Pricing and Revenue Optimization

Auteur

Robert L. Phillips is Director of Pricing Science at Amazon. He was previously Director of Marketplace Optimization Data Science at Uber Technologies, Professor of Professional Practice at Columbia Business School, Founder and Chief Science Officer at Nomis Solutions, and CEO of Talus Solutions. He is the author of Pricing Credit Products (Stanford, 2018) and the co-editor of The Oxford Handbook of Pricing Management (2014).

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"Employing analytical techniques derived from management science, and the author's extensive corporate experience, this is the definitive resource for what has emerged as critical and rapidly changing field in business strategy. MBA and executive courses will be drawn to the updates throughout the book, focusing on AI impact on revenue management, as well as compelling new cases on e-marketplace pricing at Amazon, Uber, and other leading companies"--

Contenu

Contents and Abstracts1Background chapter abstractThis 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. 2Introduction to Pricing and Revenue Optimization chapter abstract 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. 3Models of Demand chapter abstract 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 may compete with each other. I introduce the most common price-response functions including the linear, constant-elasticity, logit, and probit functions and describe their properties, as well as when they can be best used. 4Estimating Price Response chapter abstract I show how a price-response function can be estimated from historical data about prices and demand. Ways in which historical data can be obtained include price tests, A/B tests, natural experiments, and regression discontinuity design. I show how regression can be used to estimate the parameters of different price-response functions including linear, exponential, and constant elasticity. I introduce measures of fit including root-mean-square error, mean absolute percentage error, and weighted mean absolute percentage error. I show how the availability of potential demand data can significantly improve estimation, and I introduce methods for estimating a price-response function when potential demand data are available. The estimation process is discussed, as well as challenges to estimation including collinearity and endogeneity and how they can be addressed. . 5Optimization chapter abstract In this chapter I show how to calculate an optimal price. The first step is to determine an objective function to be maximized. Typically we assume that contribution is to be maximized, but in some cases, revenue may be an element in the objective function. I discuss how to calculate contribution. Given contribution and a price-response function, the optimal price must satisfy a set of conditions, such as marginal cost equaling marginal revenue and elasticity equaling inverse unit margin. I discuss the implications of these conditions for real-world prices and show how explicit optimization can be used to calculate an optimal price. I discuss how to address the case of multiple objective functions, when a seller might be interested in maximizing both profit and market share. Finally, I introduce a data-driven approach to finding an optimal price that does not require a price-response function. 6Price Differentiation chapter abstract Price differentiation is the practice of charging different prices to different customers for the same good or slightly different versions of the same good. In this chapter, I describe various techniques for using price differentiation to improve profitability including group pricing, channel pricing, regional pricing, and couponing. Some of the most effective tactics for price differentiation are inferior goods, superior goods, product lines, product versioning, and time-based differentiation. I show how to calculate optimal differentiated prices in the presence of arbitrage and cannibalization and discuss the implications of price differentiation for consumer welfare. One common approach to price differentiation is nonlinear pricing, in which purchasing multiple products together can be cheaper than purchasing them independently. I present models for two of the most common nonlinear pricing approaches: volume discounts and bundling. 7Pricing with Constrained Supply chapter abstract Supply and capacity constraints are commonplace across many industries and can have a strong effect on optimal prices. I start by discussing the nature of supply constraints and the situations in which they occur. I then discuss how a seller can determine the optimal price to charge when faced with a supply constraint, and I introduce the important concept of opportunity cost. I extend the calculation of optimal prices to the case when a supplier has a segmented market and faces supply constraints. This leads to the tactic of variable pricing, which is used when a supplier has multiple units of constrained capacity and can change prices in order to balance supply and demand. I discuss examples of variable pricing from industries including ride-sharing, concerts, and sporting events. 8Revenue Management chapter abstract Revenue management is a profit-maximizing tactic used by sellers with a fixed stock of perishable capacity. This chapter relates the history of revenue management and shows how revenue management is an application of price differentiation. I introduce revenue management tactics-the ways in which companies can manage their capacity to maximize return. The airlines and other travel-related industries were pioneers in revenue management. I show how computerized reservation systems and global distribution systems play major roles in the way that revenue management has been implemented in these industries. I discuss the role of ancillary revenue and incremental costs and how they influence revenue managem…