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Online controlled experiments are the primary tool for measuring the causalimpact of product changes in digital businesses. It is increasingly common fordigital products and services to interact with customers in a personalised way.Using online controlled experiments to optimise personalised interactionstrategies is challenging because the usual assumption of statisticallyequivalent user groups is violated. Additionally, challenges are introduced byusers qualifying for strategies based on dynamic, stochastic attributes.Traditional A/B tests can salvage statistical equivalence by pre-allocatingusers to control and exposed groups, but this dilutes the experimental metricsand reduces the test power. We present a stacked incrementality test frameworkthat addresses problems with running online experiments for personalised userstrategies. We derive bounds that show that our framework is superior to thebest simple A/B test given enough users and that this condition is easily metfor large scale online experiments. In addition, we provide a test powercalculator and describe a selection of pitfalls and lessons learnt from ourexperience using it.

Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls