Leveraging Reinforcement Learning and Neural Networks for Optimized Dynamic Pricing Strategies in B2C Markets

Abstract

This research explores the integration of reinforcement learning (RL) and neural networks to enhance dynamic pricing strategies in business-to-consumer (B2C) markets. Dynamic pricing, a crucial component of revenue management, aims to optimize price points based on consumer behavior and market conditions. Traditional models, however, often lack the adaptability required to respond effectively to real-time market fluctuations and consumer demand shifts. This paper presents a novel framework that employs reinforcement learning algorithms, specifically designed to iteratively adjust and optimize pricing strategies. Neural networks are incorporated to augment the capability of RL by predicting customer responses and interpreting vast datasets, which include historical sales data, market trends, and customer demographics. The proposed model is evaluated through simulations and real-world scenarios, demonstrating significant improvements in pricing efficiency and revenue outcomes compared to conventional static and rule-based pricing models. Key performance metrics, such as price elasticity, demand forecasting accuracy, and revenue uplift, are analyzed to validate the efficacy of the system. The study also investigates the impact of various RL parameters and neural network architectures, providing insights into the optimal configurations for diverse market conditions. This work contributes to the existing literature by offering a scalable and adaptable pricing solution that can be seamlessly integrated into existing B2C platforms, advancing the frontier of intelligent pricing systems in competitive markets.