Leveraging Reinforcement Learning and Predictive Analytics for Enhanced Customer Lifetime Value Optimization

Abstract

This research paper explores the integration of reinforcement learning (RL) and predictive analytics as a novel approach to optimizing customer lifetime value (CLV) across various industries. By leveraging advanced machine learning techniques, the study addresses the limitations of traditional CLV models, which often rely on static, rule-based frameworks that fail to capture the dynamic nature of customer interactions and preferences. The proposed model utilizes reinforcement learning to adaptively personalize marketing strategies, creating an iterative loop where customer responses to marketing actions are continuously analyzed and optimized. Predictive analytics are employed to forecast future customer behavior, providing essential inputs that enhance the RL model's decision-making capability. The research demonstrates the effectiveness of this integrated approach through empirical testing on diverse datasets from retail and subscription services, highlighting significant improvements in precision and profitability over conventional methods. Key performance indicators such as customer retention rates, average revenue per user, and overall CLV are analyzed to validate the model's efficacy. The findings suggest that this RL-augmented predictive analytics framework not only boosts CLV but also offers strategic insights into customer segmentation and engagement tactics, paving the way for more intelligent and responsive customer relationship management systems. Implications for business applications and future research directions are discussed, emphasizing the potential of this hybrid approach to transform marketing strategies in the digital age.