In web analysis, A/B testing is a method of comparing two versions of a webpage, feature, or element to determine which one performs better. Typically, traffic is split equally between variants - for example, version A (the original) and version B (the modified version).
It is important that both variants run at the same time, rather than sequentially (e.g., two weeks of version A followed by two weeks of version B). Running them simultaneously ensures that differences in user behavior are due to the changes being tested, not external factors such as seasonal trends, marketing campaigns, or other time-dependent influences. By observing how users behave in each variant under the same conditions, you can make more accurate, data-driven decisions about design, copy, or functionality.
It’s important to note that CUX is not a tool for creating A/B tests, but it is the perfect place to analyze your experiment. The setup of an A/B test and traffic distribution must be done in a testing platform of your choice (such as a dedicated A/B testing tool) or your own development framework. But why exactly is it better to use CUX for A/B test analysis?
While testing platforms usually provide only high-level results (e.g., conversion rates, clicks, or statistical significance), CUX provides quantitative data (the what) and also helps you understand why users behave differently between variants. With CUX, you go beyond numbers to uncover behavioral patterns - where users hesitate, which steps of the journey cause drop-offs, and how attention shifts across page elements. This deeper insight makes CUX a valuable complement to your testing setup.