Pricing and Revenue Optimization PDF

pricing and revenue optimization pricing and revenue optimization robert l. phillips stanford business books An imprint of Stanford University Press Stanford, California 2005 Stanford University Press Stanford, California © 2005 by the Board of Trustees of the Leland Stanford Junior University. All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or in any information storage or retrieval system without the prior writ- ten permission of Stanford University Press. Printed in the United States of America on acid-free, archival-quality paper Library of Congress Cataloging-in-Publication Data Phillips, Robert L. (Robert Lewis), 1955 – Pricing and revenue optimization / Robert L. Phillips. p. cm. Includes bibliographical references and index. ISBN 0-8047-4698-2 (cloth : alk. paper) 1. Pricing. 2. Revenue management. I. Title. HF5416.5.P457 2005 658.1554 — dc22 2005009126 Typeset by G&S Book Services in 10/13.5 Minion Original Printing 2005 Reprinted with corrections 2011 To Doria with love and gratitude contents Preface xi Chapter 1 Background and Introduction 1 1.1 Historical Background and Context 2 1.2 The Financial Impact of Pricing and Revenue Optimization 13 1.3 Organization of the Book 14 Chapter 2 Introduction to Pricing and Revenue Optimization 18 2.1 The Challenges of Pricing 18 2.2 Traditional Approaches to Pricing 22 2.3 The Scope of Pricing and Revenue Optimization 26 2.4 The Pricing and Revenue Optimization Process 29 2.5 Summary 35 2.6 Exercises 36 Chapter 3 Basic Price Optimization 38 3.1 The Price-Response Function 38 3.2 Price Response with Competition 55 3.3 Incremental Costs 59 3.4 The Basic Price Optimization Problem 61 3.5 Summary and Extensions 69 3.6 Exercises 70 Chapter 4 Price Differentiation 74 4.1 The Economics of Price Differentiation 75 4.2 Limits to Price Differentiation 77 4.3 Tactics for Price Differentiation 78 4.4 Volume Discounts 86 4.5 Calculating Differentiated Prices 89 viii contents 4.6 Price Differentiation and Consumer Welfare 93 4.7 Summary 96 4.8 Exercises 96 Chapter 5 Pricing with Constrained Supply 99 5.1 The Nature of Supply Constraints 100 5.2 Optimal Pricing with a Supply Constraint 101 5.3 Opportunity Cost 103 5.4 Market Segmentation and Supply Constraints 104 5.5 Variable Pricing 106 5.6 Variable Pricing in Action 111 5.7 Summary 116 5.8 Exercises 117 Chapter 6 Revenue Management 120 6.1 History 121 6.2 Levels of Revenue Management 123 6.3 Revenue Management Strategy 123 6.4 The System Context 125 6.5 Booking Control 126 6.6 Tactical Revenue Management 131 6.7 Net Contribution in Revenue Management 136 6.8 Measuring Revenue Management Effectiveness 140 6.9 Revenue Management in Action 141 6.10 Summary 144 6.11 Exercise 146 Chapter 7 Capacity Allocation 149 7.1 Introduction 149 7.2 The Two-Class Problem 149 7.3 Capacity Allocation with Multiple Fare Classes 158 7.4 Capacity Allocation with Dependent Demands 165 7.5 Capacity Allocation in Action 169 7.6 Measuring Capacity Allocation Effectiveness 170 7.7 Summary 172 7.8 Exercises 173 Chapter 8 Network Management 176 8.1 Background and Introduction 176 8.2 When is Network Management Applicable? 177 8.3 A Linear Programming Approach 183 8.4 Virtual Nesting 189 8.5 Network Bid Pricing 195 contents ix 8.6 Dynamic Virtual Nesting 202 8.7 Network Management in Action 202 8.8 Summary 204 8.9 Exercises 205 Chapter 9 Overbooking 207 9.1 Introduction 207 9.2 A Model of Customer Bookings 212 9.3 Solution Approaches 213 9.4 Extensions 228 9.5 Measuring and Managing Overbooking 233 9.6 Alternatives to Overbooking 235 9.7 *Appendix: Derivation of the Show Probability for Overbooking 237 9.8 Exercises 238 Chapter 10 Markdown Management 240 10.1 Background 241 10.2 Markdown Optimization 249 10.3 Estimating Markdown Sensitivity 256 10.4 Markdown Management in Action 258 10.5 Summary 261 10.6 Exercises 261 Chapter 11 Customized Pricing 264 11.1 Background and Business Setting 264 11.2 Calculating Optimal Customized Prices 269 11.3 Bid Response 277 11.4 Extensions and Variations 290 11.5 Customized Pricing in Action 297 11.6 Exercise 299 Chapter 12 Pricing and Revenue Optimization and Customer Acceptance 301 12.1 Price Presentation and Framing 304 12.2 Fairness 309 12.3 Implications for Pricing and Revenue Optimization 315 12.4 Summary 321 12.5 Exercise 321 Appendix A Optimization 323 A.1 Continuous Optimization 323 A.2 Linear Programming 324 A.3 Duality and Complementary Slackness 325 A.4 Discrete Optimization 326 x contents Appendix B Probability 327 B.1 Probability Distributions 327 B.2 Continuous Distributions 330 B.3 Discrete Distributions 331 Bibliography 337 Index 345 preface This book grew out of courses in pricing and revenue optimization developed at Columbia University and Stanford University.1 At the time there were few other comparable courses.2 Since then, it has become clear that there is growing interest in pricing and revenue opti- mization (a.k.a. revenue management and dynamic pricing) as a topic of study within both business schools and management science/operations research departments. This interest is quite understandable: Not only is pricing and revenue optimization an important appli- cations arena for quantitative analysis, it has achieved widely publicized successes in many industries, and there is growing interest in the techniques of pricing and revenue optimiza- tion across many different industries. Some of the issues involved in developing and teach- ing an MBA course in pricing and revenue optimization have been treated in articles by Peter Bell (2004) and myself (Phillips 2004). The primary audience for this book is students at the MBA, masters, or undergraduate level. The book assumes some familiarity with probabilistic modeling and optimization the- ory and comfort with basic calculus. Sections that require somewhat more quantitative so- phistication (or at least more patience) have been marked with an asterisk (*) and can be skipped without loss of continuity. In pricing and revenue optimization, as in other appli- cations of management science, what is theoretically elegant is often not practical and what is practical is usually not theoretically elegant. When in doubt, I have erred on the side of presenting the practical. For those who would like to dive deeper into the theory, I would recommend Talluri and van Ryzin’s The Theory and Practice of Revenue Management (2004). Among those who read drafts of this book and generously provided comments and sug- gestions, pride of place belongs to Michael Harrison, who cotaught the pricing and revenue optimization course with me at the Stanford Business School. Not only did Mike read the ﬁrst draft and provide many helpful comments, but our discussions helped me focus my own thinking. I am also thankful to Brenda Barnes, whose careful reading and thoughtful comments on several chapters resulted in substantial improvements. The late Ken McLeod of Stanford University Press provided encouragement and inspiration in the early days of writing the book. He is very much missed. I would also like to thank Dean Boyd, Bill Carroll, Yosun Denizeri, Michael Eldredge, Mehran Farahmand, Scott Friend, Steve Haas, Jake xii preface Krakauer, Ahmet Kuyumcu, Bob Oliver, Rama Ramakrishnan, Carol Redﬁeld, Alex Roma- nenko, and Nicola Secomandi, who all contributed comments and suggestions that im- proved the book. Thanks also to my students at Columbia and Stanford, who caught many typos. Finally, thanks to the Columbia University Business School, the Stanford University Business School, Manugistics, and Nomis Solutions, all of whom provided ofﬁce space and support at various times through the writing of the book. The book has also beneﬁted from my extensive interactions and discussions with col- leagues over the years, including Bill Brunger, Simon Cauﬁeld, Glenn Colville, Guillermo Gallego, Tom Grossman, Lloyd Hansen, Peter Grønlund, Garud Iyengar, Anton Kleywegt, Steve Kou, Warren Lieberman, Ray Lyons, Costis Maglaras, Özalp Özer, Özgur Özluk, Jörn Peter Petersen, Özge Şahin, Kalyan Talluri, Van Veen, Loren Williams, Garrett van Ryzin, Graham Young, and Jon Zimmerman, among many others. Special thanks to Christian Albright, Serhan Duran, Jihong Kong, Warren Lieberman, Joern Meissner, and Nicola Secomandi for catching errors in previous printings. Very special thanks to my parents for their love and support. Needless to say, any errors in the book are neither the author’s fault nor the fault of any of the others mentioned here; they are due to malign outside inﬂuences. notes 1. The course at Stanford was developed jointly with Michael Harrison. 2. Notable exceptions were Ioana Popescu’s course on dynamic pricing and revenue management at INSEAD, Peter Bell’s course on revenue management at the University of Western Ontario, and courses in hotel and restaurant revenue management developed by Sheryl Kimes at Cornell. 1 background and introduction What is a cynic?... A man who knows the price of everything and the value of nothing Oscar Wilde (1892) This is a book about pricing—speciﬁcally, how companies should set and adjust their prices in order to maximize proﬁtability. It takes the view that pricing decisions are commonplace, that they can be complex, and that they are usually critical determinants of proﬁtability. Despite this, pricing decisions are often badly managed (or even unmanaged). While most companies have pretty good prices in place most of the time, very few have the processes and capabilities needed to ensure that they have the right prices in place for all their prod- ucts, to all their customers, through all their channels, all the time. This is the goal of pric- ing and revenue optimization. Pricing and revenue optimization is a tactical function. It recognizes that prices need to change rapidly and often and provides guidance on how they should change. This makes it distinct from strategic pricing, where the goal is usually to establish a general position within a marketplace. While strategic pricing worries about how a product should in general be priced relative to the market, pricing and revenue optimization is concerned with deter- mining the prices that will be in place tomorrow and next week. Strategic pricing sets the constraints within which pricing and revenue optimization operates. One of the distinguishing characteristics of pricing and revenue optimization is its use of analytical techniques derived from management science. The use of these techniques to set prices in a complex, dynamic environment is relatively new. One of the ﬁrst applica- tions of the approach was the development of revenue management systems by the passen- ger airlines in the 1980s. Since then, the rapid development of e-commerce and the avail- ability of customer data through customer relationship management (CRM) systems has led to the adoption of similar techniques in many other industries, including automotive, retail, telecommunications, ﬁnancial services, and manufacturing. A number of software vendors provide “price optimization” or “demand management” or “revenue manage- ment” solutions focused on one or more industries. In this context, pricing and revenue op- timization is increasingly becoming a core competency within many different companies. The purpose of this book is to provide an introduction to this relatively new and rapidly changing ﬁeld. In this chapter, we begin by giving some historical context for pricing and revenue opti- 2 background and introduction mization. In particular, we give some perspective on why pricing has gone from being a largely ignored and obscure “black art” within many companies to become the subject of intense scrutiny and analysis. We argue that the “pricing problem” has become increasingly difﬁcult and is likely to become even more difﬁcult in the future. We argue further that im- proving pricing is often one of the highest-return investments available to a company. Hopefully this will whet the reader’s appetite for the more quantitative material to come in the following chapters. 