Optimization of Marketing Campaigns with Reinforcement Learning

This research introduces a reinforcement learning-based system which enables marketers to optimize campaign budgets dynamically in response to live consumer behavior patterns. The Markov Decision Process (MDP) formulation uses customer engagement metrics and channel performance to drive ongoing learning and decision-making processes. Historical marketing data serves as the basis for comparing three RL models: Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO). The PPO model delivers superior performance by reaching a 7.4% Click-Through Rate (CTR), 32.5% Return on Investment (ROI), and lowering the Cost Per Acquisition (CPA) to 9.2 dollars. RL-based methods deliver superior performance compared to rule-based systems by increasing conversion rates by 71% and engagement rates by 45% . Feature importance analysis reveals Customer Lifetime Value and Engagement Score as primary predictive factors that provide understandable information for campaign targeting. The framework exhibits scalability and ethical standards while remaining practical which allows it to adapt well to various campaign sizes and supply actionable data-driven