Christ Operationalizing Dynamic Pricing Models

Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers
2011
ISBN: 978-3-8349-6184-6
Verlag: Betriebswirtschaftlicher Verlag Gabler
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 351 Seiten, eBook

Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.

Dr. Steffen Christ wrote his dissertation at the Institute of Statistics and Mathematical Economics of Prof. Dr. Robert Klein at the University of Augsburg.

Zielgruppe

Research

Autoren/Hrsg.

Christ, Steffen

Inhaltsverzeichnis

1;Foreword;7
2;Acknowledgements;9
3;Contents;10
4;List of Figures;14
5;List of Tables;19
6;Nomenclature;21
7;Mathematical Nomenclature;23
8;Mathematical Notation;25
9;Part I Dynamic Pricing in the Airline Industry;26
9.1;Chapter 1 Introduction;27
9.1.1;1.1 The Passenger Airline Industry;27
9.1.2;1.2 The Low Cost Revolution;30
9.1.3;1.3 The Advent of Dynamic Pricing;35
9.2;Chapter 2 Motivation and Structure;38
9.2.1;2.1 Relevance of the Topic;38
9.2.2;2.2 Focus on the Airline Industry;41
9.2.3;2.3 Objective and Differentiation;42
9.2.4;2.4 Structure of Work;43
9.3;Chapter 3 Dynamic Pricing;45
9.3.1;3.1 Definition and Scope;45
9.3.1.1;3.1.1 Introduction to Pricing;45
9.3.1.2;3.1.2 Dynamic Pricing and Revenue Optimization;47
9.3.2;3.2 Literature Overview;53
9.3.2.1;3.2.1 Demand Learning Models;53
9.3.2.2;3.2.2 Non-learning Pricing Models;64
9.3.3;3.3 Limitations and Shortcomings;76
9.3.3.1;3.3.1 Dynamic Pricing Models;76
9.3.3.2;3.3.2 Demand Learning Models;78
9.3.4;3.4 Proposed Approach;80
10;Part II Forecasting Latent Demand;85
10.1;Part II Objective;86
10.2;Chapter 4 Self-Learning Linear Models;88
10.2.1;4.1 Linear Regression Models;89
10.2.2;4.2 Bayesian Statistics;100
10.2.2.1;4.2.1 Bayesian Probabilities;101
10.2.2.2;4.2.2 Bayesian Inference;104
10.2.3;4.3 Bayesian Linear Regression;106
10.2.3.1;4.3.1 Parameter Distribution;106
10.2.3.2;4.3.2 Predictive Distribution;110
10.2.4;4.4 Critique and Limitations;113
10.3;Chapter 5 Demand in Low Cost Markets;117
10.3.1;5.1 Experimental Data Set;117
10.3.1.1;5.1.1 Data Collection;118
10.3.1.2;5.1.2 Data Cleansing;121
10.3.2;5.2 Overarching Long-term Characteristics;123
10.3.2.1;5.2.1 Log-linear Demand Structure;124
10.3.2.2;5.2.2 Macro-Seasonalities and Trends;130
10.3.2.3;5.2.3 Similarities of Adjacent Flights;133
10.3.3;5.3 Short-term Characteristics;135
10.3.3.1;5.3.1 Time Series Disruption Through Outliers;136
10.3.3.2;5.3.2 Patterns Based on Departure Weekdays;141
10.3.3.3;5.3.3 Micro-Seasonalities along ObservationWeekdays;145
10.3.3.4;5.3.4 Cross-Effects of Departure and ObservationWeek-days;148
10.3.4;5.4 Implications for Forecasting Model;149
10.4;Chapter 6 The Demand Forecasting Model;151
10.4.1;6.1 Linear Basis Function Model;151
10.4.1.1;6.1.1 Indexing and Data Transformation;152
10.4.1.2;6.1.2 Driving Model Parameters;154
10.4.1.3;6.1.3 Model Specification and Re-transformation;158
10.4.1.4;6.1.4 Frequentist Coefficient Weights;160