1.1 historical background and context For most of history, philosophers took it for granted that goods had an intrinsic value in the same sense that they had an intrinsic color or weight. A fair price reﬂected that intrinsic value. Charging a price too much in excess of the intrinsic value was condemned as a sign of “avarice” and often prohibited by law. Prices were set by custom, by law, or by imperial ﬁat. Sermons were preached inveighing against the sin of charging unfair prices in order to receive excessive proﬁts.1 The problem of pricing did not really exist until modern market economies began to emerge in the West in the 17th and 18th centuries. With the emergence of these economies, prices were allowed to move more freely—untied to the traditional concept of value. Speculative bubbles such as “tulipomania” in the Dutch republic in the 1630s—in which the price of some varieties of tulips rose more than a hundredfold in 18 months before collapsing in 1637—and the “South Sea bubble” in England in 1720 — in which the prices of shares in the South Sea Company soared before the company col- lapsed amid general scandal—fed a sense of anxiety and the belief that prices could some- how lose touch with reality.2 Furthermore, for the ﬁrst time, large numbers of people could amass fortunes—and lose them—by buying and selling goods on the market. The question naturally arose—what were prices, exactly? Where did they come from? What determined the right price? When was a price fair? When should the government intervene in pricing? The modern ﬁeld of economics arose, at least in part, in response to these questions. Possibly the greatest insight of classical economics was that the price of a good at any time in an ideal capitalist economy is not based on any intrinsic “value” but rather on the inter- play of supply and demand. This was a major intellectual breakthrough— on par in its way with the Newtonian view of the clockwork universe and Darwin’s theory of evolution. In es- sence, the price of a good or service was determined by the interaction of people willing to sell the good with the willingness of others to buy the good. That’s all there is to it—neither intrinsic “value” nor cost nor labor content enters directly into the equation. Of course, these and other factors enter indirectly into pricing—sellers would not last long selling goods below cost, and the prices buyers accept are based on the “value” they placed on the item—but these were not primary. There are many reasons why sellers sell below cost when they are in possession of a cartload of vegetables that are on the verge of going rotten—the classic “sell it or smell it” situation— or to attract a desirable new customer. Just so, the “value” that buyers placed on different goods changed with their changing situation and the dictates of fashion. According to modern economics there is no normative “right price” for a good or service against which the price can be compared—rather, there are only the background and introduction 3 actual prices out in the marketplace, ﬂoating freely without an anchor, based only on the willingness of sellers to sell and buyers to buy. While classical economics solved the problem of the origin of price, it raised as many ques- tions as it answered. In particular, if prices were not tied to fundamental values—if they had no anchor—why did they show any stability at all? Under normal circumstances, prices for most goods are pretty stable most of the time. If prices are based only on the whims of buy- ers and sellers, why is the price of bread not subject to wild swings like the Dutch tulip mar- ket in 1689? Why doesn’t milk cost ﬁve times as much in Chicago as it does in New York? How can manufacturers and merchants plan at all and make reasonable proﬁts in order to stay in business? How can an economy based on free-ﬂoating prices work at all? And, assuming that such an economy could work, how could it possibly work better than a centralized economy where planners carefully sought to allocate resources across the entire economy? One of the great achievements of 20th century economics was to show mathematically how a largely unregulated economy could work: that an economy consisting of individuals who supply their labor in return for wages and use their earnings to buy goods to maximize their “utility” combined with ﬁrms who seek to maximize proﬁtability can be remarkably stable and efﬁcient.3 Under certain assumptions, this type of capitalist economy can be shown to be at least as efﬁcient as any centrally planned economy. Furthermore, prices in such an economy would generally be stable and reasonably predictable. The price for a product would equal the long-run marginal production cost of that product plus the return on invested capital necessary to produce the product. If someone were selling the product for less, he or she would go out of business because his or her costs would not be covered. If someone tried selling for more, other sellers would undercut his price, consumers would ﬂee to the lower-priced sellers, and the high-price seller would be forced to lower his price or go bankrupt for lack of business. As this happens simultaneously, economy-wide, prices equilibrate and change only due to exogenous shocks, changes in resource availability, tax- ation or monetary policy, or changes in consumer tastes. This view of the world is based primarily on the assumption that most markets are per- fectly competitive, where the idea of perfect competition can be summarized as follows. A market structure is perfectly competitive if the following conditions hold: There are many ﬁrms, each with an insubstantial share of the market. These ﬁrms produce a homog- enous product using identical production processes and possess perfect information. It is also the case that there is free entry to the industry; that is, new ﬁrms can and will enter the industry if they observe that greater-than-normal proﬁts are being earned. The effect of this free entry is to push the demand curve facing each ﬁrm downwards until each ﬁrm earns only normal proﬁts, at which point there is no further incentive for new entrants to come into the industry. Moreover, since each ﬁrm produces a homogenous product, it cannot raise its price without losing all of its market to its competitors.... Thus ﬁrms are price takers and can sell as much as they are capable of producing at the prevailing market price.4 There are no pricing decisions in perfectly competitive markets—prices are determined by the iron law of the market. If one merchant were offering a good for a lower price than another, neoclassical economics assumes that either customers would entirely abandon the 4 background and introduction higher-price merchant and swamp the lower-price merchant or an arbitrageur would arise who would buy all the goods from the lower-price merchant and sell them at the higher price. In either case, a single market price would prevail. Furthermore, if prices were so high that merchants enjoyed higher proﬁts than the rest of the economy, more sellers would en- ter, lowering the average price until the return on capital dropped to the market level. In this situation, there are no pricing decisions at all: Prices are set “by the market”—as stock prices are set by the New York Stock Exchange or NASDAQ. The price of Microsoft stock is not set by any “pricer” but by the interplay of supply and demand for the stock. Many ﬁnan- cial instruments, such as stocks and bonds, satisfy the economic deﬁnition of a commodity. Certain other highly fungible goods—grain, crude oil, and some bulk chemicals—also come very close to being commodities. In these markets, there is simply no need for pric- ing and revenue optimization—the market truly sets the price. As any shopper can tell you, much of the real world is messier—prices vary all over the place, sometimes in ways that seem irrational. Buyers often behave erratically, sellers do not always seek to maximize short-run proﬁt, neither buyers nor sellers are possessed of perfect information, and opportunities for arbitrage are not immediately seized. Table 1.1 shows prices for a half gallon of whole milk at different markets in a 16-block area of the upper west side of Manhattan on a single day in May 2002. Prices range from a low of $1.39 to a high of $2.00 —a variation of $0.61, or 44%. Furthermore, the price varied by more than $0.40 even for two stores on the same block. How could this be? Why would anybody buy milk at a high price when they could walk a block and save 40 cents? Why don’t arbitrageurs buy all the milk at the lower price and sell it at the higher? Table 1.1 Retail prices for a half gallon of whole milk on the Upper West Side of Manhattan, May 2002 Location Price 74th and Broadway $1.39 79th and Amsterdam 1.59 77th and Broadway 1.59 74th and Columbus 1.69 73rd and Columbus 1.79 74th and Amsterdam 1.79 75th and Broadway 1.89 71st and Columbus 1.99 78th and Amsterdam 2.00 AVERAGE $1.75 STANDARD DEVIATION 0.20 The price variation shown in Table 1.1 will hardly come as a shock to most people— after all, both businesses and consumers know that it pays to shop around—suppliers of the same (or similar) products will often charge different prices. Furthermore, there are other ways to pay a lower price for exactly the same product: Wait until it goes on sale, travel