From Prediction to Action: Integrating Counterfactual Analysis with Logistic Regression for Interpretable Customer Churn Intervention

Abstract

Customer churn critically impacts business sustainability, necessitating not only accurate prediction but also interpretable, actionable insights. This study addresses the gap between identifying churn drivers and formulating executable strategies. This paper employs logistic regression coefficient analysis on a bank churn dataset (n=10,000) to identify the top four churn drivers: active_member (coefficient: -1.042), country_Germany (0.733), age (0.732), and gender_Male (-0.488). Subsequently, virtual intervention experiments are designed to quantify the potential impact of strategic actions. For instance, a 10% increase in the proportion of active members is projected to reduce the churn rate by approximately 0.7%, from a baseline of 20.36%. Similarly, interventions on geographic composition, demographic structure, and gender ratio are also simulated, with their respective effects on churn rate meticulously quantified. The findings provide a rigorously quantified foundation for strategic decision-making. The intervention simulations enable prioritization of retention tactics based on their projected efficacy, allowing businesses to allocate resources optimally and implement the most cost-effective strategies to mitigate churn.