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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Bayesian A/B Testing for Business Decisions
verfasst von : Shafi Kamalbasha, Manuel J. A. Eugster
Erschienen in: Data Science – Analytics and Applications
Verlag: Springer Fachmedien Wiesbaden
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Controlled experiments (A/B tests or randomized field experiments) are the de facto standard to make data-driven decisions when implementing changes and observing customer responses. The methodology to analyze such experiments should be easily understandable to stakeholders like product and marketing managers. Bayesian inference recently gained a lot of popularity and, in terms of A/B testing, one key argument is the easy interpretability. For stakeholders, “probability to be best” (with corresponding credible intervals) provides a natural metric to make business decisions. In this paper, we motivate the quintessential questions a business owner typically has and how to answer them with a Bayesian approach. We present three experiment scenarios that are common in our company, how they are modeled in a Bayesian fashion, and how to use the models to draw business decisions. For each of the scenarios, we present a real-world experiment, the results and the final business decisions drawn.