Pricing and Revenue Optimization
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This book offers the first introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries.
But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates.
In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. With updates to every chapter, this second edition covers topics such as estimation of price-response functions and machine-learning-based price optimization. New discussions of applications of dynamic pricing and revenue management by companies such as Amazon, Uber, and Disney, and in industries such as sports, theater, and electric power, are also included.
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- Stanford University Press
- 9781503614260
- 9781503610002
- ePub
- 2
- Robert L. Phillips
- English
- 2021-05-18
- 100
- 10
- 10
Kaflar
- Cover
- Title Page
- Copyright
- Contents
- Preface to the Second Edition
- Chapter 1: Background
- 1.1 Historical Background and Context
- 1.2 the Financial Impact of Pricing and Revenue Optimization
- 1.3 Organization of the Book
- 1.4 Further Reading
- Chapter 2: Introduction to Pricing and Revenue Optimization
- 2.1 the Challenges of Pricing
- 2.2 Traditional Approaches to Pricing
- 2.3 the Scope of Pricing and Revenue Optimization
- 2.4 the Pricing and Revenue Optimization Process
- 2.5 Summary
- 2.6 Further Reading
- 2.7 Exercise
- Chapter 3: Models of Demand
- 3.1 the Price-Response Function
- 3.2 Measures of Price Sensitivity
- 3.3 Common Price-Response Functions
- 3.4 Summary
- 3.5 Further Reading
- 3.6 Exercise
- Chapter 4: Estimating Price Response
- 4.1 Data Sources for Price-Response Estimation
- 4.2 Price-Response Estimation Using Historical Data
- 4.3 the Estimation Process
- 4.4 Challenges in Estimation
- 4.5 Updating the Estimates
- 4.6 Data-Free Approaches to Estimation
- 4.7 Summary
- 4.8 Further Reading
- 4.9 Exercises
- Chapter 5: Optimization
- 5.1 Elements of Contribution
- 5.2 the Basic Price Optimization Problem
- 5.3 Existence and Uniqueness of Optimal Prices
- 5.4 Optimization with Multiple Prices
- 5.5 a Data-Driven approach to Price Optimization
- 5.6 Competitive Response and Optimization
- 5.7 Optimization with Multiple Objective Functions
- 5.8 Summary
- 5.9 Further Reading
- 5.10 Exercises
- Chapter 6: Price Differentiation
- 6.1 the Economics of Price Differentiation
- 6.2 Limits to Price Differentiation
- 6.3 Tactics for Price Differentiation
- 6.4 Calculating Differentiated Prices
- 6.5 Price Differentiation and Consumer Welfare
- 6.6 Nonlinear Pricing
- 6.7 Summary
- 6.8 Further Reading
- 6.9 Exercises
- Chapter 7: Pricing with Constrained Supply
- 7.1 the Nature of Supply Constraints
- 7.2 Optimal Pricing with a Supply Constraint
- 7.3 Opportunity Cost
- 7.4 Market Segmentation and Supply Constraints
- 7.5 Variable Pricing
- 7.6 Variable Pricing in Action
- 7.7 Summary
- 7.8 Further Reading
- 7.9 Exercises
- Chapter 8: Revenue Management
- 8.1 History
- 8.2 Levels of Revenue Management
- 8.3 Revenue Management Strategy
- 8.4 the System Context
- 8.5 Booking Control
- 8.6 Tactical Revenue Management
- 8.7 Revenue Management Metrics
- 8.8 incremental Costs and Ancillary Revenue in Revenue Management
- 8.9 Revenue Management in Action
- 8.10 Summary
- 8.11 Further Reading
- 8.12 Exercise
- Chapter 9: Capacity Allocation
- 9.1 the Two-Class Problems
- 9.2 Capacity Allocation with Multiple Fare Classes
- 9.3 Capacity Allocation with Dependent Demands
- 9.4 a Data-Driven approach to Capacity Control
- 9.5 Capacity Allocation in Action
- 9.6 Measuring Capacity Allocation Effectiveness
- 9.7 Summary
- 9.8 Further Reading
- 9.9 Exercises
- Chapter 10: Network Management
- 10.1 When is Network Management Applicable?
- 10.2 a Linear Programming approach
- 10.3 Virtual Nesting*
- 10.4 Network Bid Pricing
- 10.5 Network Management in Action
- 10.6 Summary
- 10.7 Further Reading
- 10.8 Exercises
- Chapter 11: Overbooking
- 11.1 Background
- 11.2 Approaches to Overbooking
- 11.3 a Deterministic Heuristic
- 11.4 Risk-Based Policies
- 11.5 Service-Level Policies
- 11.6 Hybrid Policies
- 11.7 Extensions
- 11.8 Measuring and Managing Overbooking
- 11.9 Alternatives to Overbooking
- 11.10 Summary
- 11.11 Further Reading
- 11.12 Exercises
- Chapter 12: Markdown Management
- 12.1 Background
- 12.2 Markdown Optimization
- 12.3 Estimating Markdown Sensitivity
- 12.4 Strategic Customers and Markdown Management
- 12.5 Markdown Management in Action
- 12.6 Summary
- 12.7 Further Reading
- 12.8 Exercises
- Chapter 13: Customized Pricing
- 13.1 Background and Business Setting
- 13.2 Calculating Optimal Customized Prices
- 13.3 Bid Response
- 13.4 Extensions and Variations
- 13.5 Customized Pricing in Action
- 13.6 Summary
- 13.7 Further Reading
- 13.8 Exercises
- Chapter 14: Behavioral Economics and Pricing
- 14.1 Violations of the Law of Demand
- 14.2 Price Presentation and Framing
- 14.3 Fairness
- 14.4 Implications for Pricing and Revenue Optimization
- 14.5 Summary
- 14.6 Further Reading
- 14.7 Exercises
- Appendix A: Optimization
- A.1 Continuous Optimization
- A.2 Linear Programming
- A.3 Duality and Complementary Slackness
- A.4 Discrete Optimization
- A.5 Reinforcement Learning and Bandit Approaches
- A.6 Further Reading
- Appendix B: Probability
- B.1 Probability Distributions
- B.2 Continuous Distributions
- B.3 Discrete Distributions
- B.4 Sample Statistics
- References
- Index