 04 Oct 2024 04:00:00 GMT

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and its data can be divided into two categories: quantitative and qualitative. But what's the difference between each? When should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you better understand your data and, ultimately, your customers. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.
Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data:

How many people attended last week's webinar?
How much revenue did our company make last year?
How often does a customer rage click on this app?
Which area of a webpage receives the most engagement, as shown by heatmaps?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis—collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are in-app surveys, experiments, polls, and more.

Tools like user session replay software provide granular visibility into user interactions, adding depth to quantitative data by showing exactly how users engage with site elements.

Newer platforms like Quadratic are also making it easier to perform quantitative data analysis by combining the familiarity of a spreadsheet with the power of Python, SQL, and AI — so teams can run analysis workflows without switching between tools.

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?
How do customers feel about their customer service experience?
What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.
Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors.
Quantitative data is fixed and universal. Qualitative data is subjective and unique.
Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.
Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns.

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data:

Quantitative data example

The customer has clicked on the button 13 times.
The engineer has resolved 34 support tickets today.
The team has completed 7 upgrades this month.
14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.
My coworker is funny, loud, and a good listener.
The customer has a very friendly face and a contagious laugh.
The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively.

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers.

Addressing bias in data collection and analysis

While both quantitative and qualitative data provide valuable insights, they’re each susceptible to bias.

Where your organization sits on the data maturity curve largely determines how tough those biases are to spot—and how equipped you are to fix them.

In quantitative research, selection bias is a common concern. This occurs when the data collected isn't truly representative of the population being studied, potentially leading to skewed results. For instance, an online-only survey might exclude demographics with limited internet access.

Qualitative research faces its own bias challenges. Researcher bias can influence data interpretation, while participant selection bias might result in missing important perspectives. Additionally, participants might alter their responses based on what they think the researcher wants to hear.

To address these issues:

Use careful sampling methods in quantitative studies
Employ strategies like triangulation and reflexivity in qualitative research
Consider mixed methods approaches when possible
Be transparent about potential limitations in your research

By acknowledging and actively working to minimize bias in both quantitative and qualitative research, researchers can enhance the validity and reliability of their findings, leading to more accurate and useful insights.

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons.

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from.
When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use.
As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective).
With choppy information, it can be inconclusive.
Quantitative research can be limited, which can lead to overlooking broader themes and relationships.
By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.
It’s great for exploratory purposes.
Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).
It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other.

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

For example, A/B testing can complement both approaches by allowing teams to test quantitative outcomes of different variants while gathering qualitative insights from user feedback on why certain variations perform better.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

Behavioral data refers to everything in terms of numbers (or discrete data)—like the number of users clicking a button, bounce rates, time on site, and more.

Some examples of quantitative research:

What is the amount of money invested into this service?
What is the average number of times a button was dead clicked?
How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view.

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made.

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions, find averages, test causes and effects, and generalize results to larger measurable data pools.

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

Surveys

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Polls

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group. The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive.

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too.

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research:

The sunflowers had a fresh smell that filled the office.
All the bagels with bites taken out of them had cream cheese.
The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

Interviews

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design.

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

So which type of data is better for data analysis?

So how do you determine which type is better for data analysis?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable.

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results.

At Fullstory, we understand the importance of data, which is why we created a behavioral data platform that analyzes customer data for better insights. Our platform delivers a complete, retroactive view of how people interact with your site or app—and analyzes every point of user interaction so you can scale.

The role of AI in driving product adoption in SaaS

The Fullstory Team — Fri, 20 Mar 2026 06:00:00 GMT

In the race to retain customers and grow revenue, SaaS leaders are drowning in data but starved for answers. The traditional dashboard—once the command center for business intelligence—has failed them. It presents what happened, but not why it happened or what to do next. This reactive approach is no longer viable.

Shifting from manual data analysis to AI-driven autonomous insights is a necessary move for organizations that want to keep up. This transition empowers teams to reclaim their focus for strategic work, make decisions with confidence, and build a culture of proactive optimization that directly impacts the bottom line.

Manual analysis can’t keep pace with user behavior

Today's product teams are expected to monitor hundreds of metrics across dozens of dashboards, manually correlating data points to identify patterns, and then somehow prioritize which issues deserve attention.

But by the time an analyst spots a trend, investigates its cause, and escalates it to the right team, thousands of users have already experienced the friction. This manual approach creates three critical bottlenecks:

Analysis paralysis: Teams spend more time debating what to measure than fixing problems
Delayed detection: Critical issues surface only after they've compounded into major problems
Misaligned priorities: Without clear impact quantification, teams fix the loudest complaints instead of the costliest ones

The gap between having data and knowing what to do with it has never been wider.

The cost of inaction

Hesitation to move beyond manual analysis is a significant competitive liability. While familiar, legacy processes introduce costly delays and mask critical issues that automated systems capture instantly. The cost of this inaction is tangible and directly impacts business performance. Consider the following drains on resources and revenue:

Wasted engineering cycles: Countless hours are spent trying to reproduce user-reported bugs that an AI-native platform could have identified and contextualized in seconds.
Lost revenue: Hidden friction points in critical conversion funnels go undetected, causing user drop-off that silently erodes revenue.
Customer churn: Poor product experiences, left to fester, lead to frustration and churn. By the time these issues appear in surveys or support tickets, the customer relationship is already at risk.

From dashboards to autonomous insights

Imagine a world where your data works for you, not the other way around. That is the reality of autonomous insights. Instead of requiring analysts to manually sift through static dashboards, platforms like Fullstory’s StoryAI leverage generative AI to analyze billions of user interactions in real-time.

StoryAI doesn't just find user friction; it surfaces the revenue-impacting issues first. By automatically detecting where users struggle, identifying points of confusion, and quantifying the impact of these issues, it delivers a prioritized queue of opportunities to the teams that can act on them. This allows product, engineering, and marketing to shift from reactive problem-solving to proactive optimization, addressing critical issues before they escalate and impact revenue.

Beyond AI-enabled features: The power of AI-native architecture

True transformation does not come from bolting on a few AI-enabled features; it comes from an AI-native architecture where intelligence is woven into the platform's core. This fundamental difference creates a seamless, intuitive experience that accelerates adoption because users find value without friction.