to a retail outlet, clip a coupon, buy in bulk, buy it online, try to negotiate a lower price. In fact, it is hardly a secret not only that prices vary between sellers but that a single seller will often sell the same product to different customers for different prices! background and introduction 5 The tools that pricers use day to day are far more likely to be drawn from the ﬁelds of sta- tistics or operations research than from economics. Marketing science, which deals with the quantitative analysis of marketing initiatives, including pricing, is usually considered part of the broader ﬁeld of operations research and management science.5 Application of these techniques to problems of marketing began to emerge in a signiﬁcant fashion in the 1960s.6 Since then, marketing scientists have developed, applied, validated, and reﬁned important mathematical models to a broad range of issues, such as forecasting sales, product planning, predicting market response, product positioning, pricing, promotions, sales force compen- sation, and marketing strategy.7 True to its name, marketing science has brought some sci- ence to what was previously viewed as a “black art.” Despite these achievements, there remains a gap between marketing science models and their use in practice. The reasons for this gap are numerous. Many marketing models have been built on unrealistically stylized views of consumer behavior. Other models have been built to “determine if what we see in practice can happen in theory.” Other models seem limited by unrealistically simplistic assumptions. In any case, a leading text on marketing science admitted: With an area of such importance and with so much at stake, it might be assumed that a great deal of continuing research and planning would by now underlie the formulation of pricing strategy and the setting of prices. One might also expect that a well-developed body of theory would have resulted in principles to guide pricing decisions. But this does not appear to be the case.8 One of the possible reasons for the gap between marketing science theory and its applica- tion to real pricing decisions is that pricing decisions are becoming increasingly tactical and operational in nature. Companies increasingly need to make pricing decisions more and more rapidly in order to respond to competitive actions, market changes, or their own in- ventory situation. They no longer have the luxury to perform market analyses or extended spreadsheet studies every time a pricing change needs to be considered. The premium is on speed. While there has been a general acceleration of business in all ﬁelds, the impact on pricing and revenue optimization has been particularly notable. This acceleration—and the corresponding interest in developing tools to enable better pricing and revenue optimiza- tion (PRO) decisions—has been driven by four trends. The success of revenue management in the airline industry provided an example of how pricing and revenue optimization could increase proﬁtability in a real-time pricing environment. The widespread adoption of enterprise resource planning (ERP) and customer relationship management (CRM) software systems provided a new wealth of corpo- rate information that can be utilized to improve pricing and revenue optimization decisions. The rise of e-commerce necessitated the ability to manage and update prices in a fast-moving, highly transparent, online environment for many companies that had not previously faced such a challenge. 6 background and introduction The success of supply chain management proved that analytic software systems could drive real business improvements. Because of their importance to the development of pricing and revenue optimization, we will spend a little time to discuss each of these trends. 1.1.1 The Success of Revenue Management In 1985, American Airlines was threatened on its core routes by the low-fare carrier PeopleExpress. In response, American developed a revenue management program based on differentiating prices between leisure and business travelers. A key element of this program was a “yield management” system that used optimization algorithms to determine the right number of seats to protect for later-booking full-fare passengers on each ﬂight while still accepting early-booking low-fare passengers. This approach was a resounding success for American, resulting ultimately in the demise of PeopleExpress. We delve more deeply into the American Airlines/PeopleExpress story in Chapter 6. For now, the importance of the story is in the publicity it garnered. American Airlines featured its revenue management capabilities in its annual report. The team that developed the sys- tem won the 1991 Edelman Prize for best application of management science. American Airlines’ revenue management has been widely touted as an important strategic application of management science (C. K. Anderson, Bell, and Kaiser 2003), and the tale of American using its superior capabilities to defeat PeopleExpress was the centerpiece of a popular busi- ness book (Cross 1997). Not surprisingly this publicity resulted in widespread interest. Companies began to in- vestigate the prospects of improving the proﬁtability of their pricing decisions. Ford Motor Company was inspired by the success of revenue management at the airlines to institute its own very successful program (Leibs 2000). Vendors arose selling revenue management soft- ware systems, and consultants appeared offering to help companies set up their own pro- grams. Revenue management spread well beyond the passenger airlines. Under its strictest deﬁnition, revenue management has a fairly narrow ﬁeld of applica- tion. In particular, the techniques of revenue management are applicable when the follow- ing conditions are met. Capacity is limited and immediately perishable. Most obviously, an empty seat on a departing aircraft or an empty hotel room cannot be stored to satisfy future demand. Customers book capacity ahead of time. Advance bookings are common in indus- tries with constrained and perishable capacity, since customers need a way to ensure ahead of time that capacity will be available when they need to consume it. This gives airlines the opportunity to track demand for future ﬂights and adjust prices accordingly to balance supply and demand. Prices are changed by opening and closing predeﬁned booking classes. This is a by-product of the design of the computerized distribution systems, such as SABRE and Galileo, that the airlines developed. These systems allow airlines to establish background and introduction 7 a set of prices (fare classes) for each ﬂight and then open or close those fare classes as they wish. This is somewhat different from the pricing issue in most industries, which is not “What fares should we open and close?” but “What prices should we be offering now for each of our products to each market segment through each chan- nel?” The difference is subtle, but it leads to major differences in system design and implementation. Many companies are understandably wary about adopting “revenue management” pro- grams, protesting that “we are not an airline.” In general, this is the right view—the algo- rithms behind airline revenue management do not transfer directly to most other indus- tries. However, the experience of the airlines contains several important lessons. Pricing and revenue optimization can deliver more than short-term proﬁtability beneﬁts. Revenue management enabled American Airlines to meet the challenge posed by PeopleExpress. It also meant the difference between survival and bank- ruptcy for National Rent-a-Car. In 1992, National was losing $1 million per month and was on the verge of being liquidated by its then-owner, General Motors.9 At this point, National had been through two rounds of downsizing, and corporate management felt there were no more signiﬁcant savings that could be achieved on the cost side. As a last-ditch effort, National decided to work on the revenue side. They worked with the revenue management company Aeronomics to develop a system that forecast supply and demand for each car type/rental length at all 170 corporate locations and adjusted fares to balance supply and demand. The results were immediate. National initiated a comprehensive revenue management program whose core is a suite of analytic models developed to manage capacity, pricing, and reservation. As it improved management of these functions, National dramatically increased its revenue. The initial implementation in July 1993 produced immediate results and returned National Car Rental to proﬁtability. (Geraghty and Johnson 1997) E-commerce both necessitates and enables pricing and revenue optimization. The airlines pioneered electronic distribution—their computerized distribution sys- tems, SABRE and Galileo, were the “Internet before the Internet.” These systems allowed immediate receipt and processing of customer booking requests. They also enabled airlines to change prices and availability and have the updated infor- mation instantaneously transmitted worldwide. In effect, the airlines were wrestling with the complexities of e-commerce well before the arrival of the Internet. The necessity to continually monitor demand and update prices accordingly will be felt by more and more industries as electronic distribution channels such as the Internet become more pervasive. Effective segmentation is critical. The key to the success of revenue management in the airline industry was the ability of the airlines to segment customers between early- booking leisure passengers and late-booking business passengers. Note that this segmentation was achieved not by direct discrimination—that is, trying to charge 8 background and introduction a different fare based on demographics, age, or other customer characteristics— but via product differentiation, creating different products that appealed to differ- ent segments. Segmenting customers based on their willingness to pay and ﬁnding ways to charge different prices to different segments is a critical piece of pricing and revenue optimization— one that we address in detail in Chapter 4. At heart, airline yield management systems are highly sophisticated opportunity cost cal- culators. They forecast the future opportunities to sell a seat and seek to ensure that the seat is not sold for less than the expected value of those future opportunities. Most industries do not face capacity constraints as stark as those faced by the airlines. Manufacturers typically have the opportunity to adjust production levels or store either ﬁnished or partially ﬁnished goods. Retailers can adjust their stocks in response to changes in demand. However, this does not mean that calculating opportunity cost is not relevant to these industries. On the contrary, in many industries facing inventory or capacity constraints, opportunity cost can be the critical link between supply chain management and pricing and revenue optimiza- tion. We explore this link in greater detail in Chapter 5. 