Adaptive Marketing Campaigns Using Deep Reinforcement Learning: A Customer-Centric Approach

Abstract

In an era of data-driven decision making, traditional static marketing campaigns fall short in responding to rapidly evolving customer behaviors and preferences. This paper proposes a deep reinforcement learning (DRL) framework to enable adaptive, personalized marketing strategies that continuously evolve based on customer interactions. The proposed model treats marketing as a sequential decision-making process, optimizing campaign strategies through trial-and-error interactions with dynamic customer segments. The DRL agent learns to tailor content, timing, and channels of engagement to maximize long-term customer lifetime value (CLV), rather than short-term conversion metrics. Empirical experiments using simulated and real-world e-commerce datasets demonstrate that the DRL-based approach significantly outperforms rule-based and supervised learning baselines in retention rate, click-through rate (CTR), and cumulative revenue. The findings support the potential of DRL to drive customer-centric, self-optimizing marketing systems in digital commerce.