When AI is deeply integrated into a platform, it fundamentally improves user experience and optimizes functionality. This level of integration is what drives product adoption, as users immediately recognize the benefits of a smarter, more responsive system.

Simplifying user experience with agentic workflows

Adopting new tools should not require a steep learning curve. SaaS AI tools excel by using agentic workflows—intelligently designed assistance models that guide users, adapt to their input, and provide real-time support. When a user hits a snag, an agentic workflow can provide contextual help, removing obstacles and accelerating their time-to-value. These enhancements facilitate smoother onboarding and drive higher retention by empowering users to succeed independently.

How faster insights translate to higher adoption

AI-native platforms enable a fundamentally different approach to product development—one in which optimization happens continuously. Instead of waiting for user complaints or survey feedback to surface problems, teams can proactively refine experiences based on real behavioral signals.

A traditional approach might reveal that 30% of users abandon a feature during their first week. An AI-native approach identifies why—maybe mobile users on specific devices encounter a rendering issue, or users coming from a particular traffic source lack context about the feature's value. With this specificity, teams can address root causes rather than symptoms, directly improving adoption rates for future cohorts.

This continuous optimization cycle compounds over time. Each friction point eliminated means more users successfully reach activation. Each confusing interaction clarified means higher feature engagement. Each performance issue resolved means better retention.

Embracing the future of AI

The impact of AI on product adoption and efficiency is profound. Through the shift to autonomous insights, the power of AI-native architecture, and the clarity of agentic workflows, SaaS organizations are fundamentally reshaping their competitive edge. With tools like Fullstory’s StoryAI, the process of understanding customer experience becomes proactive and prescriptive.

Ready to stop chasing answers? Discover how StoryAI can help your organization drive product adoption.

The operational layer behind behavioral in-app guidance and contextual feedback

Lane Greer — Tue, 24 Feb 2026 06:00:00 GMT

Behavioral data has made friction visible for years. Acting on it inside the product hasn’t kept pace. Most teams still rely on engineering tickets, disconnected tools, or delayed follow up that misses the moment when help would matter most.

Guides and Surveys changes that workflow by turning insight into intervention within the same environment. Teams can target users based on real behavior, deploy in product guidance without code, and capture contextual feedback that connects directly to session data. Insight doesn’t stop at observation. It becomes something teams can act on while the experience is still unfolding.

From behavioral insight to in-product action

Most digital teams already know where users struggle. The gap has been execution. Analysis lives in one place, action happens somewhere else, and measurement is split across tools.

Guides and Surveys bring those steps together. Behavioral signals determine who needs help and when intervention should happen. Product and digital teams can move from diagnosis to response using properties and behavioral signals already available inside Fullstory. Guidance can be deployed directly in the experience through a visual builder that doesn’t require engineering involvement or release cycles.

Each interaction feeds back into Fullstory as behavioral data that flows into Fullstory Analytics for reporting and measurement. Guidance becomes measurable through adoption, completion rates, and sentiment over time. The workflow stays intact from insight to outcome, which allows teams to respond quickly without creating another layer of process.

Precision targeting powered by behavioral data

Most in-product guidance fails because targeting is disconnected from behavior. Static rules and broad audiences treat users as if they share the same intent, even when their journeys look completely different.

Guides and Surveys approaches targeting through behavioral context. Teams can define audiences using properties they already maintain, such as account tier, lifecycle stage, or product usage patterns. These properties combine with observed behavior to create precise conditions that reflect what a user’s actually doing.

Personalization follows the same logic. Dynamic fields reference user attributes directly inside guides, which keeps the experience relevant without adding complexity. Guidance becomes a response to behavior rather than an interruption layered on top of it.

This shift moves teams away from generic tours and toward targeted assistance that appears at the moment it’s needed. Targeting rules can evolve as behavior changes, allowing teams to refine how and when guidance appears without waiting for development cycles.

Turning friction signals into in-product action

Insight alone doesn’t improve an experience. The value comes from how quickly teams can respond once friction appears. Traditionally, acting on behavioral insight meant creating a backlog item and waiting for a release window. That delay often turns small moments of confusion into larger drop offs.

Guides and Surveys shortens that gap. A product manager can identify a funnel exit, a repeated error, or a pattern of rage clicks and deploy guidance immediately through a visual builder. There’s no need to wait for a release window. Teams can explain a workflow, highlight a feature, or provide direction while users are still in the flow.

This approach doesn’t replace long term product improvements. It allows teams to intervene while those improvements are being planned. Every interaction connects back to Fullstory’s behavioral data, making it possible to measure how guidance influences adoption, conversion, and completion rates. Teams move from documenting friction to actively shaping the experience around it.

Surveys as contextual behavioral events

Most traditional surveys fail because they lack context. Feedback arrives without a clear view of what the user experienced, which makes it difficult to understand what needs to change.

Guides and Surveys connects feedback directly to behavior. Surveys can be triggered based on real user activity, such as completing a transaction, encountering an error, or exiting a flow. Because surveys inherit existing styles within the product, they feel like part of the interface rather than an external overlay. That alignment increases participation and produces feedback grounded in the moment it was captured.

Each response becomes a first class event inside Fullstory. Teams can move from a comment to the session replay that provides full context around the experience. Feedback shifts from isolated sentiment to behavioral evidence that informs product decisions.

Surveys aren’t separate from the workflow. They become another signal that helps teams understand how guidance influences user experience over time.

A different way to think about intervention

When targeting, guidance, and feedback live inside the same workflow, teams can respond while the experience is still happening.

Guides and Surveys turns intervention into an everyday product motion. Identify friction, deliver guidance, and learn from the outcome. Then do it again.

Get a personalized demo.

Maximize engagement with low-latency streaming: A game changer for digital experiences

Jaime Yap — Wed, 25 Feb 2026 07:00:00 GMT

Understanding how users interact with websites and apps is the key to improving digital experiences.

Unfortunately, collecting and acting on that data has always come with some degree of delay—a lag between when something happens on a customer’s screen and when a business can do something about it. By the time a team sees that a user struggled to complete a task, clicked in frustration, or backed out of a form, the session is over and the chance to help a frustrated user in the moment is long gone.

