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

Take control of revenue management in the new hotel economy Hotel Pricing in a Social World: How to Drive Value in the New Hotel Economy is an insightful resource that provides guidance on improving organizational decision making to keep your hotel relevant, from a pricing standpoint, in the often chaotic hotel landscape. This groundbreaking book clearly showcases the current environment of the hotel industry, and describes new and emerging trends that can impact your revenue management tactics. This essential text prepares you to survive and thrive in today's highly competitive market, and outlines the best approach to building profitable pricing strategies that follow both tactical and strategic best practices. Revenue management has become a key activity in the highly social environment of today's hotel industry, thanks to mobile technology and social media. Though relatively new, revenue management is a quickly-evolving discipline that requires precision if you want to maintain your hotel's relevance in the market. Leverage original research, case studies, and industry examples to understand the practical application of key concepts Explore current market conditions that have an impact on revenue management Consider how advances in data management, analytics, and data visualization can impact revenue management practices Identify how revenue management can help you take advantage of market opportunities and overcome challenges Hotel Pricing in a Social World: How to Drive Value in the New Hotel Economy is an essential text for hotel CFOs, CMOs, revenue managers, and operations managers who want to leverage revenue management techniques to keep their hotel competitive.

PurposeThis study aims to examine the impact of a mega-sporting event “2014 FIFA World Cup” on hotel pricing strategies and performance.Design/methodology/approachThe present project examines the host regions’ response to the 2014 FIFA World Cup which was established by the variance in the main hotel key performance indexes: occupancy, average daily rate, revenue per available room (RevPAR) and supply. Using data gathered from STR, this research distinctly shows how the Brazilian host regions reacted to the World Cup.FindingsResults suggest that the key performance indicators of Brazil’s lodging sector reacted differently to the World Cup. Although all hosting cities experienced significant RevPAR growth because of the increase in hotel room rates during the event, the supply and occupancy performed differed from each city.Research limitations/implicationsResearch is limited to the case of hotel performance at the country level for mega-events. The study focused on the reaction of revenue managers in the Latin America context. Other contexts may generate different results.Practical implicationsThe study helps revenue managers to examine how the FIFA World Cup travel demand affected pricing strategies and revenue management practices in the Brazilian hotel sector in areas undergoing seasonal growths in overnight tourism. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.Social implicationsThis study reveals that the benefits brought by a mega-event are not always translated into strong hotel revenue performance. This study highlights an important but understudied research area of revenue management pricing strategies and the effect of mega-sporting events in the hotel sector. This study contributes to the literature as one of the few investigations to benefit hotel pricing strategies and overall revenue performance.Originality/valueThis study is one of the few studies about exploring the reaction of revenue managers during the execution of a mega-sporting event. The value of the present study lies in the fact that the authors extend previous studies examining the impact of the most important sporting event in the hotel industry at the country-level perspective. This study serves to inform hoteliers and practitioners about revenue management pricing strategies to improve hotel performance during mega-sporting events.

Purpose The purpose of this study is to explore the relationship between the real time requirements of guests and efforts made by hoteliers to optimize revenue and guest service excellence. Design/methodology/approach Prior studies have demonstrated the impact of room rates on profitability. The majority of these studies assumed an algebraic relationship between room rates and room demand to obtain the optimal solution by applying calculus to the revenue or profit function. This study adopts an alternative approach by seeking to better understanding the ever changing needs of hotel guests and how their behavior patterns have evolved over time together with innovative approaches that revenue managers need to adopt to optimize revenue. In today’s environment, revenue management has evolved into a more holistic approach and the study assimilates information from senior professionals from a range of hotels in India based on semi-structured interviews. Findings Revenue management is no longer about forecasting supply and demand based on historical data. This paper yields insights on emerging areas of importance for revenue management and advocates a systematic approach that hoteliers can adopt and apply to every department to secure a bigger impact on revenue management. Practical implications The findings can be used by hoteliers to fine-tune the room rates determined by conventional methods to arrive at a realistic and definitive value for optimal room rates. Originality/value This study highlights the issues that arise from viewing revenue management in isolation and it also considers innovative, customer-focused ways to optimize revenues.