1.1.2 The New Wealth of Information As airlines began to develop more sophisticated revenue management systems in the 1990s, other businesses were adopting a new generation of corporate software. Historically, most business software had been homegrown, highly specialized, and oriented toward a single corporate function, such as payroll or invoicing. Such “legacy systems” had often been de- veloped in isolation, were not integrated, and used independent and often-conﬂicting data sources and deﬁnitions. As a result, many companies were reaching the point of diminish- ing returns on their information technology (IT) investments, with an increasing amount of each IT dollar spent on integrating existing systems rather than building new functional- ity. Furthermore, the lack of consistent information among departments was often leading to inefﬁciency and frustration, with identical data entered several times and data items with the same name often having different meanings or interpretation. This set the stage for enterprise resource planning (ERP) systems. Vendors such as Ora- cle, SAP, PeopleSoft and Baan began offering integrated client-server ERP systems that en- abled corporations to obtain a uniﬁed view of all their data. These systems enabled differ- ent parts of a company to have access to a consistent, deﬁnitive view of corporate data. This in turn enabled efﬁcient and consistent cross-functional business processes without the need for different groups to rekey data or access disparate software systems. The ERP vision is to provide a corporate “information backbone” that supports all business users with con- sistent and timely data from a single source. Information will then ﬂow freely among busi- ness processes. Analytical applications such as supply chain management or stafﬁng systems sit on top of the ERP system and draw the needed inputs from the ERP database. Informa- tion “islands” and “ﬁefdoms” are eliminated, as is the tedious task of shuttling between dif- ferent software systems and data sources. Corporations will be more efﬁcient, more nimble, and more customer responsive, and IT development can focus on improving functionality rather than integration. background and introduction 9 The ERP vision is compelling, but the history of ERP systems has hardly been one of unqualiﬁed triumph—many companies have found that replacing their legacy systems with an ERP system can be expensive, disruptive, and painful. Nonetheless, it is clear that the last decade has seen a much-needed consolidation of corporate information. This makes it much easier to implement analytical systems such as pricing and revenue optimization. Such PRO systems require both timely information about product costs and availabilities and the results of recent transactions. By automating and standardizing data consolidation and reconciliation, ERP systems enable much more rapid implementation of pricing and revenue optimization. Another source of improved data storage and availability came from the increasing adop- tion of customer relationship management (CRM) systems. These involve gathering and stor- ing customer and transaction information and using the results to improve marketing, sales, and customer service. Customer relationship management systems from such vendors as Siebel and e-piphany collect customer and transaction data from different channels and make it available in a data warehouse to various business intelligence, data-mining, and analytic systems. Pricing and revenue optimization systems are a natural extension of CRM. In essence, CRM systems provide the rich customer and transaction history that pricing and revenue optimization systems need to evaluate customer response and update pricing recommen- dations. Harrah’s Entertainment, which operates 24 casinos and 16 hotels under the Har- rah’s, Harvey’s, Rio, and Showboat brands, provides an example of the successful use of a CRM system with pricing optimization. Harrah’s has linked a homegrown CRM system with its reservation and revenue management systems. Based on historical information about customer behavior and preferences, customers are classiﬁed into 64 segments based not only on their current value but on their expected lifetime value to Harrah’s. The revenue management system forecasts daily hotel room demand for each of these segments and cal- culates the minimum room price necessary to optimize the room inventory. When cus- tomers call for reservations, call-center representatives can see on their screens the customer segment and the approved pricing offers. The Harrah’s system provides an excellent example of a CRM system closely linked with a price optimization system. The CRM system tracks customer behavior and demographics information, such as zip codes, which enables segmentation of customers into such group- ings as “avid experienced player” and the calculation of expected proﬁtability and gaming rev- enue from each segment. The revenue management system balances the need to ensure that rooms will be available for high-revenue customers while not holding back so many rooms that occupancy suffers. The information needed to manage customers at this level of detail simply would not have been available before the advent of customer relationship manage- ment technology.10 Harrah’s is an excellent example of how the new wealth of customer in- formation available through CRM systems can enable pricing and revenue optimization. 1.1.3 The Rise of e-Commerce By the late 1990s, the Internet was widely predicted to be a “revolutionary” and transfor- mative technology that would “change everything.” The fact that the Internet will drive a 10 background and introduction greater need for pricing and revenue optimization is contrary to some early expectations. A number of analysts predicted that Internet commerce would inevitably drive prices down to the lowest common denominator. Many analysts argued, in effect, that the Internet would bring about the world of perfect competition, in which sellers would lose control of prices. This was part of the vision behind Bill Gates’ concept of the “frictionless economy.” 11 However, the reality has been quite different. Studies consistently show that most online buyers actually do little shopping—for example, a McKinsey study showed that 89% of on- line book purchasers buy from the ﬁrst site they visit, as do 81% of music buyers.12 As a re- sult, online prices often vary considerably, even for identical items. Table 1.2 shows the base price and shipped price for 10 online vendors compared to the list price for Stephen King’s book Bag of Bones, gathered on April 30, 2002, by an online shopping agent.13 Note that the delivered price varies by more than $8.00 across the vendors, with none of the 10 vendors offering the book at the same price. Note also that the two most successful online book- sellers—Amazon and Barnes and Noble—have neither the highest nor the lowest price, and their prices differ substantially from each other. At least in this case, the Internet seems to support rather than eliminate price variability! Table 1.2 Online book prices for the hardback edition of Bag of Bones by Stephen King, April 30, 2002 Vendor Price Shipping cost Total 1Book Street $23.09 $0.00 $23.09 buy.com 19.88 3.25 23.13 Alphacraze 19.41 3.88 23.29 A1 Books 19.50 3.95 23.45 ecampus.com 21.00 2.98 23.98 Amazon 19.60 4.48 24.08 TextbookX.com 21.00 3.48 24.48 BooksaMillion 20.86 3.98 24.84 BarnesandNoble.com 22.40 3.99 26.39 List Price 28.00 — 28.00 Powell’s 28.00 3.50 31.50 AVERAGE $21.47 $3.35 $24.82 The resemblance between the distribution of online book prices in Table 1.2 and Table 1.1 is not totally coincidental. It shows that the Internet supports price differentials in the same way as other distribution channels, such as direct and indirect sales and retail outlets. For any seller offering a large catalog of products (such as an online bookseller), the price of each item needs to be set intelligently based on cost, inventory, current competitive prices, and other information. The pricing problems facing an online bookseller are similar to those facing retailers: Could I increase my proﬁtability by raising my price? by lowering my price? How should my price be updated as inventory changes—as competitive prices change? as demand changes? What is the right relationship between my base price and the total delivered price? Multiply these questions by a catalog of hundreds of thousands of items and a need to update daily, and the full magnitude of the pricing problem faced by on- line merchants becomes clear. background and introduction 11 While one group of analysts was predicting that the Internet would eliminate price differ- entials and drive prices inevitably toward the lowest common denominator, another group of analysts was predicting exactly the opposite—that the Internet would enable “one- to-one” pricing crafted to the individual propensities of each buyer. According to this school of thought, e-commerce would become a “market-of-one” environment, in which prices would be calculated on the ﬂy to maximize the proﬁtability of each transaction. A customer entering a Web site would immediately be identiﬁed, and, based on his past buying patterns, a pricing engine would calculate personalized prices reﬂecting his willingness to pay. Need- less to say, the one-to-one e-commerce pricing world has not yet arrived. And there are pow- erful reasons to believe it will never fully arrive. For one thing, there is strong buyer resistance to attempts at pricing discrimination that are perceived as unfair or arbitrary. People are no more inclined to accept online price discrimination than they would be willing to accept vari- able pricing at the time of checkout based on the clerk’s estimate of their “willingness to pay.” In addition, the transparency of the Internet means that whatever online pricing system a company adopts, the details will be widely known across the buying community within a very short period of time. As a result, price differentiation on the Internet will occur, but it will require careful analysis and execution to be successful. Given that the Internet will neither drive prices to their lowest common denominator nor lead to real-time personalized pricing, can we conclude that it will have no impact on pricing? Not at all. On the contrary, e-commerce will be a major