Schweitzer Fachinformationen
Wenn es um professionelles Wissen geht, ist Schweitzer Fachinformationen wegweisend. Kunden aus Recht und Beratung sowie Unternehmen, öffentliche Verwaltungen und Bibliotheken erhalten komplette Lösungen zum Beschaffen, Verwalten und Nutzen von digitalen und gedruckten Medien.
Today, Revenue Management is a key practice in the air transport, tourism and hotel industries. Originally known as Yield Management, Revenue Management has gradually evolved into an integral revenue optimization strategy for businesses characterized by capacity constraints and fluctuating demand.
Revenue Management in the Age of Artificial Intelligence explores, through numerous case studies and concrete examples, the principles, models and applications of Revenue Management, while addressing the ethical challenges and prospects offered by digital technology and artificial intelligence. This book is aimed at professionals, students, researchers and anyone wishing to understand the dynamics of price management in a constantly changing economic environment. It highlights the importance of transparency and fairness in maintaining consumer confidence, while demonstrating that Revenue Management is much more than a simple pricing technique: it is an essential strategic tool for many service companies.
Sourou Meatchi is Senior Lecturer in Management Sciences at the University of Angers, within the ESTHUA National Institute of Tourism, France. As a member of the Economics and Management Research Laboratory (GRANEM), his scientific research focuses on Revenue Management, the digital transformation of VSEs and SMEs, and the economics of tourism in emerging countries.
Acknowledgements xiii
Preface xv
General Introduction xvii
Chapter 1. What is Revenue Management? 1
1.1. Introduction 1
1.2. Origins and evolution of the discipline: from Yield Management to Revenue Management 2
1.3. Distinction between Revenue Management, Yield Management and pricing 4
1.3.1. Yield Management 5
1.3.2. Pricing 5
1.3.3. Revenue Management 7
1.4. General objectives of Revenue Management 9
1.4.1. Maximizing revenue 9
1.4.2. Demand management 9
1.4.3. Capacity optimization 10
1.4.4. Personalizing offers 10
1.5. Conditions of applying Revenue Management in service companies 10
1.5.1. Constrained company capacity 11
1.5.2. The perishability of supply 11
1.5.3. The level of fixed versus variable costs 11
1.5.4. The possibility of booking the service in advance 11
1.5.5. Temporal variability of demand 12
1.5.6. The possibility of segmenting demand 12
1.5.7. Price elasticity of demand from individual customers 13
1.5.8. Communication and distribution capacity 14
1.5.9. The absence of perfect competition 14
1.5.10. Demand forecasting capability 16
1.5.11. Low consumer sensitivity to pricing strategies 16
1.5.12. Organizational flexibility and operational flexibility 16
1.6. The fundamental components of Revenue Management 17
1.6.1. Price management 17
1.6.2. Capacity or inventory management 17
1.6.3. Analytics 18
1.7. The pillars of Revenue Management 18
1.7.1. Revenue Management forecasts 18
1.7.2. Capacity (inventory) and price optimization 19
1.7.3. Performance analysis and management control 19
1.8. Steps in the classic Revenue Management process 21
1.8.1. Analysis of the business environment and competition 21
1.8.2. Market and consumer behavior analysis 22
1.8.3. Demand analysis and segmentation 22
1.8.4. Qualifying and positioning the company's offering 23
1.8.5. Setting up pricing grids and capacity management 28
1.8.6. Dynamic price and capacity management 30
1.8.7. Dynamic tariff class management rules 31
1.8.8. Bottom-up price management 32
1.8.9. Overbooking 32
1.9. Software and technological tools used in Revenue Management 34
1.10. The evolution and challenges of Revenue Management 35
1.11. Revenue Management's disciplinary and scientific positioning 35
1.12. Toward a microeconomic approach to Revenue Management 37
1.13. Conclusion 38
Chapter 2. Revenue Management Models for the Tourism, Hotel and Transport Industries 41
2.1. Introduction 41
2.2. Revenue Management forecasting models 41
2.2.1. Traditional Revenue Management forecasting models 42
2.2.2. Advanced econometric models 46
2.2.3. Methods for forecasting seasonal variations 48
2.2.4. Method of unconstraining capacities 48
2.2.5. Comparative analysis of traditional forecasting methods 50
2.2.6. Modern techniques and the contribution of artificial intelligence 50
2.3. Capacity and price optimization models in Revenue Management 51
2.3.1. Probabilistic capacity and price optimization models 52
2.3.2. The bid price method 57
2.3.3. Threshold curve method 58
2.3.4. Empirical approaches to Revenue Management optimization 59
2.3.5. Overbooking models 60
2.3.6. Group management strategies 61
