ab-test-setup

To design an A/B test for conversion rate optimization, you need to formulate a structured hypothesis, calculate the required sample size based on your baseline conversion rate, and select primary, secondary, and guardrail metrics to measure success.

The best way to calculate sample size for split testing is by defining your baseline conversion rate and the minimum detectable effect you want to observe, which determines the traffic allocation needed for statistical significance.

Formulating a strong hypothesis for growth marketing experiments requires using a structured framework to ensure your predicted outcome is data-driven, testable, and directly tied to meaningful variant design changes.

Choosing between client-side and server-side A/B testing implementation depends on your variant design requirements and technical stack, with server-side offering deeper control over user experiences and client-side being faster to deploy.

During an experimentation campaign, you should track primary metrics to measure your main objective, secondary metrics to observe adjacent impacts, and guardrail metrics to ensure you do not harm other areas of the user experience.