The best time to help a user is in the moment

Many personalization strategies are designed around the assumption that users will return. Delayed actions, like showing tailored content the next time someone logs in or sending a follow-up email, can be effective if you know who the user is and you expect them to come back.

That approach doesn’t work when the majority of your traffic is anonymous, which has become increasingly common, especially in industries like retail, where users often arrive from social media platforms. When you don’t know who the visitor is or whether you’ll ever see them again, your only chance to make a good impression is to help them in the moment.

Even when users are known, there are still time-sensitive situations that can’t wait. If someone is trying to complete a purchase, resolve a service issue, submit an important form, or understand a policy that will impact their decision, a few seconds of friction can derail the entire journey and even lose you a customer for life.

Closing the window between user friction and response

Low-latency streaming gives you access to behavioral intelligence about your users within seconds of it happening. Instead of waiting hours or more to see real user data, you’re alerted to friction signals while the session is still in progress and while there’s still time to do something about it.

Fullstory makes this possible by continuously capturing behavioral data and processing it in near real time. Behind the scenes, you configure the patterns you want to watch for—a sequence of actions, a single event, or a combination of signals—and Fullstory constantly matches those conditions against all incoming user traffic.

When a configured pattern is detected, the system can automatically dispatch an event. That event might trigger a workflow, send a signal to another platform like Adobe or Braze, or surface session context through an API. And this is all possible without any custom code.

Built for action, not just analysis

Low-latency streaming powers some of Fullstory’s most impactful capabilities, including Streams, which dispatch events to other systems in near real time, and the Session Summary API, which returns up-to-the-second behavioral context for use in downstream tools like AI assistants.

What sets Fullstory apart is its unique combination of full data capture and a configuration-driven workflow. You don’t require engineering support or manual instrumentation to start leveraging streaming. Teams simply define what they care about—such as an event, a sequence, or a friction pattern—and Fullstory automatically tracks and responds to those signals. This enables real-time analytics without the complexity traditionally associated with real-time data processing.

Use cases: How teams power better digital experiences with low-latency streaming

Improving customer support

Organizations utilize low-latency streaming to provide differentiated support experiences. For example, in regulated industries like responsible gaming, certain in-session behaviors can trigger workflows that escalate the session to the appropriate response team. Other organizations identify VIP users and route them to dedicated support teams when their sessions meet specific behavioral criteria.

Detecting and responding to risk

In addition to support, low-latency streaming plays a critical role in fraud detection workflows. When risky behavior is detected based on predefined patterns, that information is dispatched to downstream systems quickly enough to enable real-time mitigation. Timing is crucial in these scenarios, and streaming ensures the signals are surfaced before the session concludes.

Powering AI-driven engagement

Streaming is also employed to trigger AI processes. When a session matches defined conditions, an external AI system can use Fullstory’s Session Context and Summary API to pull the recent context from the session. This context is pivotal for generating timely next steps or messaging. For example, some internal sales processes leverage this feature to guide outreach based on live product engagement, enhancing the personalization of customer interactions.

Low-latency and warehouse exports: different tools, different jobs

Fullstory supports both warehouse data exports (delivered hourly) and low-latency streaming. Each serves a different purpose.

Warehouse exports are designed for bulk analytics and modeling. They include all captured session data and are well-suited to training personalization models or analyzing aggregate behavior patterns.

Low-latency streaming, on the other hand, is designed to react to individual user behaviors in near real time. It enables triggering lightweight, tailored experiences—like emails, personalized offers, or proactive support—based on specific user actions during the session.

→ Ready to harness the power of your data? Discover how Fullstory Anywhere: Warehouse can help you understand customer journeys, accelerate cross-functional analyses, and fuel AI models.

Start experiencing the benefits of low-latency streaming

Fullstory’s low-latency streaming moves behavior data from retrospective analysis into active engagement. With Streams and the Session Summary API in place, organizations no longer have to infer intent or wait for post-session insights; they can respond while the user is still on their site.

For digital teams looking to proactively support current users, personalize in-session experiences, or feed real-time AI agents with context, low-latency streaming is increasingly foundational.

→ Request a demo to see how Fullstory can help you transform digital experiences in real time.

What is product experience? Insights to analyze, improve, and measure PX

The Fullstory Team — Fri, 13 Dec 2024 13:30:00 GMT

Remember that last product you used where you thought, "Wow, they really get me"? That feeling of everything just clicking into place, the design feeling natural, the whole thing just working, that's no accident.

It's the magic of a thoughtfully built product experience (PX).

Great products do more than serve a purpose, they create experiences that feel intuitive and rewarding. When a product anticipates user needs and removes friction, it becomes something users can’t imagine living without.

Quick takeaways

Product experience covers the whole customer journey, from first touch to long-term love. User experience (UX) zeroes in on specific interactions and usability. You need both to win.
Bad PX costs you. Seriously. 86% of customers ditch a brand after just two rough patches, and 90% bail on apps because of performance glitches.
Building a killer product experience means nailing four things: smooth onboarding, listening to customer feedback, deep behavioral analytics, and always-there in-app guidance.
Fullstory's behavioral data helps you spot where users struggle, so your team can fine-tune workflows and keep customers happy.

What is product experience?

Product experience is the complete journey a customer has with a product, covering every interaction from first use to ongoing engagement. Unlike general user experience (UX), PX zeroes in on how the product feels, functions, and delivers value within the customer’s journey.

And loyalty isn’t just about features, it’s built on how a product supports and delights its users. In fact, 96% of customers stay loyal to brands that offer great service, proving that experience often outweighs functionality when it comes to long-term success.

To truly understand PX, think big: map the entire customer journey. From the first trial to renewal, every interaction shapes how customers see and feel about your product.

Product experience vs. user experience

At first glance, product and user experience (UX) may seem interchangeable, but they address different aspects of how customers interact with and perceive a product.

What they have in common

Both PX and UX aim to improve how users engage with a product. They focus on creating seamless and enjoyable experiences that meet user needs and expectations. Both are essential for building products that users love.

