A/B testing

What is A/B testing?

A/B testing is a method of comparing two versions of a webpage, email, app feature, or other digital content to determine which one performs better. It involves showing two variants (A and B) to similar audiences at the same time and measuring which version drives more conversions, clicks, sign-ups, or other desired actions. This experimentation approach uses statistical analysis to determine whether changes to your content create meaningful improvements in performance. A/B testing removes guesswork from optimization efforts by providing concrete data about what actually works with your audience.

How does A/B testing work?

A/B testing begins by identifying what you want to improve and creating a hypothesis about what change might lead to better results. You then create two versions: the control (version A, your current design) and the variant (version B, with your proposed change). When users visit your site or open your email, they're randomly assigned to see either version A or version B. Their interactions are tracked, measured, and compared to see which version performs better against your success metrics. The test runs until you've collected enough data to reach statistical significance—the point at which you can be confident the difference in performance isn't due to random chance. This typically requires hundreds or thousands of visitors depending on your conversion rates.

When should you use A/B testing?

A/B testing is most valuable when you have sufficient traffic to gather meaningful data in a reasonable timeframe. It's ideal for optimizing high-impact pages like landing pages, checkout flows, sign-up forms, and email campaigns. Use A/B testing when you have specific, measurable goals such as increasing conversion rates, reducing bounce rates, or improving engagement metrics. It's particularly effective when you have competing ideas about what might work better, or when you're considering a significant change but want to validate its impact before full implementation. A/B testing is also valuable when you need to prove the ROI of design or content changes to stakeholders.

What elements can you A/B test?

Almost any element of your digital experience can be A/B tested. Common elements include headlines, call-to-action buttons (text, color, size, placement), images, page layouts, form fields, pricing displays, navigation menus, and content length. In emails, you might test subject lines, sender names, content structure, or send times. For apps, you can test features, onboarding flows, notifications, or interface elements. Even small changes like button color or headline phrasing can sometimes create significant performance differences. The most effective A/B tests often focus on changes that directly address user pain points or friction in the conversion process.

How do you analyze A/B test results?

Analyzing A/B test results requires looking beyond simple conversion rates to understand statistical significance. A good analysis examines whether the difference between variants is large enough to rule out random chance—typically requiring a confidence level of 95% or higher. Pay attention to sample size; tests with too few participants won't yield reliable results. Look for segments within your audience that responded differently to each variant, as this can reveal opportunities for personalization. Consider secondary metrics beyond your primary goal to understand the full impact of changes. Sometimes a variant might increase immediate conversions but harm long-term metrics like retention. Finally, document your findings and use them to inform future tests, building a knowledge base of what works for your specific audience.