This method can help brands obtain more concrete ROI on ads.
This piece gives insights into Bayesian A/B testing that involves continuous calculation and trial and error for testing variants to find out audiences’ preferences. This ad testing method involves more specific and tactical approach than traditional A/B testing, by factoring in more metrics.
Bayesian tests collect the information from similar past experiments and combine that with current data to reach a conclusion. The conclusion obtained from the previous Bayesian experiments acts as a variant for the new test. The test can also be periodically modified to improve test results.
Bayesian A/B tests do not require marketers to change their software or different channels. They can instead leverage the tools they have to get more calculated results and then continuously run tests.
[5 minute read]