On revenue management and the use of occupancy forecasting error measures

This study aims to draw the attention of the revenue management academic community to inherent problems in forecasting accuracy measurement, and to initiate a critical discussion about forecast quality assessment in hotels. An exhaustive, literature-based set of seventeen forecasting accuracy measures was applied to hotel daily occupancy forecasting data of 2043 pairs of computer and human forecast/actuals, across multiple forecasting horizons. The empirical analysis demonstrates endemic inconsistencies across the accuracy measures, and a plethora of theoretical and practical challenges with regard to total hotel, as well as customer segment level forecast accuracy assessment. The analysis illustrates the difficulty of interpreting conflicting results, as well as issues like level of data aggregation and multiple forecasting horizons. The paper concludes by briefly discussing a more comprehensive approach to hotel forecasting quality assessment framework and serves to warn hotel revenue management academics, practitioners and solution providers against the unconsidered use of accuracy measures.     

Competitive revenue management for fixing quota and price of hotel commodities under uncertainty

This article develops an artificial neural network (ANN) based forecasting model using the past profit records of hotel commodities. Based on forecasting, hotel commodities are categorised into two kinds: ones that push up the revenue, and others, which pull it down. Thereafter, long and short term goals are formulated for fixing quota and proper revenue management under uncertainty. For long term goal, analytical network process (ANP) framework is adopted to establish interrelationships among the factors using DEMATEL methodology. Risk adjusted maximum expected profit is employed for short term goal. Subsequently optimal numbers of commodities are obtained using a fuzzy goal programming approach, and favourable price of the individual commodities is determined keeping the price elasticity as one. Finally, a comparison is made between the respective revenue generated with the new quota and price from the proposed revenue maximization model, and that of the old practised price and quota. The paper demonstrates the superiority of the proposed approach. A case study of a hotel has been taken up to demonstrate the model.     

Influences of consumer characteristics on fairness perceptions of revenue management pricing in the hotel industry

Fairness perceptions play an important role in customers’ behavior, and this study explores which consumer characteristics influence fairness perceptions of revenue management (RM) pricing in the hotel context. To examine such differentiating characteristics, the study conducts a logit analysis by comparing two groups: one group of customers who consider hotels’ RM practices to be fair and the other group considers the practices to be unfair. The findings provide an opportunity for hotel managers to identify customers’ particular characteristics that affect customer's perceptions of the fairness of hotels’ RM pricing practices.     

Game theoretic pricing models in hotel revenue management: An equilibrium choice-based conjoint analysis approach

This paper explores a game-theoretically founded approach to conjoint analysis that determines equilibrium room rates under differentiated price competition in an oligopolistic hotel market. Competition between hotels is specified in terms of market share functions that can be estimated using multinomial logit models of consumer choice. The approach is based on choice-based conjoint analysis that permits the estimation of attributes weights (“part-worths”) for an additive utility formulation of the utility function. From this, room rates that equilibrate the market, conditioned on the differences in services and facilities offered by competing hotels, can be determined. The approach is illustrated by an example.     

Relationship or revenue: Potential management conflicts between customer relationship management and hotel revenue management

The concepts of customer relationship management (CRM) and revenue management (RevM) have been embraced by managers in the hospitality industry although, in practice, companies may find it difficult to accommodate both fully. This paper examines the compatibility between the two practices and discusses the possible management conflicts that occur from both account managers’ and revenue managers’ viewpoints. Findings gathered from an international hotel company reveal several causes of potential management conflicts including: management goals, management timescales, perceived business assets, performance indicators and management foci between CRM and RevM due to divergence occurring in managers’ priorities and in their approaches to achieving their individual set goals. These differences have rarely been comprehensively investigated in previous studies, yet are vital in integrating CRM and RevM practices.     

Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows

Forecasting is the initial component of the hospitality revenue management (RM) cycle. The accuracy of the forecast is critical for RM systems to make appropriate recommendations to optimize revenue. Over recent years the industry has cited shifting booking windows due to a variety of macro (e.g., technology and economy) and micro (e.g., promotion) factors. These shifts pose challenges for RM forecasting algorithms particularly in the domain of pick-up based techniques. In this paper, we review the literature on hotel RM forecasting, particularly with respect to popular techniques used in practice. We then introduce a neural network approach to the advance booking environment to address issues related to booking window shifts. The models are estimated and tested for accuracy, and then re-tested years later after the booking window has shifted. The results are synthesized with discussion as to which models are more suitable for forecasting in dynamic booking windows.     