motivator for companies to improve their pricing capabilities. Four speciﬁc characteristics of Internet commerce in- crease the urgency of pricing and revenue optimization. The Internet increases the velocity of pricing decisions. Many companies that changed list prices once a quarter or less now ﬁnd they face the daily challenge of determining what prices to display on their Web site or to transmit to e-commerce intermediaries. Many companies are beginning to struggle with this increased price velocity now—and things will only get worse. As a possible harbinger of things to come, a typical major domestic airline needs to evaluate more than 500,000 price changes a week. The magnitude and complexity of the pricing decisions faced by the airlines are a direct (if unintentional) result of their widespread adoption of electronic distribution systems more than 20 years ago. Over time, companies sell- ing on the Internet may see 10-fold or even 100-fold increases in pricing velocity. The Internet makes available an immediate wealth of information about customer behavior that was formerly unavailable or only available after a considerable time lag. This includes not just information on who bought what, but also who clicked on what, and who looked at what and for how long. This information is being in- creasingly captured and analyzed by companies both to support cross-selling and up-selling and also to understand customer behavior and segmentation. The Internet provides a unique laboratory for experimenting with pricing alterna- tives and alternative pricing models. eBay and Priceline are two Internet success stories, each with a business model based on variations of auction pricing. The Internet also has the potential to be an ideal laboratory for pricing experiments. 12 background and introduction As Michael Marn and his colleagues point out, “Traditional price-sensitivity re- search can cost up to $300,000 for each product category and take anywhere from six to ten weeks to complete.... On the Internet, however, prices can be tested continually in real time, and customers’ responses can be instantly received.” 14 Even in cases where a customer does not buy online, the Internet may provide deeper information about costs and competitive prices. This has been particularly true in “big ticket” consumer purchases, such as home mortgages and automobiles. According to J. D. Power and Associates, in 2001, 62% of new-car buyers consulted the Internet to ﬁnd information on invoice prices, sticker prices, and trade-in values (Fahey 2002). In this environment, merchants need to be able to use intelligent targeted pricing in order to maintain proﬁtability. It is too early to predict the ultimate role of the Internet in the world of commerce. How- ever, it is certainly safe to say that it will be an increasingly vital distribution channel in many, if not most, industries. Furthermore, it is only going to increase the pressure for more rapid price updates and transaction responses. And it will deliver ever richer views of cus- tomer response and behavior. As a result, the need for pricing and revenue optimization systems to support online pricing and sales will become ever more urgent. 1.1.4 The Success of Supply Chain Management Systems Supply chain management was another success story of the 1990s. Supply chain software de- veloped and sold by companies such as i2, Manugistics, and SAP enabled companies to im- prove efﬁciencies, slash inventories, cut costs, and improve customer service. By 2000, even Alan Greenspan was crediting automated supply chain systems with “driving substantial economy-wide improvements in efﬁciency and productivity.” The success of supply chain management systems proved that sophisticated quantitative analysis applied to complex corporate problems could lead to real improvements. Pricing and revenue optimization is based on the same basic idea of using analytical techniques to improve corporate decision making—in this case on the marketing and sales side of the organization. The successes of SCM has given corporate management greater conﬁdence that sophisticated analytical software can lead to real improvements in proﬁtability, thereby paving the way for PRO. Supply chain management has opened the door for pricing and revenue optimization in another way. Most of the major corporate initiatives of the 1990s were focused on improv- ing efﬁciency and reducing cost. This includes the fad for corporate reengineering that fol- lowed the 1993 publication of Hammer and Champy’s book Reengineering the Corporation, the controversial wave of downsizing and “right-sizing” in the 1990s, and the growing adoption of enterprise resource planning and supply chain management software. What this diverse set of initiatives had in common was a focus on improving corporate efﬁciency: the “cost” side of the income statement. A 1999 Deloitte and Touche survey of more than 200 companies that had either implemented ERP systems or were in the process of doing so revealed that only 7% of those companies named “increased revenue or proﬁts” as an anticipated beneﬁt from their implementation.15 While the opportunities to improve background and introduction 13 efﬁciency and reduce costs have hardly disappeared, it is fair to say that most companies are beginning to see reduced marginal returns from cost-focused initiatives. This means that the major area remaining for corporate proﬁtability improvement will be on the marketing and sales side. And, among opportunities for improvement in marketing and sales, PRO usually has the most immediate impact and the highest return. Finally, supply chain management has a natural synergy with pricing and revenue op- timization. Supply chain management systems have generally assumed that demand, while uncertain, was exogenous. The job of the supply chain management system was to ﬁnd a way to ﬁll current and anticipated orders at lowest cost while meeting customer service con- straints. Pricing and revenue optimization assumes that variable costs and capacity avail- abilities are ﬁxed, and it looks to ﬁnd the set of prices and customer allocations that maxi- mizes proﬁtability, subject to these constraints. While individually critical, each of these capabilities is looking at only a limited subset of the decisions faced by the overall organi- zation. A company that has achieved both supply chain management excellence and pric- ing and revenue optimization excellence may still have the opportunity to increase proﬁt- ability further by explicitly linking the operations and the customer-facing sides of the company. All four of these factors—revenue management, the rise of the Internet, the new wealth of information, the success of supply chain management—point in the same direction, to- ward a future in which pricing will increasingly become a tactical and operational function, supported by a wealth of customer and supply information and requiring quantitative deci- sion support technology. Both the time available to set prices and the allowable margin of error will continue to decrease while the complexity of pricing decisions will increase. Time- consuming “ofﬂine” analyses will become increasingly irrelevant—their results will be ob- solete before they can be completed because the world moves too quickly. Automated pricing and revenue optimization systems will be required to respond rapidly and effectively in the new world. However, automated systems are only part of the answer— effective pricing will also require the right supporting business processes, metrics, and supporing organization. 1.2 the financial impact of pricing and revenue optimization Of course, the most compelling reason for a company to improve its pricing and revenue optimization capabilities is to make more money. “For most companies, better manage- ment of pricing is the fastest and most cost-effective way to increase proﬁts.” So concluded a pioneering study by McKinsey and Associates.16 Based on the “average economics of 2,463 companies in Compustat aggregate,” the McKinsey researchers concluded that a 1% im- provement in proﬁt would, on average, result in an improvement in operating proﬁt of 11.1%. By contrast, 1% improvements in variable cost, volume, and ﬁxed cost would pro- duce operating improvements of 7.8%, 3.3%, and 2.3%, respectively. Similar results were obtained from a 1999 A. T. Kearney analysis of 500 companies in the S & P 500. The A. T. Kearney leverage numbers were different, but, as shown in Table 1.3, price improve- ment had still, by far, the largest impact of the three categories. 14 background and introduction Table 1.3 Results of two studies on the average impact of a 1% improvement in different variables on operating proﬁt McKinsey (1992) A. T. Kearney (2000) Price management 11.1% 8.2% Variable cost 7.8% 5.1% Sales volume 3.3% 3.0% Fixed cost 2.3% 2.0% The passenger airlines typically claim between 8% and 11% beneﬁt from the use of their revenue management systems (Smith et al. 1992)—a ﬁgure remarkably consistent with the numbers shown in Table 1.3. A Harvard Business Review article noted that some retailers are achieving “gains in gross margins in the range of 5 –15%” from the use of optimization- based assortment and pricing optimization systems. Early adopters include J. C. Penney and Gymboree (Friend and Walker 2001). ShopKo Stores has seen a 24% increase in gross mar- gin from the pilot use of a markdown management system (Levison 2002). Ford Motor Company has used a promotions management system to signiﬁcantly improve the use of its $9 billion annual North American promotions budget (Leibs 2000) and expects to realize an additional $5 billion in proﬁts over 5 years from effective market segmentation.17 Putting it all together, we can see there is a strong case for many companies to consider pricing and revenue optimization. Not only is improving pricing already the “fastest and most cost-effective way to increase proﬁts,” but it is likely to gain in importance as the veloc- ity and complexity of pricing decisions inexorably increase. Furthermore, a new generation of information technology provides the information and algorithmic power needed to an- alyze and exploit market opportunities. Finally, not only does pricing have extremely high leverage in improving proﬁtability, as shown in Table 1.3; it is often the area that can be im- proved the most with the least investment. The focus on cost improvement throughout the 1990s means that incremental improvements in variable costs and ﬁxed costs will be more expensive and difﬁcult than incremental improvements in pricing and price management. 