Hotel Revenue Management Research Articles

Game—Gimme a Better Price! A Negotiation Role Play on B2B Pricing in Hotel Revenue Management

We present a classroom role play to immerse students in the difficulties of applying value-based pricing principles to the practice of B2B price negotiations at different levels of the hotel value chain. The students assume roles as hotel chain and individual hotel management and as key business clients to the individual hotels. Through strategic and tactical preparations that rely on incremental additions of information on the goals, students prepare themselves to negotiate aspects linked with B2B pricing in a hotel revenue management context and collect points based on their success. After the negotiations, they systematically reflect on their performance vis-à-vis their set intentions. The role play is targeted to be an assignment in master’s level hotel revenue management teaching, but it could be modified to target bachelor’s students or other fields in which value-based pricing is still at its infancy. Based on our experience with running the game in a classroom as well as twice remotely because of COVID-19, it is compatible with both forms of learning and could potentially be extended to an asynchronous online learning environment.

Hotel revenue management forecasting accuracy: the hidden impact of booking windows

PurposeThe pace of booking is a critical element in the accuracy of revenue management (RM) systems. Anecdotal evidence suggests that booking windows exhibit persistent shifts due to a variety of macro and micro factors. The article outlines several causes and tests the impact of the shifts on forecasting accuracy.Design/methodology/approachA novel methodological approach is utilized to empirically shift hotel reservation windows into smaller increments. Forecasts are then estimated and tested on the incremental shifts with popular RM techniques characteristic of advance booking data. A random effects model assesses the impact of the shifts on forecast accuracy.FindingsThe results show that shifts in booking behavior can cause the accuracy of forecasting models to deteriorate. The findings stress the importance of considering these shifts in model estimation and evaluation.Practical implicationsThe results demonstrate that changes in booking behavior can be detrimental to the accuracy of RM forecasting algorithms. It is recommended that revenue managers monitor booking window shifts when forecasting with advanced booking data.Originality/valueThis study is the first to systematically assess the impact of booking window shifts on forecasting accuracy. The demonstrated approach can be implemented in future research to assess model accuracy as booking behavior changes.

Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach

The paper is focusing on the problem of price optimization in the area of accommodation services. The main aim is to propose a novel simulation-based methodology of price optimization based on the customer’s price acceptance. The authors create a model based on the known approaches but extended by the stochastic approach and optimization based on the coefficient of price elasticity. The whole model is created, the price is set and optimized in two steps. The first step makes segmentation and optimization (with the price elasticity approach). The second step then sets the price of the reservation—the final price for a customer. This reservation price is mainly determined by knowledge of the length of stay, occupancy and booking lead time. All those parameters are described in the text from the economic point of view and make the base for the whole and complex revenue management model.

Dynamic pricing with demand disaggregation for hotel revenue management

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

The research-practice gap in hotel revenue management: Insights from Italy

Abstract This paper deepens the knowledge in the field of revenue management (RM) by investigating the extent to which RM practices are adopted by hotels in Italy. Specifically, using a sample of 248 Italian hotels, it aims to identify whether they actually adopt RM practices, to profile them based on the frequency/degree of usage of RM tools, and to investigate whether the organisation’s characteristics (i.e. hotel category, size, location, and chain affiliation) shape the RM practices of hotels. Findings reveal that hotels are not homogenous in their RM practices and the organisational characteristics (category, size, location, and chain affiliation) play a significant role in the RM practices of hotels in Italy. Results also highlight that a wide research-practice gap exists in the area of hotel RM. Theoretical and managerial implications, limitations and future research directions are discussed as well.

Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics

With the rising wave of travelers and changing market landscape, understanding marketplace dynamics in commoditized accommodations in the hotel industry has never been more important. In this research, a machine learning approach is applied to build a framework that can forecast the unconstrained and constrained market demand (aggregated and segmented) by leveraging data from disparate sources. Several machine learning algorithms are explored to learn traveler’s booking patterns and the latent progression of the booking curve. This solution can be leveraged by independent hoteliers in their revenue management strategy by comparing their behavior to the market.

