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Glossary · Marketing, CRM & Growth

A/B Testing

Definition: A/B testing is a method of comparing two versions of a web page, email, or ad by showing each to a random half of your audience and measuring which drives more conversions. It replaces opinion with statistical evidence about what actually works.

Reference: Wikipedia

Overview

How A/B testing works

A/B testing (also called split testing) divides your traffic randomly between two variants: the current version (A) and a changed version (B). Because visitors are assigned at random and see only one version, any difference in conversion rate can be attributed to the change. You run the test until you have enough data to be confident the result isn't chance.

What to test and how to read results

Common tests include headlines, calls to action, button placement, page layouts, pricing displays, and email subject lines. The key discipline is changing one variable at a time and waiting for statistical significance — typically a 95% confidence level — before declaring a winner. Ending a test early on a promising but unstable lead is the most common and costly mistake.

A/B testing in real optimization work

Testing pays off when it's tied to a clear hypothesis and real traffic. A full-service team uses A/B testing alongside conversion rate optimization, Hotjar heatmaps, and analytics to decide what to test next, then applies wins to landing pages, sales funnels, and app UI. Paid channels like Google Ads and email tools such as Klaviyo and Mailchimp give tests the volume they need.

Where we use it

Related Zen in Tech services

How our team puts A/B Testing to work in real projects.

FAQ

A/B Testing — common questions

How long should an A/B test run?

Long enough to reach statistical significance and cover at least one full business cycle, usually one to four weeks. Stopping early or with too few conversions produces unreliable results that don't hold up once you ship the change.

What's the difference between A/B testing and multivariate testing?

A/B testing compares two whole versions that differ by one element. Multivariate testing changes several elements at once to find the best combination, but it needs far more traffic to reach reliable conclusions.

How much traffic do I need for A/B testing?

It depends on your baseline conversion rate and the size of improvement you want to detect, but low-traffic pages can take months to reach significance. A sample-size calculator gives a realistic estimate before you start.

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