Key differences

Scope: PX is the bigger picture—it’s the customer’s overall journey with the product, encompassing every interaction from onboarding to support. UX, by contrast, zooms in on specific features, design elements, and usability during individual interactions.
Focus: PX measures success over time, considering how the product integrates into a customer’s life or workflow. UX focuses on the immediacy of individual touchpoints—how easy, intuitive, or satisfying a single task feels.
Inclusion: PX includes external factors like customer support, surrounding services, and long-term engagement. UX is narrower, dealing primarily with usability, accessibility, and feature design within the product itself.

Both experiences are critical to a product’s success. UX lays the foundation for great usability, while PX drives long-term satisfaction and loyalty by addressing the holistic customer journey.

Why product experience matters across industries

You might look at "product experience" (PX) and "user experience" (UX) and think, "Aren't those the same thing?" Not quite. They're definitely related, but they tackle different parts of how customers interact with, and feel about, a product.

Technology and SaaS

The technology and SaaS industry thrives on innovation, but even minor performance issues can be costly. Studies show that 90% of users abandon an app due to poor performance, highlighting the importance of delivering seamless, reliable experiences to retain users.”

Smooth onboarding processes help users quickly grasp value.
Intuitive design reduces support tickets and increases retention.
Proactive support fosters trust and deepens user relationships.

E-commerce and retail

In e-commerce, where competition is fierce, PX is what turns casual browsers into loyal customers. A personalized shopping experience can mean the difference between a sale and an abandoned cart.

Optimized checkout flows reduce friction and improve conversion rates.
Personalization tools drive higher customer satisfaction and repeat purchases.
Frustration-free navigation keeps customers engaged and lowers bounce rates.

Consider this real example: Vivid Seats was able to identify and resolve critical issues in their ticket-buying journey that other tools just couldn’t see, which prevented significant revenue loss and improving customer satisfaction. Read the entire story.

Finance and insurance

Finance and insurance customers expect secure, user-friendly platforms to handle sensitive tasks. Product experience from a fintech analytics tool can make complex actions like managing accounts or applying for policies feel effortless.

Streamlined interfaces simplify otherwise complicated workflows.
Clear, accessible information builds customer confidence.
Robust error prevention minimizes user frustration and churn.

Healthcare

In healthcare, PX impacts not just satisfaction but outcomes. A well-designed experience empowers patients and professionals to access information and services when they need them most.

Intuitive portals enhance appointment scheduling and records access.
Accessible design improves experiences for patients with varying needs.
Frictionless navigation ensures critical data is always within reach.

Travel and hospitality

The travel and hospitality industry relies on PX to create memorable, stress-free experiences. From the moment a customer books to the time they check out, PX shapes their journey.

Frictionless booking processes increase trust and conversion rates.
Real-time updates keep travelers informed and confident.
Personalized recommendations elevate satisfaction and loyalty.

From ecommerce to healthcare, the principles of a great PX remain the same, even as the products themselves vary widely. Whether you’re optimizing a digital shopping cart, a patient portal, or a travel booking system, a thoughtful PX ensures users feel valued and supported at every step.

Let’s break down the core elements that make this possible, no matter the industry.

What makes a great product experience?

A positive product experience is essential for earning customer satisfaction and loyalty. Whether it’s a retail platform, banking app, or healthcare portal, the same foundational elements create an experience that not only meets expectations but exceeds them.

In fact, 74% of consumers are at least somewhat likely to make purchasing decisions based solely on their experience with a product. This underscores just how vital PX is for driving loyalty and boosting revenue.

Each element builds upon the other, forming a roadmap to a truly exceptional PX:

1. User onboarding: Start the journey right

Onboarding is the first impression your product makes. A streamlined and thoughtful onboarding process sets the tone for the entire customer journey, ensuring users can quickly and confidently engage with your product.

Welcome surveys: Capture user preferences to deliver tailored experiences from the start.
Guided walkthroughs: Use tooltips and step-by-step guides and in-app surveys to familiarize users with essential features.
Streamlined processes: Minimize steps to reduce frustration and boost early engagement.

Effective in-app onboarding ensures users see value quickly, reducing the risk of churn before they even get started.

2. Customer feedback: Learn from your users

Your customers hold the key to continuous improvement. Collecting customer feedback ensures your product evolves to meet their needs and stay ahead of expectations.

Surveys and reviews: Identify strengths and pain points directly from your users.
Feedback reminders: Use well-timed prompts to maximize responses and uncover actionable product experience insights.
Exit feedback: Learn why users leave to refine your product and address potential issues.

Feedback is a two-way street—it builds trust with your users while guiding your product roadmap.

3. Product analytics: Understand behavior at scale

Great PX is built on understanding what works and what doesn’t. Product analytics tools empower product teams to make data-driven decisions, ensuring products remain relevant and impactful.

Usage insights: Discover which features resonate with users—and which don’t.
Real-time metrics: Quickly identify pain points to optimize the experience.
Behavioral data: Analyze patterns to refine workflows and anticipate user needs.

Analytics bridge the gap between intuition and evidence, keeping your product aligned with user expectations.

4. Contextual in-app guides: Support users when it matters most

Even the best-designed products benefit from a guiding hand. Contextual in-app guides provide support exactly where and when users need it, turning potential confusion into confidence.

Prompts and tips: Simplify complex workflows with intuitive guidance.
Feature discoverability: Highlight hidden tools to enhance the user experience.
Ongoing support: Reduce the learning curve and foster long-term engagement.

While these core elements lay the foundation for an exceptional product experience, the next step is understanding how to bring them to life through actionable strategies that make a tangible impact.

How to improve customer product experience

Improving product experience isn’t just about fixing what’s broken—it’s about creating an environment where users feel supported, engaged, and empowered every step of the way. Here’s how you can take actionable steps to elevate PX, no matter your product or industry:

1. Add an in-app resource center

Your users should never have to guess where to find help. An in-app resource hub puts guidance front and center, offering tutorials, FAQs, and support options right when users need them.

What this looks like in action: Imagine a banking app with a “Help & Learn” tab that offers quick how-tos on setting up payments, troubleshooting login issues, or understanding interest rates.

How to start: Use behavioral data to identify areas where users often get stuck and create tailored resources that address their specific challenges.

2. Create a user-driven product community

A product is a part of a broader experience. Building a community allows users to connect, share insights, and learn from one another while reinforcing their relationship with your brand.