An Intelligent Passenger Flow Prediction Method for Pricing Strategy and Hotel Operations

Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory (LSTM) to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network (ANN) and support vector regression (SVR) are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement.

PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes

A customer type discovery algorithm in hotel revenue management systems

Knowing customer types and their purchase behavior helps revenue management experts to estimate the demand and finally devise a better sales strategy to improve the revenue. Inferring customer types from sales transactions and availability data is a challenging topic in RM. In this paper, we proposed an approach to discover customer types using a classic linear ordering problem. Our linear ordering-based market discovery approach (LMD) comprises three steps: generation of an initial solution, evaluation of the solution by a choice-based model, and finally creation and addition of a new customer type. The number of different customer types is factorial in the number of alternatives and should be pruned. Here, the customer types are pruned based on observed sales and offered-sets, instead of business assumptions or applications. To evaluate the proposed method, a real publicly available dataset of five hotels is used. The results show that LMD outperforms the other available approaches in the literature and improves the log-value results of all datasets by approximately 6%.

The relationship between price paid and hotel review ratings: Expectancy-disconfirmation or placebo effect?

Whilst there is extant research focusing on the link between hotel reviews and pricing there is scant attention paid to the impact of pricing on hotel review ratings. This paper presents a large scale study based on a novel dataset of more than 44,000 guest review ratings linked to the prices paid for rooms of an European hotel group. Through applying a panel regression analysis we reveal that expectancy-disconfirmation is generally stronger than the placebo effect and higher prices have a negative effect on review ratings, not only when assessing perceived value for money but also when evaluating perceived quality. We identify the trip and guest characteristics which impact on the price-review relationship. Findings provide hotel managers with key quantitative information for simultaneously calibrating quality and revenue targets. Substantial implications include an integrated approach for segmentation as a route to better manage pricing, revenue optimization and reputation. • A unique dataset links individual prices paid with review ratings by hotel guests. • Higher prices have a negative impact on perceived value and perceived quality. • Expectancy-disconfirmation is generally stronger than the placebo effect. • Specific strategies are outlined for hotel revenue management. • Application of panel regression as an analytical tool.

Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms

The problem of maximizing the benefit from a specified number of a particular product with respect to the behavior of customer choices is regarded as revenue management. This managerial technique was first adopted by the airline industries before being widely used by many others such as hotel industries. The scope of this research is mainly focused on hotel revenue management, regarding which a bi-objective model is proposed. The suggested method aims at increasing the revenue of hotels by assigning the same rooms to different customers. Maximization of hotel revenue is a network management problem aiming to manage several resources simultaneously. Accordingly, a model is proposed in this paper based on the customer choice behavior in which the customers are divided into two groups of business and leisure. Customers of the business group prefer products with full price, whereas products with discounts are most desirable for leisure customers. The model consists of two objectives, the first one of which maximizes the means of revenue, and the second one minimizes the dispersion of revenue. Since the problem under consideration is Non-deterministic Polynomial-time hard (NP-hard), two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are proposed to solve the problem. Moreover, the tuned algorithms are compared via the statistical analysis method. The results show that the NSGA-II is more efficient in comparison with MOPSO.

Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

Prediction for canceled booking hotels is an important part of hotel revenue management systems in the modern era. Because the predicted result can be used for the optimization of hotel performance. The application of machine learning will be very helpful for predicting canceled booking hotels because machine learning can process complex data. In this research, the proposed methods are Artificial Neural Network (ANN) and Logistic Regression. Later it will be done five times experiments with hyperparameter tuning to see which method is the most optimal to do prediction canceled booking hotel. From five times experiments, experiments number five (logistic regression with GridSearchCV) is the most optimal for predicting canceled booking hotels, with 79.77% accuracy, 85.86% precision, and 55.07% recall.

A meta-analysis of hotel revenue management

This meta-analysis scrutinized 76 peer-reviewed articles regarding hotel revenue management published from 2013 to 2019. The main topics explored were pricing strategy, demand modeling and forecasting, business analysis, performance analysis and evaluation, inventory and price optimization, setting booking controls, and distribution channel management. Furthermore, articles were investigated based on their use of theory, methodology, as well as location. In the end, a comprehensive analysis of gaps in the literature and avenues for future research were provided.