10.4.2;6.2 Model Validation;161
10.4.2.1;6.2.1 Model and Coefficient Significance;162
10.4.2.2;6.2.2 Prerequisites and Assumptions;164
10.4.3;6.3 Bayesian Learning Mechanism;166
10.4.3.1;6.3.1 Online Demand Learning;167
10.4.3.2;6.3.2 Overarching Demand Structures and Prior De-mand Knowledge;173
10.5;Chapter 7 Computational Results and Evaluation;178
10.5.1;7.1 Performance of the Na¨ive Bayesian Scheme;178
10.5.1.1;7.1.1 Distribution Convergence Speed;178
10.5.1.2;7.1.2 Forecast Quality and Accuracy;183
10.5.2;7.2 Sensitivity of Forecast Accuracy;187
10.5.2.1;7.2.1 Improvement Through Informed Priors;188
10.5.2.2;7.2.2 Sizing of Learning window;191
10.5.2.3;7.2.3 Granularity of Forecasting Basis;197
10.5.2.4;7.2.4 Combined Effects;200
10.5.3;7.3 Recommended Approach;204
10.6;Chapter 8 Summary and Outlook;207
11;Part III Estimating Price Sensitivity;216
11.1;Part III Objective;217
11.2;Chapter 9 Discrete Customer Choice Analysis;219
11.2.1;9.1 Fundamentals of Choice Modeling;220
11.2.2;9.2 Elements of a Choice Decision Process;222
11.2.2.1;9.2.1 Decision Maker and its Characteristics;223
11.2.2.2;9.2.2 Choice Set;224
11.2.2.3;9.2.3 Alternative Attributes;225
11.2.2.4;9.2.4 Decision Rule;226
11.2.3;9.3 Individual Choice Behavior;227
11.2.3.1;9.3.1 Economic Utility-based Consumer Theory;227
11.2.3.2;9.3.2 Deterministic Choice Theory;229
11.2.3.3;9.3.3 Probabilistic Choice Theory;231
11.2.4;9.4 The Multinomial Logit Model;233
11.2.4.1;9.4.1 Description and Functional Form;234
11.2.4.2;9.4.2 Specific Properties and Limitations;236
11.2.4.3;9.4.3 Coefficient Estimation;240
11.2.4.4;9.4.4 Tests of Model Specifications;242
11.3;Chapter 10 Choice Situation in Low-Cost Markets;248
11.3.1;10.1 Experimental Data Set;248
11.3.2;10.2 Market Overview;253
11.3.2.1;10.2.1 Market Participants and Supply;253
11.3.2.2;10.2.2 Pricing Environment and Behavior;254
11.3.3;10.3 Observed Demand Behavior;258
11.3.3.1;10.3.1 Price Sensitivity;258
11.3.3.2;10.3.2 Schedule Preference;262
11.3.3.3;10.3.3 Booking Day Preference;264
11.3.4;10.4 Implications for Choice Model;265
11.4;Chapter 11 Multinomial Logit Model for Low-Cost Travel Choice;268
11.4.1;11.1 Modeling Constraints and Specifics;270
11.4.2;11.2 Model Building and Goodness of Fit;276
11.4.2.1;11.2.1 Internal Choice Drivers;277
11.4.2.2;11.2.2 Decision Maker Characteristics;283
11.4.2.3;11.2.3 External Outbound Choice Drivers;293
11.4.2.4;11.2.4 External Inbound Choice Drivers;309
11.5;Chapter 12 Computational Results and Evaluation;317
11.5.1;12.1 Predictive Model Performance;317
11.5.2;12.2 Choice Elasticities of Fare Changes;325
11.5.3;12.3 Applications to Dynamic Airfare Pric-ing;330
11.6;Chapter 13 Summary and Outlook;333
12;Appendix and Bibliography;341
12.1;Appendix;342
12.2;Bibliography;343

Über Autor(innen)

Dr. Steffen Christ wrote his dissertation at the Institute of Statistics and Mathematical Economics of Prof. Dr. Robert Klein at the University of Augsburg.