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.
Funding Statement
Mouloud Belbahri and Alejandro Murua were partially funded by The Natural Sciences and Engineering Research Council of Canada grant 2019-05444. Vahid Partovi Nia was supported by the Natural Sciences and Engineering Research Council of Canada grant 418034-2012.
Acknowledgments
We would like to thank the Editor, Associate Editor and the reviewers for their valuable comments and suggestions that helped us to improve this manuscript.
Citation
Mouloud Belbahri. Alejandro Murua. Olivier Gandouet. Vahid Partovi Nia. "Qini-based uplift regression." Ann. Appl. Stat. 15 (3) 1247 - 1272, September 2021. https://doi.org/10.1214/21-AOAS1465
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