A/B Testing

Last updated 15/10/2026

Also known as: Split Testing, A/B Test, Split Test

A/B testing compares two versions of an ad, email or page by showing each to a similar group of people and measuring which performs better. Changing one element at a time, such as the headline or the image, shows what actually made the difference.

A fair test needs a clear goal decided in advance, such as clicks, sign-ups or cost per result, and enough people in each group for the difference to be more than chance. Ending a test after a day because one version is ahead is a common way to learn the wrong lesson. Ad platforms such as Meta have built-in A/B test tools that split the audience, so the two versions do not compete for the same people.

The most useful tests change something that matters: the offer, the angle, the opening line or the image, rather than a button colour. Testing with small budgets first and putting more money only behind proven winners turns advertising into a series of lessons rather than a single bet. Multivariate testing, which changes several elements at once, needs far more traffic to give a reliable answer.

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