1.3 organization of the book The structure and dependencies of the remaining chapters are illustrated in Figure 1.1. The topic of each chapter is outlined brieﬂy next. Chapter 2 discusses pricing and revenue optimization as a corporate process and con- trasts it with other approaches to pricing. It stresses that PRO is a highly dynamic process, dependent on continual feedback to ensure that pricing decisions are kept in line with changing market realities. It also discusses the concept that the core of pricing and revenue optimization lies in the formulation of pricing and availability decisions as constrained optimization problems. Chapter 3 reviews the basic economics behind price optimization. It introduces the idea of a price-response curve and presents several common forms of price-response curve. It shows how the price-response curve can incorporate competitive pricing. It argues that incremental cost is also a critical input to pricing a customer commitment. Finally it shows background and introduction 15 Chapter 2 Introduction to Pricing and Revenue Optimization Chapter 3 Basic Price Optimization Chapter 4 Price Differentiation Chapter 5 Pricing with Constrained Supply Chapter 6 Chapter 10 Chapter 11 Revenue Management Markdown Management Customized Pricing Chapter 7 Chapter 8 Chapter 9 Capacity Allocation Network Management Overbooking Chapter 12 PRO and Customer Acceptance Figure 1.1 Relationships among the remaining chapters. several ways that prices can be optimized in the simplest case of a supplier selling a single product with a known price-response curve. Price differentiation is at the core of pricing and revenue optimization. Chapter 4 dis- cusses how markets can be divided into different market segments such that a different price can be charged to each segment. Tactics for price differentiation include virtual products, product lines, group pricing, channel pricing, and regional pricing. The idea of multipricing is the source of both the beneﬁts of PRO and the challenges involved in managing a large portfolio of prices. This chapter discusses how to optimize differentiated prices in the face of potential cannibalization. Chapter 5 introduces another major theme in pricing and revenue optimization—pric- ing when supply (or inventory) is constrained. Supply constraints are ubiquitous. They may arise from the intrinsically constrained capacity of a service provider, such as a hotel, res- taurant, barbershop, or trucking company; they may be due to limited inventory on hand; or they may be due to “bottlenecks” in a supply chain that restrict the rate at which a good can be produced or transported. In any case, constrained supply signiﬁcantly complicates optimal pricing. This chapter also introduces the key concept of an opportunity cost—the incremental contribution lost due to a supply constraint. Revenue management has been one of the most important and publicized applications of pricing and revenue optimization over the past two decades. While it has its origin in the airline industry, it has spread beyond the airlines and is in widespread use at hotels, rental car ﬁrms, cruise lines, freight transportation companies, and event ticketing agents. Some 16 background and introduction of the core techniques are gaining acceptance and use well beyond these industries. We de- vote four chapters to revenue management. The ﬁrst— Chapter 6 — describes the back- ground, history, and business setting of revenue management. Chapter 7 is devoted to ca- pacity allocation, the techniques used to determine which fare classes should be open and which closed at any time for a product consisting of a constrained resource. Chapter 8 ex- tends this analysis to the case of a network, in which an individual product may use many different resources. Chapter 9 discusses overbooking—the question of how many units of a constrained product should be sold when customers may not show or may cancel. Markdown management—the topic of Chapter 10 —is an increasingly popular appli- cation of pricing and revenue optimization. In a markdown industry, a merchant has a stock of inventory whose value decreases over time. His problem is to determine the schedule of price reductions to take in order to maximize the return from inventory. Applications are widespread, from fashion goods, through consumer electronics and durables, to theater tickets. This chapter describes basic markdown optimization models and some of the chal- lenges in implementing markdown management in the real world. Chapter 11 treats customized prices. Customized pricing is common in business-to- business settings where goods and services are sold based either on long-term contracts or as part of large individual transactions. In these settings, list prices may not exist or may serve only as “guidelines” off of which discounts are set. The pricing and revenue opti- mization challenge is to determine the discount levels to provide to each customer in order to maximize the expected proﬁtability of the deal. This requires estimating the tradeoff be- tween the probability of winning the deal and the margin contribution if the deal is won. Chapter 12 treats the issue of customer perception and acceptance of pricing tactics. Much of the classical theory of pricing is based on the idea that consumers are rational “util- ity maximizers.” Prices are emotionally neutral signals that guide purchasing decisions. However, both common sense and recent research has shown that this view is incomplete at best and misguided at worst. Consumers can care deeply about prices and the way they are presented. In particular, pricing that is perceived as “unfair” can trigger an emotional rejection. Chapter 12 discusses the implication of consumer “irrationality” for PRO. notes 1. Robert Heilbroner (1999), in his classic history of economics, The Worldly Philoso- phers, cites a 1639 sermon in which the minister of Boston inveighed against such false principles of trade as “that a man might sell as dear as he can and buy as cheap as he can” and “that he may sell as he bought, though he paid too dear.” 2. The classic account of early economic manias is Extraordinary Popular Delusions and the Madness of Crowds by Charles Mackay, originally published in 1841 and reprinted many times since. Mike Dash’s book Tulipomania (1999) is a very readable account of the incredible boom and bust in tulip prices in the 17th century Dutch Republic. John Carswell’s The South Sea Bubble (2001) is the deﬁnitive account of the 18th century British stock scandal. A Conspiracy of Paper (2000) by David Liss is an entertaining ﬁctional treat- ment of the bubble. 3. The most compact summary of this work is Gerard Debreu’s Theory of Value (1963). 4. This is from the entry on “perfect competition” in the MIT Dictionary of Modern Economics (Pearce 1992). 5. As evidence, the journal Marketing Science is published by INFORMS, the Interna- tional Forum for Operations Research and Management Science. 6. See Eliashberg and Lilien (1993, Ch. 1). The journal Marketing Science published its ﬁrst issue in 1964. 7. A good overview of the scope of marketing science can be found in Eliashberg and Lilien (1993). 8. From Lilien, Kotlar, and Murthy (1992). 9. Cross (1997, Ch. 7). 10. See Phillips and Krakauer (2002) and Margulis (2002) for more discussion of Harrah’s system. 11. See Gates (1996). 12. This McKinsey study is cited in Baker, Marn, and Zawada (2000). Similar results were reported in a 1999 Jupiter Communications study. 13. Prices do not include state tax. Delivery times and availability vary among vendors. 14. From W. Baker, Marn, and Zawada (1996). 15. Cited in O’Leary (2000). 16. Marn and Rosiello (1992). 17. Cited in Richardson (2002). 2 introduction to pricing and revenue optimization In this chapter, we introduce the basic concepts behind pricing and revenue optimiza- tion. We ﬁrst look at some of the common pricing challenges faced by organizations. These include a lack of consistent management, discipline, and analysis across pricing decisions. We describe three traditional approaches to pricing— cost-plus, market based, and value based—and discuss some of their shortcomings. We then introduce pricing and revenue optimization. At the highest level, pricing and revenue optimization is a process for man- aging and updating pricing decisions in a consistent and effective fashion. At the core of this process is an approach to ﬁnding the set of prices that will maximize total expected contri- bution, subject to a set of constraints. The constraints reﬂect either business goals set by the organization or physical limitations, such as limited capacity and inventory. While the use of constrained optimization is common to all pricing and revenue optimization applica- tions, the type of problem to be solved depends on the speciﬁc characteristics of the mar- ket. Markets vary in terms of timing or cadence of pricing decisions, the nature of the goods and services being sold, and the type of customer commitment being priced. 2.1 the challenges of pricing For many organizations, pricing includes a remarkably complex set of decisions. While most companies have a good idea of the list prices they have established for their products, they are often unclear on the prices that customers are actually paying. A multitude of different dis- counts, adjustments, and rebates are often applied to each sale. For this reason it is critical to distinguish between the list price of a good and its pocket price—that is, what a particular cus- tomer ends up actually paying. The list price is generic, while the pocket price may be differ- ent for each customer. The price waterfall was introduced by McKinsey and Company as a graphical way of illustrating the discounts that occur between the list price of a good and its pocket price. A consumer package goods (CPG) example is shown in Figure 2.1. In this case, there are 12 price reductions or discounts applied between the list price and the pocket price. These include an 8% competitive discount, 3% sales special, 1% exception deal, and so on, 100 8 3 1 2 2 2 Average “Pocket discount” of 29% 82 2 3 1 2 2 1 71 Percent Key Competitive Sales Exception Quantity Terminal Direct Invoice Cash Annual Product Coop Special Freight Pocket dealer discounts specials deals shipment allowance factory price discounts volume bonus advertising promos price price allowance shipment bonus discount Figure 2.1 Price waterfall for a consumer package goods (CPG) company. Source: Figure courtesy of Mike Reopel of A. T. Kearney. 