2.3.7. Optimizing distribution channels 62
2.4. Performance measurement and control models in Revenue Management 63
2.4.1. Occupancy rate 63
2.4.2. Revenue per available room. 63
2.4.3. Gross operating profit per available room 64
2.4.4. Average daily rate 64
2.5. Controversial Revenue Management models 64
2.5.1. Price models based on the lure effect 68
2.5.2. Models based on countdown techniques 71
2.6. Conclusion 72
Chapter 3. Revenue Management Perceptions and Consumer Behavior 75
3.1. Introduction 75
3.2. Concepts of fairness and unfairness in social relations 76
3.3. The contributions of Adams' equity theory (1965) 76
3.4. Equity theory in Revenue Management 77
3.5. Impact of perceptions of inequity on behavior 79
3.5.1. Contributions of Deutsch's model (1975) 80
3.5.2. Contributions of Oliver and Swan's model (1989) 81
3.5.3. Contribution of behavioral economics theories 82
3.5.4. Organizational justice theories 82
3.6. The quest for fairness in Revenue Management 85
3.6.1. The instrumental model 85
3.6.2. The interpersonal model 85
3.6.3. The deontic model 86
3.7. Role of product value in price judgment 87
3.8. The importance of justifying pricing policies 87
3.9. Assigning responsibility in price judgments 88
3.10. Influence of perceived opacity on prices 89
3.11. The impact of normative deviance on the perception of Revenue Management 90
3.12. The influence of perceived risk on Revenue Management perception 92
3.13. Contingency factors of perceived unfairness toward Revenue Management 92
3.13.1. Consumer-internal contingency factors 93
3.13.2. External contingency factors 95
3.14. Consequences of perceptions of unfairness toward Revenue Management 95
3.14.1. Consequences for consumer attitudes 96
3.14.2. Consequences for consumer behavior 96
3.14.3. Impact on brand image and company performance 98
3.15. The integrative model 98
3.16. Limitations of models on perceived price unfairness 99
3.17. Conclusion 101
Chapter 4. Qualitative Study of Affective Reactions to Revenue Management 103
4.1. Introduction 103
4.2. State of the art on perceived price unfairness 104
4.3. Gaps in research into consumers' affective reactions 106
4.4. Research methodology 107
4.4.1. Critical incident technique 107
4.4.2. Survey sample, transcription and preanalysis of data 107
4.4.3. Analysis techniques used 108
4.5. Research results 110
4.5.1. The multidimensionality of perceived unfairness in Revenue Management practices 110
4.5.2. Confirmation of the multidimensionality of perceived unfairness in Revenue Management 111
4.5.3. Characterization of the affective manifestations of perceived unfairness in Revenue Management 113
4.6. Clarifying indicators of perceived unfairness to Revenue Management 116
4.7. Conclusion: discussion, contributions and limitations of the study 118
Chapter 5. Measuring Perceived Unfairness in Revenue Management 121
5.1. Introduction 121
5.2. Models for measuring perceived unfairness in Revenue Management 122
5.3. Exploratory qualitative studies and identification of indices of perceived unfairness 123
5.4. Development of a scale to measure perceived unfairness in Revenue Management 124
5.4.1. Definition of the construct domain of perceived unfairness in Revenue Management 124
5.4.2. Specification of the measurement model and scale items 124
5.4.3. Exploratory factor analysis of perceived unfairness in Revenue Management 125
5.4.4. Results of the PCA of perceived unfairness in Revenue Management 126
5.4.5. Interpretation of the selected factorial axes 127
5.4.6. Confirmatory analysis of the Revenue Management perceived unfairness scale 128
5.4.7. Testing the reliability of the perceived unfairness Revenue Management scale 129
5.4.8. Measuring the validity of the Revenue Management perceived unfairness scale 129
5.5. Research discussions: contributions, limitations and avenues of research 134
5.5.1. Theoretical research contributions 134
5.5.2. The managerial contributions of research 135
5.5.3. Methodological contributions of the research 136
5.5.4. Limits of the proposed measurement model 136
5.5.5. Future research avenues 137
5.6. Conclusion 137
Chapter 6. Testing an Empirical Model of Responsible Revenue Management in the Hotel Sector 139
6.1. Introduction 139
6.2. The factors of responsible Revenue Management 140
6.2.1. Ethical issues in Revenue Management practices 140
6.2.2. Perceived price fairness 140
6.2.3. Transparent pricing information 141
6.3. Integrating ethics, fairness and transparency into Revenue Management practices 141
6.4. Testing the effects of fairness and transparency on unfairness reduction and WTP Revenue Management-based prices 143
6.4.1. Perceived injustice of Revenue Management 144
6.4.2. Willingness to pay prices resulting from Revenue Management 144
