Dynamic Pricing Strategy Driven by Deep Reinforcement Learning with Empirical Analysis on the Collaborative Optimization of Hotel Revenue Management and Customer Satisfaction

In the context of hotel revenue management, dynamic pricing plays a crucial role in maximizing revenue while maintaining a delicate balance with customer satisfaction. Traditional pricing strategies often depend on static rules or overly simplistic models that lack the ability to adapt to real-time changes in market demand, evolving customer behavior, and competitive trends. These outdated approaches can lead to missed revenue opportunities and suboptimal guest experiences. This research addresses these challenges by proposing a dynamic pricing strategy driven by deep reinforcement learning, which integrates real-time data streams with predictive analytics to create a highly responsive and intelligent pricing system. At the core of the proposed methodology is a novel framework that combines advanced deep neural network architectures with adaptive optimization algorithms. This framework is designed to optimize both pricing and inventory decisions across multiple booking channels simultaneously. The Innovative Pricing Transformer (IPT) model underpins this framework by leveraging attention mechanisms and temporal sequence modeling to accurately forecast future demand and recommend context-aware pricing decisions. In addition, the Adaptive Yield Optimization Strategy (AYOS) refines this process by incorporating real-world operational constraints such as overbooking policies, price parity requirements, and channel-specific pricing rules, ensuring practicality and compliance. Empirical analysis conducted on real-world datasets reveals that our approach consistently outperforms traditional pricing models, not only in revenue enhancement but also in improving overall customer satisfaction. The proposed strategy represents a scalable, efficient, and intelligent solution for modern hotel revenue management, enabling hotels to remain agile and competitive in dynamic and uncertain market conditions.