20 introduction to pricing and revenue optimization down to a 1% freight allowance. The net result is that the pocket price for this customer is 29% less than the list price. The price waterfall illustrates quite neatly that the pocket price paid by an individual cus- tomer is often not the result of a single decision, but the cumulative result of a series of deci- sions. In fact, for the majority of companies, many discounts are the results of independent decisions made by different parts of the organization, without consistent measurement or tracking. The competitive discount might have been authorized by the regional sales man- ager, while the product bonus was determined as part of a general marketing program and the freight allowance was given by the local salesperson in response to a last-minute call by the purchaser. As a result, no one is in charge. No one in the organization is responsible for the fact that the discount offered to this customer was 29% while that offered to another was 18%. In fact, not only is no one in charge, it is often remarkably difﬁcult to determine what the pocket price paid by a particular customer even is. As Michael Marn and Robert Rosiello put it: The complexity and volume of transactions tend to create a smoke screen that makes it nearly impossible for even the rare senior managers who show an interest to understand what is actually happening at the transaction level. Management information systems most often do not report on transaction price performance, or report only average prices and thus shed no real light on pricing opportunities lost transaction by transaction. (Marn and Rosiello 1992, p. 86) Without a consistent process of analysis and evaluation, the probability that a particular pocket price maximizes customer proﬁtability is like the probability that a blindfolded dart player will hit a bulls-eye—not zero, but not very high. In fact, the situation can be even worse. Sophisticated buyers often understand a seller’s pricing process better than the seller does himself. A sophisticated buying department, faced with a price waterfall such as that shown in Figure 2.1, would quickly learn how to “divide and conquer” in order to obtain the lowest pocket price. The buyer’s procurement agent will call the local salesperson to get additional concessions, the senior salesperson to get relief from strict interpretation of vol- ume purchase agreements, and even invoicing to get payment term changes. Smart buyers will detect a disorganized or dispersed pricing organization and play it to their advantage. Management attention is often heavily concentrated on invoice prices or list prices. However, the price waterfall illustrates that the majority of important pricing adjustments often take place after the invoice price and certainly after the list price. A typical trucking company will sell less than 5% of its business at list price—all the rest involves discounting. Yet, in many cases, management will spend long hours preparing and analyzing list prices, despite the fact that list prices ultimately have little or no relationship to what most of their customers will be quoted, since all the important action occurs in the discounting. As Fig- ure 2.1 shows, even companies that focus on the invoice price are still missing much of the important action. In this example, 11 points of discount occurred after the invoice price. The distribution of pocket-price discounts given by a CPG company to its various cus- tomers over a year is shown in Figure 2.2. In this case, 9% of the customers were receiving a discount of greater than 40%, while 16% were receiving discounts between 35% and 40%, introduction to pricing and revenue optimization 21 30 25 Percent of total volume 20 15 10 5 0 40 35 30 25 20 15 10 5 List Discount to list price (%) Figure 2.2 Pocket-price distribution for a consumer package goods company. Source: Figure courtesy of Mike Reopel of A. T. Kearney. and only 3% were paying list price. This distribution represents a fairly typical spread of discounts for the CPG industry. This distribution in itself does not tell us anything about the quality of the pricing decisions being made by the company. However, it immediately demonstrates two facts. 1. The item being sold is not a commodity. The distribution of pocket prices means that customers are willing to pay a wide range of prices for the item. 2. Only 3% of customers bought at list price. For this item, setting list price is not the critical PRO decision. Rather, list price is being set high and discounts are being used to target prices to individual customers. The key decisions are what discounts to offer each customer. It should be stressed that the existence of a pocket-price distribution such as the one shown in Figure 2.2 does not by itself say anything about the quality of the pricing decisions. A company practicing sophisticated PRO may also show a wide distribution of prices. After all, PRO is based on offering different prices to different customer segments. The question is: Is the pocket-price distribution the result of a conscious corporate process based on sound analysis, or is it the result of an arbitrary process? A key measure of the quality of PRO decision making is the extent to which the pocket- price correlates with customer characteristics that are indicators of price sensitivity. For example, many companies believe they need to offer higher discounts to their larger cus- tomers. To the extent that larger customers have higher sensitivity to price, this is a sensible policy. In this case, a seller might expect discount levels as a function of customer size to fall within a band something like that shown in Figure 2.3A. However, the actual mapping of discount level against customer size for the CPG company is shown in Figure 2.3B. The cor- relation of discount to customer size was only about 0.09 for this company—statistically, indistinguishable from random. Tools such as the price waterfall in Figure 2.1 and the pocket-price distribution histo- gram in Figure 2.2 are useful to help companies assess the current state of their pricing. The 22 introduction to pricing and revenue optimization Expected ﬁt Actual ﬁt R 2 0.09 25 25 20 20 Percent discount Percent discount 15 15 10 10 5 5 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Sales ($ millions) Sales ($ millions) A B Figure 2.3 Correlation of discount with customer size— consumer package goods example. Part A shows management expectations; part B shows the actual distribution. Source: Figure courtesy of Mike Reopel of A. T. Kearney. price waterfall can identify how pricing responsibilities are dispersed across the organization and where sophisticated buyers may be utilizing “divide and conquer” techniques to drive higher discounts. The pocket-price histogram can give an idea of the breadth of discounts being given to customers. An analysis such as that given in Figure 2.3 can show the extent to which discounts correlate to customer characteristics such as size of customer, size of ac- count, and mix of customer business. This type of presentation often forms part of a prelim- inary diagnostic analysis and can be used to illustrate the need for better pricing decisions. 2.2 traditional approaches to pricing Pricing and revenue optimization incorporates costs, customer demand (or willingness to pay), and the competitive environment to determine the prices that maximize expected net contribution. Other approaches to pricing tend to weigh one of these three aspects more than the others, as shown in Table 2.1. Cost-plus pricing calculates prices based on cost plus a standard margin. Market-based pricing bases prices on what competitors are doing. Value- based pricing sets prices based on an estimate of how customers “value” the good or service Table 2.1 Alternative approaches to pricing Approach Based on Ignores Liked by Cost-plus Costs Competition, customers Finance Market based Competition Cost, customers Sales Value based Customers Cost, competition Marketing introduction to pricing and revenue optimization 23 being sold. The Finance Department tends to like cost-based pricing because it guarantees that each sale produces an adequate margin, which seems ﬁscally prudent. The Sales Depart- ment tends to like market-based pricing because it helps them sell against competition. The Marketing Department is often the natural supporter of pricing according to how customers value a product. 2.2.1 Cost-Plus Pricing Cost-plus pricing is perhaps the oldest approach to setting prices and still one of the most popular. It has a compelling simplicity— determine the cost of each product and add a per- centage surcharge to determine price. The surcharge is often calculated to reﬂect an alloca- tion of ﬁxed costs plus a required return on capital. It may also simply be based on tradition or a rule of thumb. For example, a common rule of thumb in the restaurant industry is “Food is marked up three times direct costs, beer four times, and liquor six times.” 1 The cost-plus pricing approach appears to be objective and defensible. If all competitors in a market have similar cost structures, it would appear to be a reasonable way to ensure consistency with the competition. Finally, it gives the appearance of ﬁnancial prudence. Af- ter all, if all of our products are priced with the right surcharge, the company is guaranteed to make back the cost of production plus ﬁxed costs plus the required return on capital. Everybody, including the shareholders, should be happy. It is not surprising that cost-based pricing, with its dual appeals to objectivity and ﬁnancial prudence, often appeals to the Fi- nance Department. The major drawback of cost-plus pricing is widely recognized: It is an entirely inward- focused exercise that has nothing to do with the market. Calculating prices without any ref- erence to what customers might (or might not) being willing to pay for your product is an obvious folly. Furthermore, it does not support price differentiation—the ability to charge different prices to different customer segments—which is at the heart of pricing and revenue optimization. Another problem with cost-plus pricing is that the costs used as its basis are often no- where near as “objective” as they seem (and as the Finance Department may believe them to be). The calculation of the variable and ﬁxed costs involved in the production of a com- plex slate of products involves innumerable subjective judgments. Furthermore, all of the “hard” cost numbers available to the organization are based on historical performance— production costs in the future may be widely different as the mix of business changes and as production efﬁciency changes. Basing pricing decisions strictly on “costs” plus a sur- charge can lead to highly distorted prices driving lower-than-expected results. This can yield results that often seem very puzzling, since the prices were based on seemingly objec- tive costs. Cooper and Kaplan (1987) discuss several real-world examples of this effect. Given these drawbacks, it should not be surprising that experts are uniformly harsh on cost-plus pricing. Nagle and Holden (1994, p. 3) decry the “cost-plus delusion” and state that the “problem with cost-driven pricing is fundamental.” Dolan and Simon (1996, p. 38): “Cost-plus pricing is not an acceptable method.” 