6.4.3. Direct effects of perceived fairness and transparency on reducing perceived injustice and WTP 145
6.4.4. Interaction effects of perceived justice and perceived transparency on perceived injustice and WTP 147
6.5. Research methodology 149
6.5.1. Quantitative data collection and preanalysis 149
6.5.2. Validation of measuring instruments 150
6.5.3. Justifying the choice of structural equations to test the explanatory model 151
6.6. Research results 151
6.6.1. Direct effects of perceived fairness and perceived transparency on the reduction of perceived injustice and on WTP 151
6.6.2. Interaction effects of perceived fairness and perceived transparency on reducing perceived injustice and WTP 153
6.7. Contributions, limits and avenues of research 155
6.7.1. Theoretical contributions 155
6.7.2. Managerial contributions 158
6.7.3. Research limits 159
6.7.4. Prospects and avenues for future research 160
6.8. Conclusion 161
Chapter 7. Towards Ethical and Responsible Revenue Management in the Tourism Sector 163
7.1. Introduction 163
7.2. Price fairness levers in the age of AI 164
7.2.1. Value-based pricing 164
7.2.2. Prices based on time and distance of use 165
7.3. The levers of Revenue Management transparency in the age of AI 166
7.3.1. The challenges of clear pricing information 167
7.3.2. Dynamic communication on the value of the offer 167
7.3.3. Reducing the opacity of Revenue Management-based prices 168
7.3.4. The challenges of information regarding price variation 168
7.3.5. Displaying reliable and transparent information 169
7.3.6. Displaying reference prices to guide consumers 170
7.3.7. Developing media communication on Revenue Management issues 171
7.3.8. The challenges of bottom-up pricing compared with fluctuating prices 172
7.4. The "Best Available Rate" method and its advantages 172
7.5. Price parity between distribution channels 172
7.6. Revenue Management in the mutual interest of both company and consumer 173
7.7. Bundling practices 174
7.8. Cross-selling 174
7.9. Data challenges for ethical personalization of the price offer 175
7.10. Developing a data-driven management culture 175
7.11. Compliance with European regulations on dynamic pricing 177
7.12. Conclusion 178
General Conclusion 181
References 185
Index 205
Capacity-constrained service companies (airlines, hotels, ski resorts, theme parks, etc.) are generally characterized by high fixed costs (e.g. maintenance costs for an aircraft, hotel, cruise ship and ski resort) and lower variable costs (Capiez 2003). These companies offer perishable assets and non-stockable products. Demand is generally highly fluctuating. It can vary greatly from one period to the next, from one day to the next and even from one hour to the next on the same day. A hotel may turn away customers on certain days (demand outstripping supply) and be short of customers on other days (supply outstripping demand). In addition, a hotel's sales located in a seaside town can vary by as much as double depending on the season (high season vs. low season). The occupancy rate for a train varies significantly from one day of the week to the next and even from one hour to the next. Faced with these constraints, it is difficult to apply a rigid pricing system, as is the case in the industrial and trade sectors for tangible goods. However, owing to the freedom of pricing set out in current legislation (Box 1.1), Revenue Management offers solutions enabling service companies such as airlines and hotels to cope with the specific constraints of their business sector. Revenue Management provides effective tools for adapting services and prices to demand and other parameters, such as competition, events and weather.
In the tourism sector, for example, a hotel may rent out its rooms at higher rates during periods of high demand to optimize inventory and revenue. During off-peak periods, the hotel can offer lower prices to stimulate demand. The practice of Revenue Management is therefore a crucial issue for businesses with high fixed costs, limited capacity and irregular clientele. The introduction of Revenue Management has enabled many companies to substantially increase their revenues. According to an article in the Wall Street Journal, the adoption of Revenue Management enabled Continental Airlines to increase its profits by between $50 and $100 million in the 2000s.
Except in cases where the law provides otherwise, the prices of goods and services governed by French Ordinance no. 45-1483 of June 30, 1945, prior to January 1, 1987, are freely determined by competition.
However, in sectors or areas where price competition is limited either because of monopoly situations or lasting supply difficulties or because of legislative or regulatory provisions, a decree by Conseil d'Etat1 may regulate prices after consultation with the French Competition Authority.
