Hotel Room Revenue Management: A Systematic Review Using PRISMA Approach

Abstract

Revenue management is a critical strategy for hotels to optimize pricing, demand forecasting, and inventory utilization in competitive, data-driven markets. Over time, Hotel Room Revenue Management (HRRM) has gained substantial academic attention. However, existing research often focuses on isolated components such as pricing, forecasting, or overbooking, resulting in a fragmented body of knowledge. This study addresses this gap through a systematic review of HRRM literature from 1980 to 2025 using the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). A total of 145 studies were analyzed and classified into three core areas: customer reservation management, inventory allocation, and dynamic pricing. The review also evaluates modelling approaches, solution methods, and the use of real hotel data, while identifying key operational challenges. Findings indicate that most studies emphasize reservation management and dynamic pricing, with comparatively limited focus on inventory allocation. Many studies rely on single-hotel contexts and small datasets, limiting generalizability and scalability. Inconsistent data usage and the lack of standardized HRRM frameworks remain key issues. This review highlights the need for integrated, data-driven HRRM frameworks that combine artificial intelligence, analytics, and optimization to improve decision-making and overall revenue performance.