What this looks like in action: A fitness app could feature a community board where users share workout tips, post progress photos, or offer advice on using specific features.

How to start: Launch a discussion forum or integrate community features directly into your product to foster engagement and collaboration.

3. Use customer feedback for targeted improvements

Feedback is a goldmine of product experience insights—but only if you act on it. Regularly collecting and analyzing user feedback can pinpoint areas to improve and validate the changes you make.

What this looks like in action: A SaaS platform might use in-app surveys to ask, “What’s one feature you’d like us to improve?” Feedback like this not only shows users you care but provides actionable data for updates.

How to start: Set up surveys or feedback prompts that target specific parts of the user journey, such as post-onboarding or after feature launches.

4. Highlight new features with in-app walkthroughs

Even the best features can go unnoticed if users don’t know they exist. Contextual walkthroughs and guides introduce users to updates in a way that feels natural and useful.

What this looks like in action: A streaming platform could use tooltips to highlight a new “Watch Party” feature, showing users exactly how to use it within seconds.

How to start: Plan feature rollouts with guided tours that pop up the first time users encounter new functionality. Keep instructions brief and focused.

5. Prioritize human-centered support

When users feel stuck, they need fast, reliable help. Whether it’s a chatbot, live chat, or email support, providing assistance that’s timely and empathetic can turn negative experiences into positive ones.

What this looks like in action: A travel booking app offering 24/7 live chat for immediate rebooking assistance when users miss a flight.

How to start: Use analytics to identify common support requests and ensure your helpdesk or chatbot solutions are equipped to handle them proactively.

How to measure product experience

Now that we’ve explored strategies to improve product experience, the next step is understanding how to measure its impact. Effective measurement identifies what’s working, what needs adjustment, and where to focus next. Use these approaches to ensure your product consistently delivers value:

1. Track key performance indicators (KPIs)

KPIs provide a high-level view of your product’s performance. Focus on metrics that matter:

Net Promoter Score (NPS): Indicates user loyalty and likelihood to recommend.
Feature adoption rates: Show how well users engage with specific features.
Churn rates and error rates: Highlight areas causing dissatisfaction.
Usage metrics: Include session duration, frequency, and active users.

Use these KPIs alongside behavioral data to understand how interactions align with user needs and expectations.

2. Analyze customer journey data

Mapping the customer journey reveals where users encounter friction or disengage.

Identify common drop-off points in workflows or funnels.
Spot recurring frustration signals like rage clicks or abandonment.
Prioritize fixes for the touchpoints that have the biggest impact on user satisfaction.

Regularly revisiting journey data ensures your product evolves with user behavior.

3. Leverage customer feedback

User feedback adds depth to the data. Gather insights with:

Surveys: Collect opinions post-interaction to uncover pain points.
In-app prompts: Ask targeted questions about specific features or moments.
Exit surveys: Understand why users leave to address churn causes.

Combine feedback with analytics to validate patterns and prioritize updates.

By focusing on these areas, you’ll create a continuous loop of insight and improvement, ensuring your product stays aligned with user needs.

Are you capturing everything you need to enhance your product experience?

Enhancing PX isn’t just about meeting expectations—it’s about creating moments that delight, engage, and keep your customers coming back. From frictionless onboarding to actionable product analytics and tailored in-app guidance, every interaction counts.

But here’s the real question: Are you seeing the full picture? Without comprehensive behavioral data, critical insights could be slipping through the cracks. Fullstory’s advanced product analytics and behavioral data tools ensure you’re not just guessing, you’re making data-driven decisions to optimize every touchpoint.

Don’t let blind spots hold your product back. Take the next step toward building a truly exceptional PX. Learn more about how Fullstory can help you capture everything.

In-app onboarding: Product experience beyond the welcome mat

Fullstory — Wed, 18 Feb 2026 05:00:00 GMT

What is in-app onboarding?

At its most basic, in-app onboarding is just the stuff inside your app that teaches people how to use it. It’s not a help doc or an email drip campaign. It lives inside the product, and it’s supposed to react to what you’re doing. A standard toolkit usually looks like:

Welcome screens: A single screen (please, not a carousel) to set expectations.
Setup wizards: A few step-by-step forms to get the absolute essentials configured.
Tooltips: Little pointers anchored to buttons or a menu, hopefully appearing when you actually need them.
Empty states: The prompts that show up in a blank dashboard, nudging you toward your first action.
Checklists: A tracker for the key "first week" tasks.

But there's a problem: people tend to build these things based on assumptions rather than behavior. They guess what a good first-time experience should be, and the result can often be a generic, one-size-fits-all flow.

There's a cost to ineffective onboarding

When you treat every user the same, you’re not just creating a bland experience. You’re actively burning money.

Low activation. That generic checklist doesn't care whether you're a project manager setting up a board or a developer integrating an API. It just wants you to "Invite a teammate!" When a decent activation rate for SaaS is only around 25-30% to begin with, you can’t afford to be this impersonal.
Wasted engineering time. We've seen teams spend weeks building intricate, hard-coded onboarding flows, only to find out they don't solve the user’s actual problem. It’s a soul-crushing cycle of building, guessing, and rebuilding.
Silent churn. This one hurts the most. A user gets stuck, gets frustrated, and just… leaves. They don’t file a ticket. They don’t complain on Twitter. They just disappear, leaving you to wonder what went wrong.

Design principles for in-app onboarding

Good onboarding doesn’t feel like onboarding. It feels like the product just… makes sense.

Start with the one action that matters

Forget the tour. What’s the action that proves your product is worth sticking around for?

For a notes app, it’s creating the first note.
For a CRM, it’s adding a contact.
For a design tool, it’s exporting something.

Cut every bit of friction you can. Everything else is secondary.

“Continue with Google” beats a password form. Delaying non-essential questions keeps the momentum going.

Stop asking for things you don’t need

Every required field is a chance to lose someone. If you don’t absolutely need it right now, don’t ask for it. You can collect more data later, ideally after users see value.

The same goes for permissions. Don’t throw a cold “Allow access to contacts?” dialog in someone’s face. Tell them why first. Context removes suspicion.

Use progressive, contextual guidance

Most onboarding fails because it’s built around what teams think users need. Instead, watch what they actually do.

