A Reinforcement Learning Approach for Adaptive Budget Allocation in Pharmaceutical Digital Marketing: Maximizing ROI Across Patient Journey Touchpoints

Abstract

This paper introduces a novel reinforcement learning framework for dynamic budget allocation in pharmaceutical digital advertising, addressing critical challenges in optimizing marketing resources across patient journey touchpoints. Traditional budget allocation methods in pharmaceutical marketing fail to adapt to complex, multi-channel environments and regulatory constraints, resulting in suboptimal ROI. We propose a comprehensive reinforcement learning approach that models budget allocation as a sequential decision-making problem, with a state space encompassing channel performance metrics, audience characteristics, and regulatory parameters. The framework incorporates a multi-objective reward function balancing immediate conversion metrics with long-term value generation while maintaining compliance requirements. Experimental validation using 24 months of real-world pharmaceutical marketing data across five therapeutic areas demonstrates significant performance improvements over conventional methodologies. The reinforcement learning framework achieved an average ROI increase of 42.3% compared to baseline methods, with particularly strong performance in rare disease categories (69.9% improvement). The system demonstrates effective learning convergence across diverse therapeutic contexts while maintaining regulatory compliance. This research provides both theoretical contributions to AI applications in healthcare marketing and practical implementation strategies for pharmaceutical companies seeking to optimize digital advertising investments across increasingly complex patient journey touchpoints.