A meta-analysis of international tourism demand forecasting and implications for practice

Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.     

Application of revenue management practices to the theme park industry

Revenue management (RM) has been an essential strategy to maximize revenue for many capacity-limited service industries. Considering the common industry characteristics of traditional RM industries, the nature of the theme park industry suggests potential for enhancing revenue by exercising a variety of RM techniques. This study suggests practices for theme park operators for successful RM application. In addition, this study examines how customers perceive RM practice in the theme park industry compared to a traditional RM industry, hotel industry. The findings indicate that customers seem to perceive RM practice in the theme park industry as relatively fair practices as similarly perceived for the hotel industry. The findings are encouraging for the theme park industry because a relatively similar level of its customers’ perceived fairness of the RM practice compared to the hotel industry suggests that adoption and implementation of the RM practice has great potential to become successful as it has been in traditional RM industries, such as hotels.     

Novel deep learning approach for forecasting daily hotel demand with agglomeration effect

Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity.     

Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.     

Theme park revenue management with a dual admission policy

There are several efforts to increase the revenue of theme parks. One of them is to divide admission policy into normal and express admission. If a certain customer buys express admission ticket with more expensive price, it is possible to reduce the excessive waiting time by using the exclusive waiting line for some or all the attraction’s rides. In this study, decision-making elements such as the price of the express admission ticket, maximum allowed number of express admission ticket per day, and the type of rides which can apply the express admission ticket are investigated to maximize the revenue.     

Function space revenue management: A product bundling approach to hotel function space utilization

This article extends revenue management research into non-traditional hotel operating departments pertaining to convention hotel function space. This research evaluates utilization patterns of multiple convention hotel function spaces and identifies concurrent high-, medium- and low-utilization groupings of multiple convention spaces using k-means clustering. These high- and low-utilization function space pairings allow us to identify convention spaces to target for additional revenue management oversight. A Bayesian analysis of potential lift in revenue per available foot (RPAF) attributable to product bundling concludes the research linking hot and cold function spaces compatible with value based price packaging opportunities.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

Strategic responses of the hotel sector to COVID-19: Toward a refined pandemic crisis management framework

The Covid-19 pandemic has long-lasting impacts that require the hotel sector to revise, innovate and transform their businesses. However, the literature related to this area remains vastly under-developed. Based on 219 articles collected from global news media and an integrated crisis management framework, this research note map out "strategic responses" from the hotel sector and suggest implications for hotels to address the evolving pandemic situation. Three modifications were proposed to refine and further develop a pandemic crisis management framework.     

Hotel revenue management and the Internet: The effect of price presentation strategies on customers’ willingness to book

A decision that is intrinsic to the application of hotel best available rate (BAR) pricing is how to present the BARs for individual nights within a multiple-night stay to prospective hotel guests. We discuss two alternative price presentation strategies, a blended and a nonblended rate approach, and examine their effect on customers’ willingness to pay in the context of Internet-based reservation requests. Study findings indicate that a nonblended rate presentation approach generates higher willingness to book ratings than a blended rate presentation approach. Furthermore, when it comes to nonblended rates, familiarity with BAR pricing moderates the effect of rate sequence on customers’ willingness to book.     

From internal brand management to organizational citizenship behaviours: Evidence from frontline employees in the hotel industry

This paper examines the links between internal brand management, organizational identification, work engagement, and organizational citizenship behaviours in the hospitality industry. A model is proposed and tested using a sample of 323 frontline employees from the main hotel chains operating in Spain. This model includes different dimensions of internal brand management and different manifestations of citizenship behaviours. Data are analysed through the use of partial least squares. The findings confirm that transformational leadership leverages organizational identification and work engagement. However, brand training and brand communications do not directly raise positive emotions in the workplace. The results also suggest that work engagement is a better predictor of citizenship behaviours than organizational identification. Identification influences citizenship behaviours towards the organization. However, this variable does not explain extra-role supportive behaviours towards customers and other employees. These results extend previous research by empirically analysing the effects of internal brand management from the employees' perspective.