Qini-based Uplift Regression
This work addresses customer retention for insurance companies, but it is incremental as it builds on existing logistic regression methods.
The paper tackles the problem of isolating marketing effects in customer churn reduction by introducing a Qini-based uplift regression model, which significantly improves performance by acting as a regularizing factor, resulting in interpretable models with few variables.
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. 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 parsimonious models with few relevant xplanatory variables. Our results show that performing Qini-based parameters estimation significantly improves the uplift models performance.