Agent-Driven Customer Retention using Reinforcement Learning and Behavioral Analytics

Abstract

Customer retention continues to be a significant issue for contemporary firms because of increasing competition and evolving customer dynamics. Existing churn prediction models tend to concentrate on detecting churners but lack mechanisms for formulating adaptive decisions for retention purposes. In this study, we propose a customer intelligence approach to identify customer personas through RFM (recency, frequency, monetary value) feature engineering, natural language processing, and K-Means Clustering techniques to generate customer personas. We then use reinforcement learning to select specific retention actions based on customer personas. For this purpose, we design an agent-based system that takes as input a set of persona-dependent actions and simulates feedback using a feedback simulation environment to generate rewards and learn optimal retention strategies for customers. Our experimental evaluation conducted on an e-commerce behavioral data set shows promising results in developing agentic decisions for retention actions based on the generated customer persona. Our study contributes to the literature by showing how to use reinforcement learning for decision-making processes in customer retention problems.