Online experimentation, or A/B Testing, is the gold standard for measuring the effectiveness of changes to a website. While A/B testing is used at tens of thousands of companies, it can seem difficult to parse without resorting to expensive end-to-end commercial options. Using DataCamp’s system as an example, I’ll illustrate how R is actually a great language for building powerful analytical and visualization A/B testing tools. We’ll first dive into our open-source funneljoin package, which allows you to quickly analyze sequential actions using different types of behavioral funnels. We'll then cover the importance of setting up health checks for every experiment. Finally, we'll see how Shiny dashboards can help people monitor and quickly analyze multiple A/B tests each week.
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