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Glossary
Product concepts

What is A/B Test?

An A/B test is a research method where two versions of a product feature, webpage, or user flow are compared to determine which one performs better. By showing Version A (the control) to one group and Version B (the variant) to another, teams can make data-driven decisions based on actual user behavior. This process identifies which version leads to higher engagement, conversion, or satisfaction.

When to use it

Use A/B tests when you have a specific hypothesis about how a change will impact a key metric, such as conversion rate or retention. It is an effective way to refine user interfaces, onboarding flows, or call-to-action placement before committing to a full-scale rollout. This method helps teams avoid shipping features that might negatively impact the user experience.

Example

A SaaS team aims to increase their trial-to-paid conversion rate. They run an A/B test where 1,500 users see a pricing page with 'Monthly' billing as the default, while another 1,500 users see 'Annual' billing as the default. After a month, the 'Annual' variant shows an 8% higher conversion rate with 97% statistical significance. Based on this data, the team permanently updates the pricing page, resulting in higher upfront revenue without a decrease in total signups.

How Planet Roadmap helps with A/B Test

Planet Roadmap has an OKR tree feature that lets you track A/B test results as Key Results. The public feedback portal also lets you collect user sentiment on different test variants.

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FAQ

How do I know when an A/B test has reached a valid conclusion?
A valid conclusion is reached when the result is statistically significant and the sample size is large enough to represent your user base. You should also ensure the test runs long enough to account for weekly patterns, as user behavior on a Monday often differs from behavior on a Sunday.
Can I run multiple A/B tests on the same page simultaneously?
Running multiple tests on one page can lead to 'interaction effects' where it is difficult to tell which test caused a change in behavior. For the cleanest data, it is best to run tests sequentially or use advanced tools that can isolate specific user cohorts for each experiment.

Related terms

Feature FlagCohort AnalysisActivationRetentionProduct-Market FitMinimum Viable Product (MVP)Key Result

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