Optimizing Customer Lifetime Value with Reinforcement Learning

Abstract

Sequential customer management decision-making needs dynamic strategies. Reinforcement learning provides computational models that train algorithms to respond optimally. Q-learning algorithms hold estimates of state-action values, which allow systems to determine the engagement strategies that yield the highest long-term payoff. Deep reinforcement learning enhances these abilities that can recognize more intricate behavior patterns. Optimization of interaction frequency helps balance customer engagement and fatigue, which reduces responsiveness. Personalization technology selects promotional materials based on individual tastes and buying records. The allocation of incentive plans provides discounts and rewards to segments that demonstrate the highest profit potential. Experimental validations compare reinforcement learning against rule-based heuristics and supervised prediction models, resulting in significant advances in retention rates and transaction values. The implementation problems include reward function specifications to align with business goals, exploration strategies to identify new approaches, and deployment architectures for real-time decisions for a large customer base.