Pricing And Revenue Optimization (second Edition)

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. In addition, the book provides current coverage of important applications such as revenue management, markdown management, customized pricing, and the behavioral economics of pricing.

This chapter describes the historical background of pricing and revenue optimization including the factors that have driven the growth of analytical approaches to pricing, such as the success of revenue management in the airlines, advances in information technology, the rise of e-commerce, and the success of machine learning. Pricing decisions have become exponentially more complex and dynamic to the extent that it is no longer possible to manage prices effectively using spreadsheets. The use of automated techniques to set and update prices dynamically has led to profitability improvements of 10% or more in many different industries.

This chapter introduces the basic concepts behind pricing and revenue optimization. It discusses common pricing challenges such as lack of consistent management, discipline, and analysis. I describe three purist approaches to pricing—cost-plus, market-based and value-based and explain the shortcomings of each. I define pricing and revenue optimization as a process for managing and updating pricing decisions across an organization in a way that most effectively meets corporate goals using mathematical analysis. I introduce the pricing and revenue optimization cube as a convenient way to think about pricing decisions across the organization and describe the steps in an effective pricing and revenue optimization process. I describe a closed-loop process for setting, evaluating and updating prices. Finally, I discuss the role of mathematical analysis and optimization in the pricing process and contrast explicit optimization with data-driven approaches.

This chapter introduces the price-response function, which describes how demand for a product changes as a function of price. The price-response function is a key component in pricing and revenue optimization. I show how the price-response function can be derived from the distribution of willingness to pay among potential customers and describe the properties that a proper price-response function should possess. I describe the most common measures of price sensitivity such as slope, elasticity, and hazard rate and extend these measures to the case in which a seller is offering multiple products that m