According to Kimes and Wirtz (2015), the beginnings of Revenue Management can be traced back to the 1950s, with Beckmann's (1958b) work in econometrics on the problem of airline seat reservations. For Capiez (2003), the first research work contributing to the emergence of Yield Management and then Revenue Management can be attributed to Rothstein, who, in 1971, proposed the first model of overbooking in air transport. Through his model, which is based on a Markov decision process, Rothstein was able to demonstrate how airlines could practice "overbooking" to anticipate the risk of "no-shows", that is, people not showing up for check-in on the day of the flight. However, Revenue Management, as we know it today, was born out of the deregulation of air transport in the United States in 1978 (Box 1.2). After the U.S. air transport market was opened up to competition, new airlines were created, making competition much more intense. Against this backdrop, major airlines such as American Airlines sought to optimize all their management levers, with a particular focus on financial revenues, as productivity gains were limited by cost rigidity (Daudel et al. 1994). The term "revenue", initially used to designate the revenue per mile of an available seat, is at the origin of the concept of Revenue Management. American Airlines pioneered the Yield Management strategy in the mid-1980s to optimize its marginal revenue through a flexible pricing system (Cross 1998)2. Yield Management enabled American Airlines to cope with the competition from charter airlines, which developed after the deregulation of the American air market. This technique also enabled the company to increase its revenues by $1.4 million over three years (Wirtz et al. 2003). The results achieved by American Airlines prompted other major airlines to follow suit and adopt Yield Management. Over time, this practice has been gradually enriched with new levers, enabling service companies to further increase their revenues. The terminology changed from Yield Management (unit Revenue Management) to Revenue Management, which encompasses Yield Management and other strategic levers, such as distribution, group management and itineraries (origin-destination), forecasting and performance control (Weatherford and Bodily 1992a; Noone et al. 2011). The transition from Yield Management to Revenue Management has been marked by the integration of technology and advanced data analysis, enabling a more dynamic and accurate approach to pricing. Revenue Management is based on demand segmentation and real-time tariff modulation with the aim of allocating the best service to the best customer at the best price at the best time (Kimes 1994). It is highly advantageous for service companies, as it constitutes a fundamental weapon for optimizing the company's overall profit (Camus et al. 2014). Recognizing the benefits of Revenue Management for airlines, other business sectors (rail transport, hotel chains, etc.) have opened up this fine management method. The practice of Revenue Management is now on the rise, suggesting that this technique will become widespread in service companies in general and in the tourism, travel and hotel industry in particular.
The Airline Deregulation Act of 1978 represents a major turning point in the history of the U.S. airline industry, bringing to an end decades of strict regulation that had been in place since the 1930s. Prior to this law, the airline market was under the control of the Civil Aeronautics Board (CAB), a government agency that regulated fares, air routes and entry requirements for airlines. While this regulation ensured a certain level of stability and security in the sector, it severely limited competition and hampered innovation (Levine 1987).
The passing of the Airline Deregulation Act under President Jimmy Carter's administration gradually removed these controls, liberalizing the air transport market (Morrison and Winston 1986). This deregulation enabled the entry of new airlines, notably low-cost carriers, which intensified competition in the industry. As a result, ticket prices fell, making air travel more accessible to the general public and stimulating significant market expansion (Borenstein 1992).
Increased competition has also catalyzed the emergence of Revenue Management. This Revenue Management model, made possible by advanced data analysis, has become standard industry practice for maximizing profitability (Phillips 2005c).
Thus, the Airline Deregulation Act not only transformed the U.S. airline industry into a more competitive and dynamic market, but also stimulated innovation in Revenue Management, permanently changing the way airlines operate (Talluri and van Ryzin 2004).
According to Talluri and van Ryzin (2004), Revenue Management can be defined as a management practice using a systematic approach to optimize overall revenue. This is achieved by setting prices and managing product availability based on demand behavior and customers' willingness to pay a given price. According to Ng et al. (2017), despite the clarity of this definition, there is still no consensus on the meaning of the term Revenue Management. Numerous other definitions exist in the literature: Weatherford and Bodily (1992a), for example, limit the meaning of Revenue Management to techniques used to determine how much inventory to make available to customers based on times in a day or days in a week. Other researchers (e.g. Kimes et al. 1999; Ng et al. 2008) adopt broader definitions of Revenue Management, including overbooking, forecasting, length-of-stay and itinerary management, customer-specific product customization and commercial risk management. Notably, there is considerable ambiguity between the terms "Yield Management" and "Revenue Management", on the one hand, and between "Revenue Management" and the term "pricing", on the other hand. An analysis of the literature on these different concepts (Yield Management, Revenue Management and pricing) and surveys of professionals have led to the definitions discussed below.
Yield Management is a component and lever of Revenue Management. The aim is to manage unit revenues by optimizing capacity allocation by fare class. In other words,...
Dateiformat: ePUBKopierschutz: Adobe-DRM (Digital Rights Management)
Systemvoraussetzungen:
Das Dateiformat ePUB ist sehr gut für Romane und Sachbücher geeignet – also für „fließenden” Text ohne komplexes Layout. Bei E-Readern oder Smartphones passt sich der Zeilen- und Seitenumbruch automatisch den kleinen Displays an. Mit Adobe-DRM wird hier ein „harter” Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.Bitte beachten Sie: Wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!
Weitere Informationen finden Sie in unserer E-Book Hilfe.
