Qini-based Uplift Regression

TL;DR

This paper introduces a Qini-based uplift regression model using logistic regression to improve customer retention targeting, resulting in more interpretable and effective marketing campaign predictions.

Contribution

It proposes a novel Qini-optimized uplift regression approach that enhances model performance and interpretability for customer retention campaigns.

Findings

Qini-based optimization improves uplift model performance

Models become more interpretable with fewer variables

Significant performance gains over traditional methods

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. 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.