PPC A/B testing is a powerful way to improve the effectiveness of your advertising campaigns.
In this practical guide, you’ll discover what A/B testing for PPC is, and learn about the different types of tests and test statistics you need for data-driven decisions. You’ll also learn how to set up your first A/B test and get actionable, high-impact ideas to try.
What is A/B testing for PPC?
A/B testing for PPC is a method of testing two or more variants france mobile database of your advertising campaign elements, such as ad copy, landing pages, or targeting, with the goal of providing statistical proof for various hypotheses, which can then be leveraged to refine your campaigns and improve results.
While not entirely different from landing page or email A/B testing, PPC A/B testing requires a dedicated approach due to the limitations of ad platforms, varying sample sizes, and the risk of impacting the overall performance of your campaigns.
Types of PPC Testing
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There are four main types of A/B testing in PPC:
A/B Testing
An A/B test is a hypothesis-driven experiment that prompts you to change a single element of your advertising campaign and test it against the original control variant. This is the most common type of test that helps you narrow down specific elements and refine your campaigns.
A/B Test Example: Test 2 text ads with free shipping versus 15% off as the main offer.
Multivariate tests
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A multivariate test is an experiment with multiple hypotheses and multiple changes. With this method, you test different combinations of small changes made to your control variable. I rarely use this type because it requires the largest sample size (usually impossible for PPC) of all four test types and produces the smallest increase in results, thus decreasing the confidence level (see my definitions of sample size, increase, and confidence level in the next section).
Multivariate testing example: testing 4 creatives with different combinations of headlines and images.
A/B/n Testing
An A/B/n test is also an experiment with multiple hypotheses and multiple changes. However, unlike a multivariate test, the variants can be completely different from each other. This is one of the types of tests I often use for new accounts or new campaigns where there is no historical data available and I want to test completely different configurations or combinations of elements rather than narrowing my selection with A/B or multivariate tests.
A/B/n Testing Example: Testing 2+ sets of creatives with completely different layouts and/or landing pages.
Sequential tests
A sequential test is a type of A/B test that tests variants of campaign elements in phases or sequences. A sequence can be 2 weeks, 1 month, or longer (I don’t recommend running a test for less than 2 weeks). This is the least preferred type of test, as running a test over different time periods introduces external factors that you can’t control, such as seasonality, sample size variation, and targeting bias. However, it’s also a common type, as not every PPC platform offers full (or any) A/B testing capabilities.
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