A/B Tests
A/B testing (also called split testing) is an experimentation method that compares two versions of a webpage, email, ad, or other digital asset to determine which one performs better against a defined metric. Traffic is randomly split between version A (the control) and version B (the variant), and statistical analysis determines whether the difference in performance is significant or due to chance. A/B testing removes guesswork from optimization by letting real user behavior drive decisions.
How A/B Testing Works
A properly structured A/B test follows a clear process. Start with a hypothesis based on data: “Changing the CTA button from ‘Submit’ to ‘Get My Free Report’ will increase form completions because it communicates value.” Next, create the variant with only one change so you can attribute any performance difference to that specific element. Split traffic evenly between control and variant, then run the test until you reach statistical significance, typically a 95% confidence level.
The key metrics you track depend on the test context: conversion rate for landing pages, click-through rate for emails, revenue per visitor for ecommerce pages, or engagement metrics for content. Testing multiple variables simultaneously requires multivariate testing, which is more complex and demands higher traffic volumes.
What to A/B Test
High-impact elements to test include headlines, call-to-action buttons (text, color, size, placement), form length and fields, pricing page layouts, email subject lines, ad copy and creative, and page layout structure. Prioritize tests based on potential impact and traffic volume. A test on a high-traffic landing page will reach significance faster and drive more overall improvement than a test on a low-traffic page.
Not everything needs to be tested. Minor changes on low-traffic pages may never reach statistical significance, wasting time and resources. Focus testing efforts where data suggests a meaningful gap between current performance and potential.
Common Mistakes
The most frequent A/B testing errors include ending tests too early (before statistical significance), testing too many variables at once, ignoring segment-level results, and failing to account for external factors like seasonality. Another critical mistake is not having a clear hypothesis; running random tests without data-driven reasoning rarely produces actionable insights.
Use dedicated A/B testing software to manage experiment setup, traffic allocation, and statistical analysis rather than attempting to build testing infrastructure from scratch. For choosing the right tool, see our guide to the best landing page software, which covers platforms with built-in A/B testing capabilities.