Chapter 1: Introduction to Revenue Management. Chapter 2: Strategic Pricing. Chapter 3: Value. Chapter 4: Differential Pricing. Chapter 5: The Revenue Manager's Role. Chapter 6: Forecasting Demand. Chapter 7: Inventory and Price Management. Chapter 8: Distribution Channel Management. Chapter 9: Evaluation of Revenue Management Efforts in Lodging. Chapter 10: Revenue Management for Food and Beverage Services. Chapter 11: Evaluation of Revenue Management Efforts in Food and Beverage Services. Chapter 12: Specialized Applications of Revenue Management. Chapter 13: Building Better Business.

Learning outcomes The learning outcomes of this study are to recall the fundamental concept of revenue management in the hotel industry (remembering); explain the various performance measures used to evaluate room revenue in hotels (understanding); use revenue management strategies to improve room revenue in hotel operations (applying); and examine and evaluate the optimal solution for revenue enhancement, considering factors such as capacity management, duration control and differential pricing (analyzing). Case overview/synopsis This case study delves into the challenges faced by Hotel King’s Cross, a business hotel located in Pune, Maharashtra, in the year 2022. A week before Christmas Eve, Soham Dande, the hotel’s revenue manager, sought a meeting with Rohan Chopra, the director of sales and marketing, to discuss “revenue optimization for the hotel.” During their meeting, Dande mentioned that the hotel had fallen behind its budgeted room sales targets for 2022 across various metrics, such as room booking nights, occupancy percentage, average room rate and revenue per available room. Furthermore, the hotel was trailing behind its competitors. The situation was compounded by the management’s decision to raise the targets for 2023 by 5%–7%, factoring in upcoming events, competitive performance and pandemic-related losses over the past two years. Chopra faced the dilemma of formulating an action plan to achieve the ambitious 2023 targets and establish Hotel King’s Cross as a market leader. Complexity academic level Students undertaking executive development programs and graduate-level courses in non-profit hospitality and tourism management, as well as revenue management courses in the executive MBA, management development and graduate MBA programs, may all benefit from this case study. Supplementary materials Teaching notes are available for educators only. Subject code CSS12: Tourism and hospitality.

Since the Lehman's crash in 2008 anecdotal evidence from the Revenue Management Society in the United Kingdom suggests that consumer buying behaviour has changed significantly. Consumers are buying different things, at different times and through different channels. As a result forecast accuracy is poor and many companies are turning off the automated forecasting systems and relying on analysts to predict booking behaviour. Historical data alone cannot be used to predict future sales in times of flux such as these and drawing together a wider range of internal and external data would help improve forecast accuracy. The impact on revenue managers varies widely. Where sales forecasts are important, adjustments to revenue management system outputs are needed to retain credibility and stop systems ‘panicking’. When customer behaviour is volatile and poorly understood, there may be a need for novel selling mechanisms such as auctions, or online experimentation to probe the market response.

YM is characterized as integrating the techniques, formulas, and strategies used to manage inventory, pricing, and sales policies. When linked to the hotel and tourism sector, YM manages availability, accommodation control, and discounts applied to overnight stays. RM is the evolution of YM, where using the full potentiality of the elements allows for maximizing revenue in periods of high demand and maximizing demand in periods of low revenue. RM is based on demand forecasting, price and inventory management, distribution channels, and revenue management performance. The world of hotel distribution is very complex and there is no way to control it. The Revenue Manager must be vigilant for the large price disparities in the various distribution channels. The distribution channels allow the hotel to reach a wider market share and an increase its competitive range. The Crowne Plaza Porto has a wide network of distribution channels represented by direct bookings and contracted distributors: Direct reservations come from four sources: Central Reservations Office (CRO), direct contact with the reservations department, the IHG brand website, and the mobile app. This research presents a qualitative methodology, applied to the Hotel Crowne Plaza Porto, whose purpose is to identify the segmentation introduced by two main segments: Individuals and Groups. The results of this research suggest that each of these segments is divided into several subgroups according to the specificity of their characteristics, and adequately identified, allowing the hotel's Revenue Manager to apply different strategies to the different customer groups, equally justified.