24 introduction to pricing and revenue optimization Lilien, Kotlar, and Murthy (1992, p. 207): “Does the use of a rigid, customary markup over cost make logical sense in the pricing of products? Generally, the answer is no.” Despite near uniform condemnation, cost-based pricing is surprisingly resilient. A 1984 survey of German industry found that about 70% of the companies used cost-based pricing in some form.2 Other surveys have routinely found that up to 50% of businesses use cost- based pricing in the United States. Even in the age of e-commerce, cost-based pricing is alive and well: A 2002 survey of members of the Professional Pricing Society showed that 22% used cost-based pricing to price on the Internet.3 Given that this was a survey of a sophisti- cated group of pricers, it is likely that the actual percentage is even higher. 2.2.2 Market-Based Pricing Market-based pricing means different things in different contexts. We use it to refer to the practice of pricing based solely on the prices being offered by the competition. It is com- monly applied by smaller players in situations in which there is a clear market leader—for example, a small cola brand might set its price based on the price of Coca-Cola. It is, of course, also the practice in pure commodity markets, such as bulk chemicals, or stocks, in which offerings are completely identical and there is rapid, perfect communications of transaction prices. In this case, there is no “pricing decision” per se—all companies take the price as given and adjust their production accordingly. For a commodity, there is no alter- native to “market-based pricing.” Market-based pricing can also be an effective strategy for a low-cost supplier seeking to enter a new market. For example, Alamo Car Rental started as a low-cost rental car com- pany targeting the price-sensitive leisure market. Alamo’s initial strategy was to ensure they were always priced at least $1.00 lower than both Hertz and Avis on the reservation system displays used by travel agents. This strategy was effective at meeting the strategic goal of rapid growth and penetration of the leisure market. While market-based pricing is appropriate in a commodity market, for small players in a market dominated by a large competitor, and as a way to drive market share, it is often used in cases where it is less appropriate. At its most extreme, it means letting the competition set our prices. Slavishly following competitive prices does not allow us to capitalize on the chang- ing value perceptions of customers in the marketplace. Furthermore it does not allow us to capitalize on the differential perception that customers hold of us versus the competition. We should charge a higher price to customers who value our product or brand more highly. Monitoring competitive prices and making sure we maintain a realistic pricing relationship with key competitors is always important—but we also need to adjust our position relative to our competitors to reﬂect current market conditions if we want to maximize proﬁtability. 2.2.3 Value-Based Pricing Like market-based pricing, value-based pricing (or value pricing) means different things in different contexts. In its broadest sense it refers to the unexceptional proposition that price should relate to customer value. In its narrowest sense, it is sometimes used as a synonym introduction to pricing and revenue optimization 25 for personalized or one-on-one pricing, in which each customer is quoted a different price based on her value for the product being sold. We use it to refer to the belief that customer value should be the key driver of price.4 Historically, value-based pricing usually referred to the use of methodologies such as customer surveys, focus groups, and conjoint analysis to estimate how customers value a product relative to the alternatives, which is then used to determine price. This type of value-based pricing is employed most frequently for consumer goods— especially when a new product is being introduced. There is absolutely nothing wrong with the basic idea behind value pricing. If we are a monopoly and we can determine the value each customer places on our product and charge them that value (assuming it is higher than our incremental cost) and not worry about arbitrage or cannibalization (or about being regulated), then that is what we should do in order to maximize proﬁt.5 This approach to pricing has a serious drawback: It is im- possible. There is no way to discern individual customer value for a product at the point of sales. The possibility of arbitrage and cannibalization almost always limits the ability to charge different prices for different products. Finally, competitive pressure means that companies almost always have to price lower than they would like to any group of customers. The competitive restriction on value-based pricing is worth emphasizing. There is a great difference between the “value” that a potential buyer might place on our product in isola- tion and what we can actually get that customer to pay in the market. A customer may value our product or services highly, but he also has alternatives. For example, management con- sulting ﬁrms routinely discuss changing from cost-based pricing (hours worked times rate per hour) to value-based pricing, under the belief that “a good consultant could boost earn- ings using a value-based model.” 6 Yet, less than 5% of consultants use value-based pricing. Why should this be? Consider a brilliant management consulting organization that can provide services to a client that will lead to improved proﬁtability of $2 million yet only cost $500,000 to provide. If the consulting company has a true monopoly, it should be able to close the deal at $1.95 million, leaving the client $50,000 ahead. But what if there is another, slightly less brilliant consulting company with the same cost structure that can provide sim- ilar services that would lead to improved proﬁtability of only $1.5 million? That company could counterpropose a project at $1.4 million, which would be a better deal for the client, since it would leave them $100,000 ahead. The upshot is that competition can severely restrict the ability of a company to “value price” even when the competition is offering an inferior product. Even the existence of an inferior substitute will mean that a company cannot charge full value. 2.2.4 Summary Cost-based pricing, market-based pricing, and value-based pricing are “purist” pricing approaches. In reality, most companies are not purists. While they may have a dominant philosophy, they do not use any one approach 100% of the time and will modify their ap- proach to achieve different goals. According to Eric Mitchell of the Professional Pricing Society, when Xerox wanted to increase market share, they would put the pricing function 26 introduction to pricing and revenue optimization under Sales. When they wanted to increase proﬁts, they would move it into the Finance Department.7 Other companies are less disciplined—their approach to pricing may change with the “ﬂavor of the month”: market-based when the emphasis is on market share, value- based when “focusing on the customer” comes into vogue. Vacillating among pricing approaches is actually better than strict devotion to one ap- proach. Any company that sticks tenaciously to any one of the three pure approaches would likely ﬁnd itself in deep trouble very quickly. What is often seen in reality is a hybrid— companies espouse a particular philosophy but use pieces of all three, supplemented by a considerable amount of improvisation. The upshot is pricing confusion—there is rarely a consistent justiﬁcation or approach applied across all pricing decisions. 2.3 the scope of pricing and revenue optimization Pricing and revenue optimization provides a consistent approach to pricing decisions across the organization. This means that a company needs to have a clear view of all the prices it is setting in the marketplace and the ways in which those prices are set. This deﬁnes the scope of pricing and revenue optimization. 2.3.1 The PRO Cube The goal of pricing and revenue optimization is to provide the right price, for every product to every customer segment through every channel and to update those prices over time in response to changing market conditions. The three dimensions of pricing and revenue optimization can be illustrated in a cube, as shown in Figure 2.4. Each element within the cube represents a combination of product, channel, and customer segment. Each element (or cell) has an associated price. For example, one element might be: medium-size turbines sold to large customers in the northeast via the direct sales channel Another element might be: Replacement gears sold to small companies via online sales In theory, each cell within the PRO cube could correspond to a different price. In prac- tice, some cells may not be meaningful. Some products may not be offered through some channels, for example. It is also the case, of course, that the prices within the PRO cube will not always be independent of each other. Our ability (or desire) to charge different prices through different channels may be constrained either by practical considerations or by strategic goals. If we want to encourage small customers to purchase online rather than through our direct sales channel, we might institute a constraint that says that the online price for small customers for all products must be less than or equal to the direct sales chan- introduction to pricing and revenue optimization 27 l ne an Ch Customer type Product Figure 2.4 Dimensions of the pricing and revenue optimization cube. nel price. These considerations are among the business rules that must be considered in the pricing and revenue optimization process described in Section 2.4. Companies may offer even more prices than the PRO cube would imply. Certain prod- ucts might be subject to tiered pricing or volume discounts. Or a company might offer bundles of products at different promotional rates or discounts that are not available for individual products. Each of these bundles or quantity combinations can be treated as an additional “virtual product” in the PRO cube. The PRO cube is a useful starti

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