A/B testing lets you change variables, such as your ad creative, audience or placement, to determine which strategy performs best and to help improve your future campaigns. A/B testing helps ensure your audiences will be evenly split and statistically comparable, as opposed to informal testing, which can lead to overlapping audiences.
To run an effective experiment, you should first choose a hypothesis - or question - you want your experiment to answer. For example, you might hypothesize that a custom audience strategy will outperform an interest-based audience strategy for your nonprofit. An A/B test lets you quickly compare both strategies to see which one performs best.
After choosing the variable you want to test, we’ll divide your budget to equally and randomly split exposure between each version. A/B testing can then measure the performance of each strategy on a Cost Per Result basis or Cost per Conversion Lift basis with a holdout.