Uplift modeling with value-driven evaluation metrics

摘要

Measuring the success of targeted marketing actions is challenging. Research on prescriptive analytics recommends uplift models to guide targeting decisions. Uplift models predict how much a marketing action will change customers' behavior, known as the individual treatment effect (ITE). Marketers can then solicit customers in decreasing order of their estimated ITE. We argue that the ITE-based targeting policy is not fully consistent with a business value maximization objective. We propose business-centric evaluation metrics that integrate estimates of the ITE and the expected business value and validate their benefits relative to the ITE-based targeting baseline using real-world marketing data. The new metrics yield remarkably higher profit across different uplift models, targeting depths, profit functions, and data sets. They further contribute to the growing field of interpretable data science by uncovering interdependencies between covariates, ITE, and profit and by clarifying whether customers are worth targeting because of high responsiveness or high value.