Operationalizing Dynamic Pricing Models : Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers

Online-Ressource (XXVII, 351p. 116 illus, digital)

Includes bibliographical references

Foreword; Acknowledgements; Contents; List of Figures; List of Tables; Nomenclature; Mathematical Nomenclature; Mathematical Notation; Part I Dynamic Pricing in the Airline Industry; Chapter 1 Introduction; 1.1 The Passenger Airline Industry; 1.2 The Low Cost Revolution; 1.3 The Advent of Dynamic Pricing; Chapter 2 Motivation and Structure; 2.1 Relevance of the Topic; 2.2 Focus on the Airline Industry; 2.3 Objective and Differentiation; 2.4 Structure of Work; Chapter 3 Dynamic Pricing; 3.1 Definition and Scope; 3.1.1 Introduction to Pricing; 3.1.2 Dynamic Pricing and Revenue Optimization

3.2 Literature Overview3.2.1 Demand Learning Models; 3.2.2 Non-learning Pricing Models; 3.3 Limitations and Shortcomings; 3.3.1 Dynamic Pricing Models; 3.3.2 Demand Learning Models; 3.4 Proposed Approach; Part II Forecasting Latent Demand; Part II Objective; Chapter 4 Self-Learning Linear Models; 4.1 Linear Regression Models; 4.2 Bayesian Statistics; 4.2.1 Bayesian Probabilities; 4.2.2 Bayesian Inference; 4.3 Bayesian Linear Regression; 4.3.1 Parameter Distribution; 4.3.2 Predictive Distribution; 4.4 Critique and Limitations; Chapter 5 Demand in Low Cost Markets; 5.1 Experimental Data Set

5.1.1 Data Collection5.1.2 Data Cleansing; 5.2 Overarching Long-term Characteristics; 5.2.1 Log-linear Demand Structure; 5.2.2 Macro-Seasonalities and Trends; 5.2.3 Similarities of Adjacent Flights; 5.3 Short-term Characteristics; 5.3.1 Time Series Disruption Through Outliers; 5.3.2 Patterns Based on Departure Weekdays; 5.3.3 Micro-Seasonalities along ObservationWeekdays; 5.3.4 Cross-Effects of Departure and ObservationWeek-days; 5.4 Implications for Forecasting Model; Chapter 6 The Demand Forecasting Model; 6.1 Linear Basis Function Model; 6.1.1 Indexing and Data Transformation

6.1.2 Driving Model Parameters6.1.3 Model Specification and Re-transformation; 6.1.4 Frequentist Coefficient Weights; 6.2 Model Validation; 6.2.1 Model and Coefficient Significance; 6.2.2 Prerequisites and Assumptions; 6.3 Bayesian Learning Mechanism; 6.3.1 Online Demand Learning; 6.3.2 Overarching Demand Structures and Prior De-mand Knowledge; Chapter 7 Computational Results and Evaluation; 7.1 Performance of the Na¨ive Bayesian Scheme; 7.1.1 Distribution Convergence Speed; 7.1.2 Forecast Quality and Accuracy; 7.2 Sensitivity of Forecast Accuracy; 7.2.1 Improvement Through Informed Priors

7.2.2 Sizing of Learning window7.2.3 Granularity of Forecasting Basis; 7.2.4 Combined Effects; 7.3 Recommended Approach; Chapter 8 Summary and Outlook; Part III Estimating Price Sensitivity; Part III Objective; Chapter 9 Discrete Customer Choice Analysis; 9.1 Fundamentals of Choice Modeling; 9.2 Elements of a Choice Decision Process; 9.2.1 Decision Maker and its Characteristics; 9.2.2 Choice Set; 9.2.3 Alternative Attributes; 9.2.4 Decision Rule; 9.3 Individual Choice Behavior; 9.3.1 Economic Utility-based Consumer Theory; 9.3.2 Deterministic Choice Theory; 9.3.3 Probabilistic Choice Theory

9.4 The Multinomial Logit Model

10.1007/978-3-8349-6184-6 [DOI]

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