Travel and tourism arranged at the “last minute” has become a growing trend among consumers today. Responding to this trend, more and more travel/tourism firms are offering services catering to consumers seeking last-minute travel and tourism opportunities. Yet, clearly, not all last-minute travellers are alike. Variability in last-minute consumer characteristics such as motivations, lifestyles, and benefits sought suggests opportunities for astute marketers and revenue managers to collaborate in further segmenting the last-minute market and developing more effective marketing and revenue management strategies to potentially increase the firms' revenues, profitability, and customer satisfaction. Toward this end, this article evaluates and discusses marketing strategies, marketing mix elements, and segmentation approaches that may be most appropriate for more effective marketing and revenue management in the last-minute travel and tourism market.

Revenue management is the art and science of making the right product or service available to the right customer at the right time through the right channel at right price. Dynamic pricing plays a crucial role in the implementation of revenue management in passenger airline reservation system. The liberalization of domestic aviation sector in countries such as India has seen many new market entrants resulting in higher competition while setting the flight fares. The variation in flight fares of Delhi – Mumbai passenger airline sector is studied for a departure date based on the number of days in advance the booking is made. Descriptive and inferential statistical analyses of the fares reveal the impact of airlines, booking channels and departure time windows on the pricing decisions of flight fares. The analysis framework of this study could be used as a basis for a continuous tracking study of flight fares by airline revenue managers to help them arrive at the right fare for each fare class of a flight.

Trust of products, services and organizations is not a subject that people regularly think about. Indeed, trust may sound like some “faddish” or “fuzzy” concept to Revenue Management professionals but a significant amount of research has indicated that profit depends on it to a surprisingly large extent. It is something that can really make a marked difference when it comes to establishing, building and maintaining healthy buyer-seller relationships. What is clear is that in today’s society, characterized by widespread consumer distrust of companies and public bodies, revenue managers need to work hard at developing a trust-based relationship with their customers.

In airline revenue management, after differentiating products on each itinerary according to various restrictions, management needs to set the prices for the products. A deterministic joint pricing and seat allocation model is proposed. It is reduced to a separable concave programming problem and thus readily solvable. Focusing on a special hub-to-hub airline network, monotonicity of the pricing decisions is explored. Using a nonlinear primal–dual technique, it is shown that some itineraries’ optimal prices are decreasing, while some other itineraries’ optimal prices are increasing with a capacity change in either a side leg or the middle leg. Such structural properties add important managerial insights into the pricing model for revenue managers in an airline company.

Dynamic cruise ship revenue management

Asymmetric preference in hotel room choice and implications on revenue management

This paper discusses future trends in revenue management (RM) in the hotel industry based on interviews with industry leaders. The integration of customer behaviour and preferences into marketing and pricing strategies appears to be a major future trend. Examples of this trend are the increased attention given to the analysis of total customer value both in the short and long term. Group RM is also receiving considerable attention from hoteliers. Many of the participants interviewed identified a need to improve the accuracy of forecasting, given the increased complexity of group business. To reap the benefits of these new strategies, companies will need to invest in technological as well as human support. In the past, RM has been driven by the search for technological solutions to improve RM techniques. In the future, it is foreseen that the customer will be the driving force of all RM efforts.

In recent years, the cruise line industry has become an exciting growth category in the leisure travel market. Like airlines and hotels, it reports all characteristics of revenue management (RM). Although RM has attracted widespread research interest in airline and hotel contexts, studies of cruise line revenue management are very limited. Using data from a major North American cruise company, we apply a variety of (24) forecasting methods, which are divided into three categories (non-pickup methods, classical pickup (CP) methods and advanced pickup (AP) methods), to generate forecasts of final bookings for the cruises that have not yet departed at a particular reading point. We use a two-stage framework to test alternative forecasting methods and compare their performance. We found the performance of multiplicative methods to be significantly worse. Among the additive methods, we find that classical methods perform the best, followed by AP and non-pickup methods. All CP methods with the exception of exponential smoothing with trend perform fairly well. Among AP methods, Autoregressive integrated moving average, linear regression and moving average (MA) produce the most accurate forecasts. Within non-pickup methods, MA is the most effective method.