Leveraging Reinforcement Learning and Gradient Boosting for Optimized AI-Driven Dynamic Pricing Strategies in B2C Markets

Abstract

This research paper explores the integration of Reinforcement Learning (RL) and Gradient Boosting (GB) algorithms to develop robust dynamic pricing strategies for business-to-consumer (B2C) markets. In rapidly evolving market environments, businesses seek to optimize pricing strategies to maximize revenue, enhance customer satisfaction, and maintain competitive edges. Traditional pricing models often fail to adapt to fluctuating demand and consumer behavior, necessitating advanced methodologies. We propose a hybrid framework that leverages the adaptive capabilities of RL with the predictive accuracy of GB models. The RL component dynamically adjusts prices by learning from environmental interactions and historical sales data, while GB fine-tunes these decisions through its superior handling of non-linear relationships and interactions between predictive features. A comprehensive dataset from a leading e-commerce platform serves as the basis for empirical evaluation, where the hybrid model demonstrates a significant increase in sales conversion rates and profitability compared to traditional pricing strategies and standalone models. Furthermore, sensitivity analyses reveal the model's robustness to diverse market conditions and consumer segments. The study underscores the potential of combining RL and GB in crafting AI-driven pricing solutions that dynamically respond to market stimuli, offering a scalable approach that can be generalized across various industries to enhance B2C market strategies.