Definition: Controlled experimentation method that compares two variants of an element to determine which produces better measurable results with statistical significance.
— Source: NERVICO, Product Development Consultancy
What is A/B Testing
A/B testing is a controlled experimentation method where two variants (A and B) of an element are presented to different user groups to measure which produces better results. Variant A is typically the current version (control) and variant B includes the change being evaluated. Traffic is randomly split between both variants, and each group’s metrics are compared with statistical rigor to determine whether the difference is significant.
How it works
The team formulates a hypothesis (“changing the purchase button color from gray to green will increase conversion rate”). The primary metric to measure (conversion rate), the sample size needed for statistical significance, and the experiment duration are defined. Traffic is randomly split: 50% sees variant A and 50% sees variant B. At the end of the period, results are analyzed with statistical tests (such as chi-square or z-test) to confirm whether the difference is statistically significant or due to chance.
Why it matters
Product decisions based on intuition or team opinion are frequently wrong. A/B testing provides empirical evidence for making informed decisions. It validates changes before deploying them to the entire user base, reducing the risk of implementing modifications that worsen metrics. Mature product teams run dozens of simultaneous experiments to continuously optimize the user experience.
Practical example
A SaaS platform observes that 70% of users abandon the registration process at step two. The team creates a variant B that reduces the form from 8 fields to 3 (name, email, password) and moves the rest to post-onboarding. The test runs for two weeks with 10,000 users per variant. Variant B shows a registration completion rate of 62% versus 30% for variant A, with 99% statistical significance. The change is implemented for all users.