If people open the reports page and immediately leave, that’s a signal.
If they fail the same action three times, that’s a signal.
If 40% abandon the integrations screen, that’s your priority.

Help should appear where friction shows. And once a user understands something, stop reminding them. Nothing kills goodwill faster than a tooltip that refuses to go away.

Personalize only where it matters

You don’t need 20 onboarding branches. A few meaningful distinctions are usually enough:

Solo user vs. team admin
“Just exploring” vs. “Ready to set up”
Basic vs. advanced or enterprise use case

Let users self-identify when possible. It feels respectful instead of invasive. Complex personalization sounds impressive internally, but it’s usually a maintenance nightmare. Simple can be better.

Design for control and optionality

Forced tours are a gamble. Some people appreciate them. Others look for the exit immediately. Always give an out:

A "Skip for now" button on every screen.
A "Remind me later" option for non-critical setup tasks.
A "Help" menu where people can find the guides on their own time

Those who want help should easily find it. Those who don't won't abandon the app in frustration.

Mobile vs. web app onboarding

Web and mobile app onboarding are different beasts. Smaller screens, frequent interruptions, and thumb-scrolling change everything. Your onboarding needs to adapt.

On mobile applications

Keep it under 60 seconds.
Lead users straight to one meaningful action.
Avoid carousels; use 1–2 clear value statements max.
Teach only unfamiliar gestures.
Test across device sizes to avoid cramped or broken layouts.

On web applications

You can support deeper setup flows while staying focused.
Use progress indicators for multi-step wizards.
For collaborative tools, guide users toward inviting teammates or configuring shared spaces.
Don’t add complexity just because you have more screen space.

If mobile drives quick activation, the web should drive structured setup. Design each experience around the context users are actually in, not a single flow forced everywhere.

How can you measure if your onboarding is working?

If onboarding matters, it should show up in the numbers. Focus on metrics that tie directly to value:

Onboarding completion rate: How many people actually finish the flow?
Time to activation: How long does it take to get to that first "aha" moment?
Feature adoption: Are people using the key features in their first week?
Early retention: Are people who finish the onboarding more likely to come back than people who skip it?

Completion rate alone is vanity. Activation and retention tell you if onboarding is working.

Find the friction points

Funnel analysis will show you exactly where people are dropping off. A huge cliff at one step is a red flag.

Behavioral analytics tools can show you where people are rage-clicking or hesitating. You might find that a screen isn't confusing; it's just that a dropdown menu has 50 options and no search bar.

And compare the people who skip onboarding to those who complete it. If the skippers are sticking around longer, your onboarding might actually be the problem. That's a sign to simplify.

Use user feedback to get better

A simple "Was this helpful?" survey at the end of onboarding can yield valuable customer feedback.

Talk to the people who churned in the first week. Ask them what they were trying to do and where they got stuck.

And A/B test everything. Test your copy, the number of steps, the visuals. Treat this as an ongoing process.

Stop guessing. Start learning.

The only way out of this cycle is to shift from showing off features to offering help based on behavior. This means using real behavioral data—what people actually do, where they click, where they get angry and rage-click—to give them the right hint at the right time.

"Behavior-driven" is a nice buzzword, but it's meaningless without receipts. Here’s what that looks like in practice.

User behavior (the signal)	The meaning	The response
The "pogo stick." A user bounces between a feature page and a settings page.	They’re trying to do one thing, but the controls are split across two places.	On the second bounce, show a tip: "Looking to configure this? Settings are here." Include the link. Don’t make them hunt.
The "hesitation highlight." A user highlights a word in your UI but doesn’t click.	They don’t know what it means, or they expected it to be a link.	After a brief pause, trigger a micro-survey: "Anything unclear here? What were you looking for?"
The "early champion." A new B2B user invites three teammates in the first ten minutes.	They’re moving fast and expanding the account.	Don’t interrupt them. Tag them as a "Champion" and on the next login, offer an advanced guide for teams.

Most teams already know where users drop off.

They just don’t have a way to intervene before it costs them activation.

Fullstory Guides and Surveys connects behavioral insight directly to in-product guidance so you can respond to friction in the moment instead of diagnosing it weeks later. Learn more about Guides and Surveys →

The ultimate guide to autocapture

Fullstory — Fri, 17 May 2024 07:00:00 GMT

Digital experiences are complex—and the ways humans navigate them are even more so.

Think about how many tiny, nuanced interactions users have with your websites or app every single day—clicks, taps, swipes, zooms, refreshes, form fills, searches, and so many more. Examining each interaction individually isn't likely to reveal insights you can act on—but when you put them all together, what you get is a rich, layered view of the complete digital customer experience.

Without behavioral data, understanding what your users experiences are actually like is next to impossible. You can’t know what will delight, engage, convert, and retain them. And making experience decisions based on guesswork—rather than data—directly impacts your bottom line.

The only way to ensure you have all the behavioral data you need to understand the true sentiment behind every click is with a combination of autocaptured events and manual instrumentation.

Let’s use an analogy to explore why both, not either, is the only holistic approach to logging and analyzing behavioral data:

This is known as the Streetlight Effect, and is a surprisingly common observational bias. We seek answers from the information we can easily access, rather than where answers can actually be found. In analytics, this manifests in the debate over the merits of manual instrumentation of data versus automatic data capture (we call it “autocapture,” while some refer to it as “auto-track”).

But this whole argument is based on a false dichotomy. If relying solely on manual instrumentation is like looking for your keys under the streetlight, then relying solely on autocapture is explicitly avoiding the streetlight.

In the quest to improve your customers’ digital experiences, you should take advantage of all the data and insights at your disposal. Fullstory has built a platform that lets you leverage the strengths of both paradigms—tagless autocapture and instrumented events. We firmly believe that this approach is the way forward for behavioral data and product analytics.

Video: Fullstory CEO Scott Voigt speaks about autocapture

What is autocapture?

Before we go too much further, let’s break down a few basics about autocapture.

Autocapture is the ability to automatically log all digital interactions users or customers have with your website or app across every visit. To put this in perspective, some analytics tools only log digital interactions and events that you specifically instrument it to log.

