Qini-based uplift regression

Abstract

Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity, only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. In practice, the uplift models performance is measured by the Qini coefficient. We introduce a Qini-based uplift regression model to analyze a large insurance company’s retention marketing campaign. Our approach is based on logistic regression models. We show that a Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression. This results in interpretable models with few relevant explanatory variables. Our results show that Qini-based parameter estimation significantly improves the Qini prediction performance of uplift models.