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June 17, 2019
There is nothing better than a head-to-head A/B test to drive marketing decisions. A/B tests lead to simple analytics that almost everyone in the organization can understand and act on. If you’ve run at least a few A/B tests, this workshop will show you how to use more powerful analytics tools like predictive modeling to get more insight out of your tests. After reviewing the basics of A/B test analysis, we will talk about testing strategies you can use when you have a very large or very small sample sizes. We will cover a pile of advanced techniques including heterogeneous treatment effects, uplift modeling, causal forests, blocking, matching, and stratification. If this sound like a bunch of jargon to you right now, that’s okay. I will demystify the jargon and help you understand when and why you might use these techniques in analyzing your A/B tests. We will also cover Bayesian approaches to A/B testing including test & roll and multi-armed bandits.