At a high level, there are three types of data that autocapture logs:

Core events and properties

Core events are the events and variables that support an organization’s consistent key performance indicators (KPIs). Since they are part of a product development plan, core events are typically known in advance, and are valid and valuable over long periods of time. They often include values found in a page’s data layer, such as customer information, traffic source, cart value, or other information. KPIs that are tied to this subset of events may include things like daily active users (DAU), conversions, and mobile app logins.

Helpful tip: The vast majority of your core events—think roughly 80%—fall under instrumentation, whereas the remaining ~20% of core events are autocaptured. You might be thinking that instrumenting 80% of core events sounds like a hassle—but because core events change infrequently or not at all, keeping them up to date is a relatively small lift.

Transient events

Some metrics come and go as your product changes—these are your transient events.

A product manager will add and remove these from your metrics as features (and the questions you have about them) come and go. Agile product organizations build, ship, and iterate so often that transient metrics can lose meaning quickly. They may include things like feature adoption (which loses relevance when the feature is no longer new), interactions with specific CTAs or content, or engagement with seasonal shopping experiences.

Helpful tip: Transient events represent the rough inverse of core events; they will fall under autocapture about 80% of the time, and you may need to instrument transient events every so often (the other 20%). This is key because transient events fluctuate constantly, and autocapture ensures you have the data you need when it’s time to analyze them.

The long tail

The long tail represents all the other engagements that occur between your product and customers that you wouldn’t manually instrument (because it would be incredibly time-consuming), but hold an untold quantity of user intent and interaction insights.

Think about all of the macro-and micro-interactions that occur on your site or app over time: myriad navigation flows, form and UI component interactions, errors, performance and network logs, scrolling, clicks, highlights, and observable issues like frustrating workflow loops or Rage Clicks. Autocapture logs them all and reveals user trends over time, helping digital leaders uncover unknown unknowns that could mean millions in revenue opportunity.

Helpful tip: The long tail is 100% autocaptured data. This is how, over time, autocapture can reveal insights, trends, and opportunities that you weren’t even looking for.

Why it matters

Transient and long-tail events are by and large logged and surfaced through autocapture rather than through instrumentation. But as trends emerge from these two event types, they can become core events—or, in other words, business-critical metrics. This is why you need a solution that encourages exploration and experimentation—to allow you to surface these insights in the first place—while also offering the flexibility to allow for this type of agile movement without disrupting key metrics.

Autocapture is a necessary complement to instrumented events to answer the questions you have and the questions you don’t know you have.

Benefits of autocapture

TL;DR: There are a lot.

If you’re only tracking events that you’ve manually instrumented, you are not getting a complete picture of your behavioral data Here are four reasons why autocapture that’s paired with manual instrumentation is the future of behavioral data analytics.

1. Maintain data integrity as your site or app evolves

Sites and apps change constantly—new features are launched, page flows shift, integrations change, and so on. If you build metrics on top of a product that’s always in flux, they’re likely to break. And with instrumentation only, you start losing data the moment they stop working. Worse, you may not even realize anything is broken until you go looking for the data that isn’t actually there.

Autocaptured events can break too, but it’s far easier to know if and why an event isn’t being captured through session replay. Additionally, true autocapture updates your metrics retroactively—so even if there’s a change in the product, you don’t actually lose any data.

2. Ensure complete and reliable experience data

A small subset of core and transient events—like conversion, account registration, and new feature usage—are typically a manually instrumented part of any product analytics solution. However, in traditional product analytics tools, most events are never collected at all, and you’ll never know what insights may remain hidden.

Autocapture helps you answer the questions you didn’t know to ask. True autocapture combines instrumented events and unstructured behavioral data that creates endless learning opportunities: understand trends, create segments, track customer journeys, build conversion funnels, and gain a complete view of your user experiences..

3. Discover the unknown unknowns

Relying solely on instrumentation, if a question crops up, the only option is to manually tag it and then wait for a critical mass of data. Obviously, this isn’t efficient.

With autocapture in your arsenal, you don’t need to know everything you want to track upfront. Plus, solutions like Fullstory allow all users to add custom events from within the platform—no dev work or professional support needed.

With true autocapture, you can:

Add, remove, and manage events to see retroactive insights immediately
Edit, rename, group, and classify data elements in whatever way makes sense for your teams
Automatically identify and add new pages, updated buttons, and any other site changes
Log unexpected interactions or events that you wouldn’t have known to manually tag

When talking to analytics providers who claim autocapture, be sure to check how quickly you can access a new data point you’re hoping to learn about. If implementing a new metric requires professional services or a delay while data is gathered, you may not really be looking at a true autocapture solution.

4. Connect quantitative and qualitative insights

When partnered with session replay capabilities, autocapture enables teams to see beyond what users are doing and get to the sentiment behind the clicks..

For example, quantitative metrics might reveal that a significant percentage of users are dropping out of a funnel after the second step. No errors are being triggered—what’s causing the abandonment? By watching relevant sessions through session replay, it’s discovered that the chat bot pop-up was blocking the button that enables the user to move to the next step in the flow. Voila! The ability to pair quantitative and qualitative data—powered by autocapture—reveals revenue-impacting opportunities that wouldn’t be discoverable otherwise.

Let’s look at a real world example of the powerful combination of quantitative and qualitative data from Fullstory customer Travel + Leisure Co.

Before implementing behavioral data on their site, customer-facing teams at Travel + Leisure Co. responded to issues as they were reported by customers—essentially one at a time. With DX analytics that uses autocapture, when a problem is identified internally, the team is able to view the issue with Session Replay, identify the root cause, and fix it before it becomes a widespread issue. Because Fullstory uses autocapture to log customer interactions, the qualitative data surrounding digital issues is available in Session Replay even though the Travel + Leisure Co. team didn't originally assume they would need it.

How autocapture works

Like anything, there are right and wrong approaches to building technology that uses autocapture. As you evaluate behavioral data platforms that employ autocapture technology, there are certain questions you should be asking to ensure you’re getting a top tier solution. Let’s take a look at how best-in-class autocapture is done.

Autocapture and the DOM

The DOM, or Document Object Model, is how users experience and interact with the web. At its best, autocapture logs everything that occurs in the DOM. So, to understand how autocapture works, we also need to understand how the DOM works.

