Revenue Management in the Age of Artificial Intelligence

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 xiiiPreface xvGeneral Introduction xviiChapter 1 What is Revenue Management? 11.1 Introduction 11.2 Origins and evolution of the discipline: from Yield Management to Revenue Management 21.3 Distinction between Revenue Management, Yield Management and pricing 41.3.1 Yield Management 51.3.2 Pricing 51.3.3 Revenue Management 71.4 General objectives of Revenue Management 91.4.1 Maximizing revenue 91.4.2 Demand management 91.4.3 Capacity optimization 101.4.4 Personalizing offers 101.5 Conditions of applying Revenue Management in service companies 101.5.1 Constrained company capacity 111.5.2 The perishability of supply 111.5.3 The level of fixed versus variable costs 111.5.4 The possibility of booking the service in advance 111.5.5 Temporal variability of demand 121.5.6 The possibility of segmenting demand 121.5.7 Price elasticity of demand from individual customers 131.5.8 Communication and distribution capacity 141.5.9 The absence of perfect competition 141.5.10 Demand forecasting capability 161.5.11 Low consumer sensitivity to pricing strategies 161.5.12 Organizational flexibility and operational flexibility 161.6 The fundamental components of Revenue Management 171.6.1 Price management 171.6.2 Capacity or inventory management 171.6.3 Analytics 181.7 The pillars of Revenue Management 181.7.1 Revenue Management forecasts 181.7.2 Capacity (inventory) and price optimization 191.7.3 Performance analysis and management control 191.8 Steps in the classic Revenue Management process 211.8.1 Analysis of the business environment and competition 211.8.2 Market and consumer behavior analysis 221.8.3 Demand analysis and segmentation 221.8.4 Qualifying and positioning the company’s offering 231.8.5 Setting up pricing grids and capacity management 281.8.6 Dynamic price and capacity management 301.8.7 Dynamic tariff class management rules 311.8.8 Bottom-up price management 321.8.9 Overbooking 321.9 Software and technological tools used in Revenue Management 341.10 The evolution and challenges of Revenue Management 351.11 Revenue Management’s disciplinary and scientific positioning 351.12. Toward a microeconomic approach to Revenue Management .. 371.13 Conclusion 38Chapter 2 Revenue Management Models for the Tourism, Hotel and Transport Industries 412.1 Introduction 412.2 Revenue Management forecasting models 412.2.1 Traditional Revenue Management forecasting models 422.2.2 Advanced econometric models 462.2.3 Methods for forecasting seasonal variations 482.2.4 Method of unconstraining capacities 482.2.5 Comparative analysis of traditional forecasting methods 502.2.6 Modern techniques and the contribution of artificial intelligence 502.3 Capacity and price optimization models in Revenue Management 512.3.1 Probabilistic capacity and price optimization models 522.3.2 The bid price method 572.3.3 Threshold curve method 582.3.4 Empirical approaches to Revenue Management optimization 592.3.5 Overbooking models 602.3.6 Group management strategies 612.3.7 Optimizing distribution channels 622.4 Performance measurement and control models in Revenue Management 632.4.1 Occupancy rate 632.4.2 Revenue per available room 632.4.3 Gross operating profit per available room 642.4.4 Average daily rate 642.5 Controversial Revenue Management models 642.5.1 Price models based on the lure effect 682.5.2 Models based on countdown techniques 712.6 Conclusion 72Chapter 3 Revenue Management Perceptions and Consumer Behavior 753.1 Introduction 753.2 Concepts of fairness and unfairness in social relations 763.3 The contributions of Adams’ equity theory (1965) 763.4 Equity theory in Revenue Management 773.5 Impact of perceptions of inequity on behavior 793.5.1 Contributions of Deutsch’s model (1975) 803.5.2 Contributions of Oliver and Swan’s model (1989) 813.5.3 Contribution of behavioral economics theories 823.5.4 Organizational justice theories 823.6 The quest for fairness in Revenue Management 853.6.1 The instrumental model 853.6.2 The interpersonal model 853.6.3 The deontic model 863.7 Role of product value in price judgment 873.8 The importance of justifying pricing policies 873.9 Assigning responsibility in price judgments 883.10 Influence of perceived opacity on prices 893.11 The impact of normative deviance on the perception of Revenue Management 903.12 The influence of perceived risk on Revenue Management perception 923.13 Contingency factors of perceived unfairness toward Revenue Management 923.13.1 Consumer-internal contingency factors 933.13.2 External contingency factors 953.14 Consequences of perceptions of unfairness toward Revenue Management 953.14.1 Consequences for consumer attitudes 963.14.2 Consequences for consumer behavior 963.14.3 Impact on brand image and company performance 983.15 The integrative model 983.16 Limitations of models on perceived price unfairness 993.17 