Personalization and Targeting: How to Experiment, Learn, and Optimize

Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these new tools and offers practical recommendations to develop personalized marketing policies. It defines the essential steps for crafting and executing effective campaigns, including the design of experiments, the utilization of observational customer data, the modeling of heterogeneous customer responses to marketing interventions, and policy evaluation. Further, the article identifies key challenges, including institutional and societal obstacles stemming from these newfound opportunities, and presents solutions and best practices to help researchers and companies leverage personalized marketing policies more effectively.