Customer & Marketing
Combining behavioral data from heatmaps with quantitative information from Google Analytics (GA) improves understanding of users and conversions. Several tools offer native integration with GA or GA4.
| Tool | Integration method | Benefits | |
|---|---|---|---|
| Hotjar | Native integration with GA: recordings and heatmaps can be filtered by GA events, and the Hotjar user ID can be sent to GA to establish correlations. | Combine quantitative and qualitative data to optimize funnels and identify problem pages. | |
| Crazy Egg | GA4 integration: send custom events to GA4 to compare metrics between A/B test variants. | Helps analyze variant performance on GA segments, such as new vs. repeat visitors. | |
| Smartlook | Full connection to GA: open records directly from GA segments in just a few steps. | Simplifies access to replays to understand the behavior behind a GA statistic. | |
| UXCam | Integration with Firebase GA: send events and session URLs to GA to connect quantitative metrics and replays. | Enables analysis of the full context of an action in a mobile application (via Firebase). |
Enable integration: in most tools, simply activate the option in the dashboard and connect the GA account via an API or OAuth key. Custom tags can be added to GTM to send events.
Define events: determine the relevant actions (CTA clicks, cart abandonment, conversions) and configure the corresponding events in GA and the heatmap tool.
Analyze segments: use GA to create segments (e.g. new users, paid traffic, visitors by region) then open the associated replays or heatmaps to see the difference in behavior.
Combining heatmaps with Google Analytics (GA) enables quantitative data to be complemented by qualitative observations. GA provides global metrics on traffic, conversions and events, while heatmaps reveal how users interact visually with pages. Their integration provides a 360° view of user behavior and facilitates decision-making. Here’s a detailed guide to the integration and combined use of the two tools, as well as case studies.
Data correlation: GA shows how many users click on a link, but not where exactly. Heatmaps fill this gap. By linking GA sessions and events to replays, we can see how a click occurs and in what context.
Advanced segmentation: GA lets you create segments based on acquisition channel, geography or behavior (new vs. old visitors). Once integration has been activated, these segments can be applied to heatmaps and replays to compare the behavior of different groups.
Conversion optimization: GA’s aggregated data can be used to identify pages with low conversion rates. By accessing the heatmaps and replays of these pages, we can identify the reasons for the problem (call-to-action not very visible, form too long).
Hotjar: integration is achieved by activating the “connect to Google Analytics” option in the dashboard. It is then possible to filter records and heatmaps by GA events (for example, “users who triggered event add_to_cart”). You can also send the Hotjar session ID to GA via a custom dimension, in order to link a GA session to a Hotjar record. This makes it possible to click on a GA report line and directly open the corresponding session.
Crazy Egg: the tool offers integration with GA4 by sending customized events (event_name, variant_id) for A/B testing. Simply create a tag in Google Tag Manager (GTM) to trigger the event for each interaction. The data is then visible in the GA4 interface in the form of event parameters, enabling sessions to be segmented by test variant.
Smartlook: configuration is achieved by adding an add-on module to GA, which enables Smartlook recordings to be launched directly from GA reports. The tool provides ready-to-use GTM code to send the Smartlook session link to GA via a custom dimension. GA segments can then be applied to filter sessions for replay.
UXCam: for mobile applications, UXCam integrates with Firebase GA. By sending application events to Firebase while attaching the UXCam session URL, the video can be accessed from the Firebase dashboard. A custom field needs to be added to the event (e.g. “uxcam_url”) and populated via the UXCam API. This integration makes it possible to link crashes or mobile behavior to replays.
Other tools: some tools (Matomo, Plerdy) offer their own analysis module and do not require GA. However, it is possible to use GTM to send events to GA in parallel, in order to cross-reference data.
Determine which data to send: identify the relevant GA events (CTA clicks, conversions, cart abandonments) and the heatmap metrics you wish to link (click maps, scroll maps, replays). Create custom dimensions to store the heatmap provider’s session ID.
Configure Google Tag Manager (GTM): use GTM to insert the heatmap tool script and create tags that send data to GA. For example, for Hotjar, add a tag that sends the session ID (hj_session_user) to GA for each page viewed.
Set up segments and views: in GA, create segments based on events or customized dimensions. For example, a “Visitors who added to cart” segment can be used to filter the corresponding replays in Smartlook.
Validate data collection: check that events and dimensions appear correctly in GA. Use GTM’s debug mode to ensure that tags are triggered as expected.
Train teams: document the process and train marketing, product and support teams in the use of GA segments and access to heatmaps. Good adoption is essential to take advantage of integration.
Analysis of a conversion funnel: a travel booking site noticed a drop in the conversion rate on the payment page. In GA, the team identifies a segment of users who leave the page without finalizing the booking. Opening the Smartlook replays associated with this segment, they discovered that the payment button was hidden below the waterline on certain screen resolutions. By repositioning the button, the conversion rate improved.
Marketing campaign optimization: after an e-mail campaign, GA shows a good click-through rate to the product page, but a low purchase rate. The Hotjar-GA integration can filter these users’ sessions and look at the heatmaps. The team noticed that the price was not very visible and that the “Buy now” button blended into the background. By adjusting the colors and adding a “Why this product” section, it increases sales.
A/B testing: thanks to the Crazy Egg-GA4 connection, an e-commerce site can analyze the performance of A/B variants on several dimensions (new vs. old customers, mobile vs. desktop traffic). Replays help to understand why a variant works better. For example, variant B shows more scrolls in the customer reviews section, meaning that users are looking for more information before buying.
Don’t overload GA: sending too many events and custom dimensions can complicate analysis. Select only the data relevant to your objective.
Make sure identifiers are consistent: the link between GA and the heatmap tool is based on a common identifier (session ID, user ID). Make sure this ID is unique and consistent throughout the session.
Ensure compliance: the integration of several tools involves the transfer of data to external services. Check that your use complies with data protection laws, and inform users in your privacy policy.
Test implementations: after any site or GA update, check that tags and event senders still work. Changes in DOM structure or cookie rules can break integration.
Analyze data together: use GA to identify high-stake pages and segments, then switch to heatmaps and replays for qualitative analysis. Document actions taken and track results in GA to measure impact.
Integrating a heatmap tool with Google Analytics is essential for contextualizing qualitative observations and measuring the impact of modifications. Hotjar, Crazy Egg, Smartlook and UXCam offer native integrations, enabling rapid switching from statistical data to video recordings. By following a structured methodology – identifying key events, configuring via GTM, creating segments and validating data – companies can fully exploit this synergy and make more informed decisions.