Using Meta-Learners and Propensity Score Matching to Optimize Customer Retention

Companies are increasingly using predictive modeling to manage customer churn proactively. While extant customer retention literature centers mainly on propensity models, recent research indicates merits of uplift models for targeting retention efforts toward customers. However, prior research on uplift modeling relies largely on experimental data and tailored uplift algorithms, making it difficult and costly for practitioners and researchers to apply. Thus, we investigate the applicability and competitiveness of an uplift modeling procedure for customer retention management combining propensity score matching with meta-learner approaches (standard machine learning algorithms). Using a semi-synthetic churn dataset with 1980 customers, we affirm the effectiveness of propensity score matching for reducing covariate imbalance in observational data. The empirical experiments show that meta-learner uplift models outperform tailored uplift random forest approaches regarding Qini scores and computation efficiency. Moreover, the results imply that targeting retention efforts based on a meta-learner uplift model reduces churn more effectively than using propensity models.