The DOM is the “physical” structure of your site or app. If your site were a house, the DOM would be the foundation, support beams, doors and windows, and so on. HTML, CSS, and JavaScript control how the site is constructed, how it looks, and how it behaves. As a user moves throughout your site, interacting with it and changing things, they are creating mutations to the DOM.

By capturing the DOM and all of the mutations that occur throughout a user’s visit, a reliable autocapture tool logs not only a user’s interactions, but all of the changes to the structure of the site or app itself. A true autocapture solution uses the DOM and other contextual information to derive user interaction events. This both provides important context for understanding and validating events, and ensures that event data is gathered safely.

The behavioral data engine

Industry-leading behavioral data platforms like Fullstory are built on a powerful data engine that …can ingest comprehensive, unsampled data with low latency through low-latency streaming infrastructure—all made possible by autocapture.

1. Operationalize data to avoid the pitfalls of sampling

When you have instant access to all of your data, you can investigate important-but-rare events that sampling or instrumentation methods can miss altogether. Unsampled data is also needed for operational uses, like configuring alerts and webhooks, empowering support teams with situation-specific sessions that help troubleshoot issues, and exporting complete data for external BI systems, and training machine learning models.

2. Get complete, reliable data with no gaps

Many digital experience solutions claim to offer machine-generated insights, but make it unclear how—or if—these insights are derived from data. Leading behavioral data platforms take a different approach: when insights are surfaced, you can clearly see where they came from and instantly validate them. This method takes the black box of insights out of the equation by letting you see exactly how metrics were derived.

3. Interpreting your data

With our unique approach to capturing every aspect of the DOM, only Fullstory has the data needed to deeply understand the structure of the sites and apps your customers are experiencing. Every page, popup, button, console error, network request—they’re all captured and available to the behavioral data engine. Our systems learn the nuances of your digital products and intelligently structure them into canonical definitions that bring accuracy and reliability to your analysis and insights.

For example, to scope your page-centered analysis to the right level of granularity, Fullstory uses machine learning to identify general URL patterns across similar page structures and group them into Pages. Without autocapture and machine learning, getting these types of insights could require a lot of manual work—or simply not get done at all.

What autocapture doesn’t do

We’ve talked about what autocapture is and how it works—now let’s tackle what autocapture is not.

One of the first ideas that might come to mind on the topic of autocapture is, “What am I supposed to do with all that data? It’s too much!” When autocapture is done well, you need not worry about manually sorting through mountains of unwanted events. Fullstory curates the events you care about, while preserving the ability to define new ones retroactively when needed.

You also never need to watch more than a small percentage of sessions to derive useful insights. Because the important trends are being extracted from them automatically by the behavioral data engine, you only need to watch sessions to support customers, understand bugs, and gain context on questions or complaints.

Perhaps most importantly, autocapture—when designed and used correctly—does not log PII and other sensitive data, or any elements that have been specifically excluded from capture.

Behavioral data tools like Fullstory use a Private by Default approach to autocapture, where no information is captured unless specifically allow-listed. This means organizations are far less likely to collect unwanted data than with a tool that simply skips over data points that have been marked for exclusion.

When it comes to ensuring that no sensitive data is being collected, a solution that can detect and alert you to any unwanted data that’s being captured is the gold standard. For instance, Fullstory Detections automatically surface when, where, and what types of PII may be slipping into your behavioral data, and enables teams to quickly triage and remediate these types of issues.

Additionally, autocapture does not collect data through screen recording, which is a method used by some less sophisticated session replay tools for mobile data capture. Because autocapture operates at the DOM level, the session replays you see in a best-in-class solution like Fullstory are recreations of the interactions a user had with a site or app, rather than a video or recording. This method leads to higher-fidelity session replays, doesn’t impact site performance, and lowers the likelihood of capturing unwanted information.

Autocapture and privacy

Any enterprise knows the importance of maintaining user privacy. Let’s take a closer look at the measures that leading solutions are taking to ensure that you have comprehensive behavioral data without risking data privacy.

A Private by Default approach to autocapture

We touched on it briefly in the previous section, but Private by Default is the most responsible way to capture digital experience data. With a Private by Default approach, only allow-listed information is captured and appears unmasked in your digital experience data. Here are three key benefits of using Private by Default in data capture:

1. Reduce risk with every deployment

Product teams can ship quickly and fearlessly without the need to rigorously confirm that the appropriate exclusions are perfectly configured. This privacy-first approach means you can safely update the allowlist after deployment, without the risk of collecting unwanted data in the interim.

2. Mask text at the source

With Private by Default, all text elements are masked at the source and never leave the customer’s browser or device. Where some solutions can only mask input fields, platforms like Fullstory can also mask on-page text that could provide sensitive contextual information.

3. Unmask precisely, in Fullstory or with custom code

Privacy settings allow authorized users to unmask safe elements in Fullstory, either one at a time or en masse via CSS selectors. Alternatively, use a code-first approach to manage masking directly from your codebase.

You don’t need PII to get ROI

You may be asking, “With all of that information excluded, is the behavioral data we’re capturing even going to reveal anything valuable about our users?”

The answer is a resounding yes.

Here’s what Ted Schlein, a general partner at Kleiner Perkins and Fullstory investor, had to say on the topic in an article for TechCrunch:

“Prioritizing individual profiles is far from the fastest or most effective way to understand and address customers’ intentions, needs and struggles. Brands don’t need to know who; they need to know what and why. Thanks to rapid advances in artificial intelligence (AI) and machine learning (ML), companies can process and interpret first-party data in real time and develop actionable behavioral intelligence…
Companies that have long relied on digital profiling to better understand customers and drive business decisions have been stymied by recent developments to curb third-party cookies and tracking. But by harnessing the power of AI to process vast amounts of first-party data about behavior—not individuals—companies can know and serve their customers better than ever while protecting user privacy.”

The future of analytics is here

A hybrid approach of both manual instrumentation and autocapture is the only path to uncovering the product and behavioral data insights that help businesses thrive.

Fullstory’s mission is to help you make sense of that data to create more perfect digital experiences for users everywhere. Schedule a demo to learn how behavioral data can help your business increase revenue, reduce churn and build products more efficiently.