Reinforcement Learning for Customer Lifetime Value Optimization: A Conceptual Framework and Directions for Future Research

Abstract

As a technique for improving sequential decision-making in customer-centric marketing situations, reinforcement learning has attracted growing interest. With special attention on its concordance with customer lifetime value maximization, this paper investigates how reinforcement learning has been employed in client modeling, personalization, pricing, engagement, and retention.  In this study, a conceptual research methodology of the theory synthesis type was employed beginning with reviewing databases from Google Scholar, Scopus, and Web of Science keywords such as 'reinforcement learning' AND 'customer lifetime value' (2015-2026), yielding more that 100 studies after screening Although current research shows great potential to affect long-term customer behavior, most uses depend on short-term or surrogate performance indicators rather than explicitly maximizing lifetime value. This study logically and theoretically combines previous studies to offer a conceptual framework connecting reinforcement learning to lifetime value optimization, presents a taxonomy of approaches and tasks, and highlights major obstacles, knowledge gaps, and future directions to help to overcome this restriction. The paper sees reinforcement learning as the basis for creating ethically, value-driven, scalable consumer intelligence systems.