Conclusion 101Chapter 4 Qualitative Study of Affective Reactions to Revenue Management 1034.1 Introduction 1034.2 State of the art on perceived price unfairness 1044.3 Gaps in research into consumers’ affective reactions 1064.4 Research methodology 1074.4.1 Critical incident technique 1074.4.2 Survey sample, transcription and preanalysis of data 1074.4.3 Analysis techniques used 1084.5 Research results 1104.5.1 The multidimensionality of perceived unfairness in Revenue Management practices 1104.5.2 Confirmation of the multidimensionality of perceived unfairness in Revenue Management 1114.5.3 Characterization of the affective manifestations of perceived unfairness in Revenue Management 1134.6 Clarifying indicators of perceived unfairness to Revenue Management 1164.7 Conclusion: discussion, contributions and limitations of the study 118Chapter 5 Measuring Perceived Unfairness in Revenue Management 1215.1 Introduction 1215.2 Models for measuring perceived unfairness in Revenue Management 1225.3 Exploratory qualitative studies and identification of indices of perceived unfairness 1235.4 Development of a scale to measure perceived unfairness in Revenue Management 1245.4.1 Definition of the construct domain of perceived unfairness in Revenue Management 1245.4.2 Specification of the measurement model and scale items 1245.4.3 Exploratory factor analysis of perceived unfairness in Revenue Management 1255.4.4 Results of the PCA of perceived unfairness in Revenue Management 1265.4.5 Interpretation of the selected factorial axes 1275.4.6 Confirmatory analysis of the Revenue Management perceived unfairness scale 1285.4.7 Testing the reliability of the perceived unfairness Revenue Management scale 1295.4.8 Measuring the validity of the Revenue Management perceived unfairness scale 1295.5 Research discussions: contributions, limitations and avenues of research 1345.5.1 Theoretical research contributions 1345.5.2 The managerial contributions of research 1355.5.3 Methodological contributions of the research 1365.5.4 Limits of the proposed measurement model 1365.5.5 Future research avenues 1375.6 Conclusion 137Chapter 6 Testing an Empirical Model of Responsible Revenue Management in the Hotel Sector 1396.1 Introduction 1396.2 The factors of responsible Revenue Management 1406.2.1 Ethical issues in Revenue Management practices 1406.2.2 Perceived price fairness 1406.2.3 Transparent pricing information 1416.3 Integrating ethics, fairness and transparency into Revenue Management practices 1416.4 Testing the effects of fairness and transparency on unfairness reduction and WTP Revenue Management-based prices 1436.4.1 Perceived injustice of Revenue Management 1446.4.2 Willingness to pay prices resulting from Revenue Management 1446.4.3 Direct effects of perceived fairness and transparency on reducing perceived injustice and WTP 1456.4.4 Interaction effects of perceived justice and perceived transparency on perceived injustice and WTP 1476.5 Research methodology 1496.5.1 Quantitative data collection and preanalysis 1496.5.2 Validation of measuring instruments 1506.5.3 Justifying the choice of structural equations to test the explanatory model 1516.6 Research results 1516.6.1 Direct effects of perceived fairness and perceived transparency on the reduction of perceived injustice and on WTP 1516.6.2 Interaction effects of perceived fairness and perceived transparency on reducing perceived injustice and WTP 1536.7 Contributions, limits and avenues of research 1556.7.1 Theoretical contributions 1556.7.2 Managerial contributions 1586.7.3 Research limits 1596.7.4 Prospects and avenues for future research 1606.8 Conclusion 161Chapter 7 Towards Ethical and Responsible Revenue Management in the Tourism Sector 1637.1 Introduction 1637.2 Price fairness levers in the age of AI 1647.2.1 Value-based pricing 1647.2.2 Prices based on time and distance of use 1657.3 The levers of Revenue Management transparency in the age of AI 1667.3.1 The challenges of clear pricing information 1677.3.2 Dynamic communication on the value of the offer 1677.3.3 Reducing the opacity of Revenue Management-based prices 1687.3.4 The challenges of information regarding price variation 1687.3.5 Displaying reliable and transparent information 1697.3.6 Displaying reference prices to guide consumers 1707.3.7 Developing media communication on Revenue Management issues 1717.3.8 The challenges of bottom-up pricing compared with fluctuating prices 1727.4 The “Best Available Rate” method and its advantages 1727.5 Price parity between distribution channels 1727.6 Revenue Management in the mutual interest of both company and consumer 1737.7 Bundling practices 1747.8 Cross-selling 1747.9 Data challenges for ethical personalization of the price offer 1757.10 Developing a data-driven management culture 1757.11 Compliance with European regulations on dynamic pricing 1777.12 Conclusion 178General Conclusion 181References 185Index 205