Optimizing Customer Targeting Using Reinforcement Learning and Neural Networks for Adaptive Marketing Strategies

Optimizing customer targeting in marketing is crucial for maximizing returns, yet traditional approaches often struggle to adapt to changing customer behaviors. This paper presents a reinforcement learning-based framework that models customers' spending inclinations as normal distributions to optimize targeting strategies. We introduce a novel mean-stat strategy, which leverages statistical confidence intervals to dynamically adjust exploration-exploitation trade-offs. Unlike traditional methods such as $\epsilon$-greedy and decaying $\epsilon$-greedy, which are used for comparison, the mean-stat strategy is mathematically proven to provide superior performance by efficiently identifying high-value customers. Additionally, a neural network-based approach using the cross-entropy method further enhances adaptability by learning complex patterns in customer behavior. Extensive simulations validate the effectiveness of these strategies, demonstrating the practical advantages of incorporating advanced learning techniques and mathematical rigor into marketing strategies.