CUX allows you to go beyond surface-level A/B test results by uncovering why users behave differently across variants. Instead of relying only on conversion numbers, you can combine different perspectives to build a full picture of user behavior and decision-making.
Checklist for reliable A/B test analysis in CUX Before you start:
- Run variants simultaneously with evenly split traffic to avoid timing bias.
- Plan sample size and duration in advance; continue the experiment until the planned threshold is reached, and avoid stopping early unless you use sequential testing methods.
- Ensure correct variant identification (via URL, element ID, or Data Layer values).
- Verify instrumentation before launch: check the CUX snippet, event IDs, and Data Layer integration.
Analyzing results:
- Use Waterfalls to analyze user flows and identify step-level drop-offs.
- Use Goals to measure conversions and compare the characteristics of users who completed key actions.
- Use Heatmaps to visualize attention and interaction patterns, explaining why one variant performs better than another.
Bringing it all together:
- Combine methods for deeper insights: Waterfalls show the journey, Goals show the outcomes, and Heatmaps reveal the attention behind those outcomes.
By approaching your analysis with this structure, you ensure that insights from CUX are consistent, interpretable, and ready to guide confident product decisions.