Restaurant revenue management: a systematic literature review and future challenges

Abstract

Purpose – This study aims to examine the evolution of restaurant revenue management (RRM), highlighting emerging research areas and challenges. It aims to provide a structured overview of revenue optimisation strategies, emphasising the impact of digital transformation, customer behaviour shifts and technological advancements. The study offers practical insights for restaurant operators on leveraging data-driven strategies, including dynamic pricing, artificial intelligence (AI)-powered forecasting and menu engineering, to enhance profitability and customer satisfaction. By mapping the field’s trajectory, the research identifies opportunities for future investigation, ensuring that restaurant managers and academics have a clear framework for optimising revenue performance in an increasingly digital and competitive environment. Design/methodology/approach – This study employs a systematic literature review (SLR) to analyse 108 articles published between 1997 and 2023 from major academic databases. The methodology ensures a structured, transparent and replicable synthesis of research trends in RRM. Following a five-step process, the study identifies key strategic levers and methodological gaps, integrating recent developments in big data analytics, digital transformation and AI-driven forecasting models. By categorising RRM literature into five strategic levers, this research provides a comprehensive understanding of the field’s evolution and its implications for future revenue management practices in the restaurant industry. Findings – The impact of COVID-19 has accelerated digital transformation in the restaurant industry, driving a fundamental shift in RM research. Adopting digital menus, consolidating online reservations and expanding delivery services have prompted new research avenues in data analytics, capacity optimisation, price personalisation and operational efficiency. As the industry evolves, academic literature reflects a clear transition toward data-driven RM, leveraging sophisticated tools to maximise profitability and enhance the customer experience in the post-pandemic landscape. Originality/value – This is the first systematic review integrating post-pandemic digitalisation trends in RRM. The study introduces an expanded framework that incorporates information and sales management as key revenue levers. It explores AI-driven decision-making, real-time data analytics, and behavioural pricing strategies, setting the foundation for future research in restaurant revenue optimisation. By addressing the sector’s ongoing digital transformation, this study provides valuable recommendations for industry practitioners and researchers, helping restaurants to implement innovative revenue strategies that improve financial performance while enhancing customer experience in an evolving, technology-driven market.