Reinforcement Learning For Self-Optimizing Customer Relationship Management Platforms: From Contextual Bandits To Deep Sequential Decision Systems

Authors: Santhosh Reddy BasiReddy

Abstract: Customer Relationship Management (CRM) platforms increasingly operate in complex, high-velocity environments characterized by massive interaction volumes, heterogeneous customer preferences, multi-channel engagement, and continuously evolving business objectives. In such settings, traditional rule-based automation and static supervised learning models often fail to generalize beyond historical patterns, leading to brittle decision logic and delayed adaptation to behavioral shifts. Reinforcement Learning (RL), grounded in sequential decision-making and long-term reward optimization, provides a principled foundation for building self-optimizing CRM systems that learn directly from ongoing customer interactions. By framing customer engagement as a dynamic control problem, RL techniques ranging from contextual bandits for real-time personalization to deep reinforcement learning for long-horizon lifetime value optimization enable CRM platforms to continuously refine engagement strategies, personalize workflows, and balance short-term conversions with long-term relationship outcomes. Drawing on foundational RL theory, recommender-system research, and applied CRM studies, this article develops a conceptual and architectural framework for RL-driven CRM platforms, while also addressing critical practical challenges such as offline policy evaluation, reward shaping under delayed feedback, system scalability, and ethical considerations including transparency, bias mitigation, and responsible automation.

DOI: http://doi.org/10.5281/zenodo.18185140