When visitors leave a page without completing an action such as booking, signing up, or making a purchase, it indicates a high abandonment rate. In most cases, this happens because of unseen friction in the page layout or call-to-action placement. 

CausalFunnel’s Product A/B Test and User Segment Statistics features together give a clear view of where users stop engaging and what can be changed to retain them.

Step 1: Open the Product A/B Test

Start by going to the Product A/B Test under the A/B Test Performance section.

This section helps you set up and monitor experiments for any product or service page. A/B testing works by comparing two versions of the same page to see which performs better.

Here, from the Select Test menu, you choose the URL where you want to run the test. This ensures that the system tracks how visitors interact with every element on that particular page. The data generated helps identify which version is driving higher engagement and conversions.

Why this matters:

Selecting the correct test URL ensures that the tracking data is relevant and accurate. Without specifying the exact page, results could reflect unrelated user behaviour, which makes any decision unreliable. This step lays the foundation for the rest of the analysis.

Step 2: Move to User Segment Statistics

Once the test URL is selected, go to User Segment Statistics and choose the same URL from the list.

Click on View Details. This opens the heat map, which visually displays how users interact with the page. Each colour-coded area shows where users click, scroll, or spend time. The map separates activity for mobile and desktop devices, allowing you to understand behaviour across screens.

Why this matters:

User Segment Statistics helps identify which sections of the page attract attention and which are ignored. If users are reading information but not moving towards the action button, it highlights a gap between interest and conversion intent. This insight helps pinpoint specific problem areas instead of guessing what might be wrong.

Step 3: Interpret the Heat Map

In one observed case, the heat map showed that most visitors were reading the upper portion of the page, engaging with the introductory content, but were not clicking the Book Now button. On reviewing the page structure, it was found that the button appeared only after three scrolls.

This meant that users interested in booking were not reaching the button easily. The drop-off wasn’t due to lack of intent but due to the button’s placement.

Why this matters:

The heat map translates user behaviour into visual evidence. It makes it easier to see how design and layout affect engagement. Without such visual data, these insights remain hidden and assumptions could lead to unnecessary changes.

Step 4: Apply a Change Based on the Finding

After identifying the issue, the Book Now button was repositioned to appear just below the banner on both mobile and desktop. This simple structural change made the call-to-action visible without extra scrolling.

After this adjustment, the results were immediate and measurable. The clicks on the Book Now button doubled, and the overall conversion rate rose by 20 percent.

Why this matters:

Even small changes based on behavioural data can deliver significant results. The objective is not to completely redesign the page but to remove barriers between user intent and the action you want them to take.

Step 5: Verify the Results

Once the changes are implemented, revisit the Product A/B Test section.

Compare the data from the two versions of the page; the original and the modified one. This comparison confirms whether the change made a measurable difference in user actions.

Why this matters:

Verification prevents false conclusions. A visible improvement in conversion rate after a change validates that it truly influenced user behaviour. This step ensures that every decision is backed by evidence, not assumptions.

Practical Example: The Sea Rockets Case Study

A clear demonstration of this approach can be seen in one of our client success stories involving Sea Rockets, a company offering boat tours. They were facing a similar challenge; high page visits but low bookings.

Using CausalFunnel’s A/B Testing and heat map tools, the team noticed that their Book Now button appeared too far down the page, resulting in missed clicks. By simply moving the button higher, just below the banner, Sea Rockets achieved a 20 percent increase in conversions.

The outcome reinforced a key learning: identifying friction points through A/B testing and visual analytics enables precise, data-backed decisions that directly improve user engagement.

Conclusion

This process demonstrates how the CausalFunnel dashboard can turn visitor behaviour data into actionable insights that reduce abandonment rates. By observing user interactions, testing small changes, and comparing results, businesses can identify why visitors drop off and implement solutions that encourage them to complete their intended actions.

A/B testing, combined with behavioural analytics, replaces guesswork with clarity; allowing every improvement to be deliberate, measurable, and aligned with user intent.