Identifying A/B testing opportunities through behavioural data
Strong A/B testing programmes start with insight. The best test ideas usually come from real user behaviour, not guesswork. When a website is tagged properly and important interactions are tracked, businesses can uncover small touchpoints that have a surprisingly strong relationship with conversion performance.
We recently worked with an insurance website where we were tracking a range of user interactions and analysing how those actions related to conversion outcomes. One interaction stood out immediately: downloads of the policy brochure PDF.
On the page, the brochure download was presented as a low-key text link rather than a prominent call to action. Only around 5% of visitors were downloading the brochure, but those users were roughly three times more likely to go on to purchase a policy.
Why brochure downloads stood out
This pattern suggested the brochure might be playing an important role in helping users make a decision. The policy documents were well designed, visually engaging, and explained the benefits of the products in much more detail than the page itself could.
At that point, the key question was whether the brochure was genuinely influencing conversions or whether these users were simply more motivated and therefore more likely to both download the document and buy anyway. That uncertainty made it a strong candidate for A/B testing.
The A/B testing hypothesis
Our hypothesis was simple: if more users downloaded the brochure, and brochure downloads were genuinely influencing purchase behaviour, then making the download more prominent should increase brochure engagement while maintaining or improving conversion performance.
To test this, we changed the brochure download from a small text link to a more visible button-style call to action. The aim was to increase interaction with the brochure and then measure whether the stronger conversion trend remained.
The result
The result was a significant increase in brochure downloads. We saw downloads roughly double after the change, which showed that more users were engaging with the content when it was given greater visual prominence.
More importantly, the conversion rate through to policy purchase was maintained at the stronger level associated with brochure downloaders. This gave us much more confidence that the brochure was not just correlated with conversion, but was likely helping influence the decision-making process.
Why tracking matters for A/B testing
Without proper tagging and interaction tracking, this opportunity would have been easy to miss. At a surface level, the brochure download looked like a relatively minor action because only a small percentage of users interacted with it. But the behavioural data showed that it had a meaningful relationship with conversion performance.
This is why strong analytics foundations matter so much in experimentation. When businesses only track the most basic page views and conversions, they often miss the smaller interactions that help explain why users buy, hesitate, or drop out. Those micro-conversions and supporting actions can become some of the most valuable sources of A/B testing ideas.
How to identify better A/B testing opportunities
If you want to identify stronger A/B testing opportunities, make sure your site is measuring more than just the final conversion. Track the interactions that may influence decision-making, such as brochure downloads, calculator usage, video views, form engagement, navigation behaviour, and other key touchpoints across the journey.
Once those interactions are tracked properly, you can start to analyse which actions are associated with stronger outcomes and use that insight to develop more meaningful test hypotheses. This creates a better experimentation process because ideas are based on observed behaviour rather than internal opinion.
Using analytics to build better test hypotheses
A/B testing works best when it is grounded in evidence. Good analytics helps businesses spot behavioural patterns, identify friction points, and uncover actions that may be helping or hurting conversion performance. Those insights can then be turned into structured hypotheses and tested through controlled experiments.
If you need help improving site tagging, uncovering behavioural insights, or turning your analytics into a stronger experimentation roadmap, Kraken Data can help.