About Split Testing
Last updated, May 2024

Split test, commonly referred to as A/B testing, is a tool in TikTok Ads Manager that allows you to test two different versions of your ads to determine which one performs best and optimize future campaigns.

There are currently three variables you can test: Targeting, Bidding & Optimization, and Creative.

You may have a hypothesis that one creative will perform better than the other. You can use Split Test to keep the other variables the same and split your audience into two equal groups with each group seeing only one ad group in order to draw a statistical conclusion about your ad performance.

Split Test is a quick and precise way to compare strategies and measure changes in performance, ultimately helping you scale your spend for the best return.

Why use split testing?

  • ​Test to find your optimal ad settings

Split test is one of the quickest ways for you to accurately test different versions of ads in order to learn which ad settings are the most effective and produce the highest return on ad spend. Following TikTok's guidance and best practices is important, but the best way to learn what works for you is by running Split Tests.

  • ​Learn from accurate and statistically significant results

Split Test is scientifically designed to perform an accurate A/B test, with a 90% confidence rate when determining which ad group performs better. Using Split Testing ensures that each audience group will exclusively see one ad group, preventing "squeezing issues" (Group A and Group B compete directly for mutual audiences). The model selects and verifies a winning ad group only if the results are statistically significant, helping you feel confident in running that ad group.

  • ​Optimize and strategically scale your spend

Split Test allows you to experiment and identify new best practices to improve your advertising strategy. At the end of a Split Test, you can simply choose to continue running the winning ad group, optimizing towards your campaign goals with just one click. Don't be afraid to run more Split Tests to continue refining and optimizing your ad strategy.