Managing hotel revenue amid the COVID-19 crisis

Purpose The revenue management (RM) discipline is built on the principle of demand-based pricing. This study aims to examine how and to what extent RM can be implemented in the hotel industry during low-demand periods, particularly during the coronavirus disease 2019 (COVID-19) crisis. Design/methodology/approach This paper used semi-structured interviews to gather information from hotel RM executives, RM consultants and RM system providers. Participants were asked to think about the impact of COVID-19 on their RM practices. This paper interviewed 26 revenue executives between January and March 2020. Findings Core RM processes are still relevant during the COVID-19 crisis; however, not all components are equally important. Business analysis, pricing strategy and demand modeling and forecasting are the most critical RM processes. Inventory and price optimization and setting booking controls are not as important at this time; along with distribution channel management, these processes will become more relevant as demand picks up. Research limitations/implications Future research in this area should focus on each core RM process separately and in-depth to understand how implementation changes during the crisis and recovery periods. Future studies should also investigate how these processes operate during the recovery period. The full breadth of consequences of the COVID-19 crisis in hotel RM will likely manifest gradually. Therefore, the core RM processes should also be examined when the crisis is over. Originality/value Apart from a few studies that touched on RM-related strategies during economic downturns, to the knowledge, this is the first study to systematically examine the extent to which RM can be implemented during a crisis.

The Pricing of Meetings and Event Spaces in Hotel Revenue Management

In this paper, we discuss the pricing of Meetings and Event Spaces in Hotel Revenue Management. We use a measure called Attendee Density, for a Meeting and Event Space, which is similar to the measure of Occupancy for a hotel room. Based on the method for forecasting Occupancy for a hotel room, we forecast Attendee Density for an Event Space. The Attendee Density is the ratio of Attendees Booked to Capacity. To forecast Attendee Density, we have to forecast how many attendees will book in the future. To forecast how many attendees will book at a specific date in the future, we sum our current Reservations on Hand and a quantity called the Pickup. The Pickup is calculated based on a Booking Curve. The Booking Curve is a graph which plots Reservations on Hand against Time. It shows us how bookings happen leading upto a particular event. Once we calculate Pickup based on the Booking Curve, we can forecast how many attendees will book at a date in the future. We can then calculate Attendee Density. A high Attendee Density for the future results in charging a High Price today. On the other hand, a low future Attendee Density results in charging a Low Price currently. We also discuss how we can go about determining a High Price and a Low Price for a Meeting and Event Space.

Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures

Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.

Book Review: Kelly A. McGuire’s Hotel Pricing in a Social World

Book Review: Kelly A. McGuire’s Hotel Pricing in a Social World

Do lower costs necessarily induce higher value ratings? An analysis of online hotel reviews

ABSTRACT Combining online reviews and secondary data, this study integrates transaction costs and travel costs to examine factors shaping travellers’ perceived value of hotel stays with a focus on the moderation effects of traveller and travel characteristics. Results of a mixed-effect ordered logit model reveal negative effects of transaction costs and travel costs, and these effects vary depending on travellers’ experience quality, expertise level, and travel mode. Particularly, travellers with higher experience quality are sensitive to transaction and travel costs when rating the value of hotel stays. Visitors travelling as a couple and on business are most sensitive to transaction costs, whereas those travelling with family and friends consider travel costs a critical factor. Travellers’ sensitivity to transaction costs also intensifies as reviewer expertise increases; by contrast, only travellers with limited and moderate expertise are sensitive to travel costs. Finally, implications for hotel marketing and revenue management are discussed.

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

• Booking windows fluctuate over time changing the accuracy of revenue management pick-up techniques. • We estimate and test a variety of techniques in one period and compare changes to accuracy on data years later. • Forecast techniques exhibiting the Markov property appear to be less stable over time. • The booking curve provides valuable information for forecasting as errors were more consistent. • Neural networks provide a viable approach when forecasting with booking curves. 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.

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Abstract Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.