Hotjar / FullStory
Session recording and behaviour analytics tools that show exactly how real users move, click, scroll, and struggle on your website or product.
What it is
About Hotjar / FullStory
Hotjar and FullStory are user behaviour analytics platforms that capture and replay real user sessions, build heatmaps of clicks and scroll depth, and surface moments where users encounter friction. Unlike quantitative analytics (which shows that users drop off at step three of a funnel), these tools show the why — you watch a recording of a specific user who abandoned the checkout and see them clicking a broken button repeatedly, scrolling past the call-to-action three times, or hitting an error message they then give up on. Hotjar is the more accessible tool: its heatmaps, session recordings, and on-site survey pop-ups are easy to set up and interpret, and its free tier covers most small-to-medium sites. FullStory is the enterprise-grade alternative — it autocaptures all DOM interactions without requiring event instrumentation, provides a powerful search interface for finding sessions where a specific error occurred, and integrates with support tools like Zendesk to link a support ticket directly to the session recording of the user who filed it. For a UX Designer, these tools answer the questions that usability testing does not: what are real users in the wild actually doing, not what do recruited test participants say they do in a moderated session? A designer who spots fifty users all pausing at the same scroll depth on a landing page — despite the heatmap showing that the CTA button is below that fold — has concrete evidence to propose a page restructure. For a Digital Marketing Manager, heatmaps on landing pages reveal whether users read the value proposition before hitting the CTA, or whether they are scrolling straight to a price or a comparison table, which fundamentally changes how the page should be written.
What you can do with it
Capabilities
Open the Hotjar heatmap for a key landing page and identify the scroll depth at which 50% of users stop reading — if the primary call-to-action is below that fold, you have quantitative evidence that it needs to move up the page
Filter FullStory sessions by users who triggered a specific error message and watch three recordings back-to-back to identify whether they all encountered the same underlying cause — building the bug report with exact reproduction steps before it goes to the engineering team
Use Hotjar's Recordings filter to find sessions where users spent more than 90 seconds on a form page without submitting — watching them to identify whether they are reading, re-reading, or clicking on non-interactive elements before abandoning
Add a Hotjar feedback poll triggered on the exit intent of a specific page — for example, asking users who are leaving the pricing page without converting what prevented them from signing up, then coding the qualitative responses into themes
Compare heatmaps for two versions of a landing page after an A/B test — not just checking conversion rates but checking whether the click distribution on the winning variant reveals any unintended user behaviour, like users clicking an image they expected to be a link
How to learn it
Learning Resources
Hotjar Academy (hotjar.com/blog/hotjar-101) — free; short articles covering heatmap interpretation, session recording best practices, and how to combine Hotjar data with Google Analytics for root-cause analysis
FullStory University (help.fullstory.com) — free; documentation and video guides covering the search and segmentation interface, DX Data reports, and integration setup
Hotjar's free tier gives full access to recording and heatmap features on low-traffic sites — install the snippet on a personal project and study a week of real sessions to develop intuition for what normal behaviour looks like before identifying friction
Contentsquare Blog (formerly Hotjar blog) — free; practitioner articles on behaviour analytics methodology, including how to avoid confirmation bias when interpreting heatmaps
Pro Tip
The most common mistake with session recordings is watching them randomly. The insight-to-hour ratio is far higher if you filter first: watch sessions where users spent over two minutes on a page but did not convert, or sessions that ended in a 404 error, or sessions from a specific traffic source that has an anomalously low conversion rate. Random sampling of sessions produces interesting anecdotes; filtered sessions produce actionable hypotheses.
Skills that use this tool
Roles that use this tool
Alternatives