Lydia González-Serrano

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.

Revenue Management (RM) is one of the challenges facing the restaurant industry, mainly due to the lack of technology in this sector and the lack of data. Forecasting is the most valuable input of RM. For this reason, the main objective of this research is the proposal of a sales forecasting model based on the data provided by the tickets of a restaurant to extract information that allows the correct management of price and capacity. A system based on an unsupervised Machine Learning (ML) model was implemented to analyze the information and visualize the relationships between dishes and temperatures. The developed system uses unsupervised ML techniques, such as multicomponent analysis and bootstrap sampling, to identify and visualize statistically relevant relationships between data. This study provides a simple and understandable solution to improve management and maximize profits to support restaurant managers’ decision-making.

Purpose: This research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.Design/methodology/approach: This qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.Findings: Further professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Research limitations/implications: This study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.Practical implications: The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Originality/value: This work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies in hospitality industry by generating a great amount of valuable information about clients, whereas Big Data tools are providing with unprecedented facilities to conduct massive analysis and to focus the client-to-business relationship. However, few instruments have been proposed to handle categorical features, which are the most usual in CRMs, aiming to adapt the statistical robustness with the best interpretability for the managers. Therefore, our aim was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system. An analysis method was created involving three elements: First, Multiple Correspondence Analysis provides us with a statistical description of the interactions among categories and features. Second, bootstrap resampling techniques give us information about the statistical variability of the feature maps. Third, kernel methods provide easy-to-visualize domain descriptions based on confidence areas in the maps. The proposed methodology can provide an operative and statistically principled way to scrutinize the CRM profiles in hospitality.

Despite the key role played by frequent consumers interacting on smart P2P (peer to peer) tourism platforms, there are hardly any studies on the explanatory variables of their frequency of use. This paper aims to understand the motivational and sociodemographic factors that bring about repeat consumers in collaborative accommodation and transport services. In order to test various assumptions, a set of logistic regressions were made where the dependent variable is the frequency of use and the independent variables are sociodemographic and motivational factors. The results suggest that many consumer attributes recognized as being typical of collaborative platforms, such as young people traveling with friends for leisure who are interested in low prices, have changed. We found that, due to the consolidation of these smart business models, the frequency of use increases with age and for those who travel for work reasons. In addition, it is worth noting the existence of a positive relationship between consumers who provide reviews on these platforms and their frequency of use.

Purpose: The aim of this paper is to define which characteristics of a hotel and staff organization determine successful revenue management (RM) implementation in urban hotels. Design/methodology/approach: Multiple linear regression analysis was used to determine whether the characteristics of the hotel and the staff member in charge of RM are explanatory variables for the degree of RM implementation. Findings: The findings show that hotel category, chain affiliation and the existence of qualified full-time staff are the determinants for successful RM implementation. The creation of the revenue manager position alone is not determinant in the degree of RM implementation, but his/her qualification and dedication are fundamental variables. Research limitations/implications: Given that this survey was confined to the region of Madrid, it would be interesting to carry out similar research nationwide. Practical implications: This study provides researchers and professionals guidance for the satisfactory implementation and use of RM, tailored to the characteristics of any given establishment. Therefore, this paper states that the creation of a qualified, on-site, full-time revenue manager position is crucial. Originality/value: To the best of the authors’ knowledge, this is the first paper to conduct a formal statistical analysis of the relationship between hotel characteristics, staff organization and the degree of RM implementation. The greatest contribution of this research is the empirical demonstration that having a qualified full-time staff in charge of RM leads to better results in terms of RM implementation as opposed to not having one, having one part-time or having one at the corporate level. Therefore, this paper states that the creation of the professional revenue manager position within hotels is fundamental. Thus, the reported results provide a significant contribution to the literature related to the characteristics and organization of RM in hotels.

Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.