A/B testing definition: Importance, process & more

What is A/B testing?

A/B testing is a statistical method that compares two different versions of a web page, email, or ad to determine which one performs better. By randomly splitting an audience into two groups and showing each group different versions, you uncover the option that delivers optimal conversion/user engagement rates.

Why should you do A/B testing?

There are several reasons why businesses should conduct A/B testing:

  • Improve conversion rates - Get more sign-ups, purchases, or downloads
  • Gain insights into user behavior - Optimise user journeys based on how users interact with you tests
  • Reduce bounce rates - Identify and remove friction points that cause users to leave your site
  • Increase user engagement - Reveal the site elements identifying that attract users to your site and keep them there
  • Make data-driven decisions - Remove guesswork from your optimisations

What does the A/B testing process look like?

The A/B or split testing process usually involves eight steps:

  • Define the objective - This could be increasing conversions, click-through rates, or engagement
  • Identify the variables - Decide which elements you want to test, like the headline, button color or image
  • Create variations - Create multiple versions of the asset you’re testing, focusing on one specific variable
  • Randomly assign visitors - Split your audience in two and show different versions to each group
  • Collect data - Record each group’s interactions with the asset, noting things like clicks, conversions and sign-ups
  • Analyse results - Compare the results of each variation to determine which one performs best
  • Implement the winning version - Monitor performance and iterate on your final version to ensure future success
  • Repeat the process - Test the effectiveness of other variables across your digital infrastructure

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