What is a cynic? . . . A man who knows the price of everything and the value of nothing.
—Oscar Wilde, Lady Windermere’s Fan
This is a book about pricing—specifically, how companies should set and adjust their prices to maximize profitability. It takes the view that pricing decisions are commonplace, that they are often complex, and that they are critical determinants of profitability and revenue. Despite this, pricing decisions are often badly managed—or simply unmanaged. While most companies have fairly good prices in place most of the time, few have the processes and capabilities needed to ensure that they have the right prices in place for all their products, for all their customers, through all their channels, all the time. This is the goal of pricing and revenue optimization.
Pricing and revenue optimization is a tactical function. It recognizes that in many cases, 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 to establish a general price position in a market. While strategic pricing is concerned with how a product should in general be priced relative to the market, pricing and revenue optimization is concerned with determining the prices that will be in place tomorrow and next week. Strategic pricing sets the constraints within which pricing and revenue optimization operates. For example, a luxury brand may decide that it will not discount its products more than a certain percentage in order to maintain its luxury image. This strategic pricing decision could be implemented as a maximum-discount constraint within a pricing and revenue management system.
One of the distinguishing characteristics of pricing and revenue optimization is its use of analytical techniques derived from management science, statistics, and machine learning. The use of these techniques to set prices in a complex, dynamic environment is about 35 years old. One of the first applications of pricing optimization was revenue management, which was first used by the passenger airlines in the 1980s. Since then, the rapid development of e-commerce, the availability of customer data through customer relationship management (CRM) systems, and the increasing dynamism of online commerce have led to the adoption of pricing optimization in many other industries, including automotive, retail, telecommunications, financial services, and manufacturing. A multitude of software vendors provide “price optimization” or “demand management” or “revenue management” solutions focused on one or more industries. Pricing and revenue optimization has become a core competency within a wide range of industries including airlines, hotels, and online and offline retail, and it has been widely adopted across a range of other industries. Pricing and revenue optimization has also been a fruitful area of academic research.
In this chapter, we begin by looking at some historical context for pricing and revenue optimization. In particular, I provide some perspective on how pricing went from being an obscure and mysterious art within many companies to becoming the subject of intense scrutiny and analysis. This chapter argues that the pricing problem has become increasingly difficult and dynamic and is likely to become even more so in the future. It argues further that improving 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.
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 with an emphasis on both fairness and stability: Plato advocated severe penalties for any merchant who asked for more than one price for a good on the same day, and he believed that prices should be set by law at a rate that provided merchants a reasonable profit.1 The general belief that prices should be fair and not set by the merchant to maximize profit survived the collapse of the Roman Empire and the rise of Christianity—many sermons inveighed against the sin of charging unfair prices to gain excessive profits.2
The problem of pricing did not exist on a large scale until modern market economies began to emerge in the West in the seventeenth and eighteenth centuries. With the emergence of market economies and a growing middle class with more disposable income, prices were allowed to move more freely—untethered to the traditional concept of value. Speculative bubbles such as tulipomania in the Dutch Republic in the 1630s, in which the prices 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 collapsed amid general scandal, fed a sense of anxiety and the belief that prices could somehow lose touch with reality. Furthermore, for the first time, large numbers of people could amass fortunes—and lose them—by buying and selling goods on the market. Questions 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—usually credited to Adam Smith—was that the price of a good at any time in an unregulated economy is not based on any intrinsic value but rather on the interplay of supply and demand. This was a major intellectual breakthrough—on par in its own way with the Newtonian theory of the clockwork universe and Darwin’s theory of evolution. In essence, 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 is all there is to it—intrinsic value, cost, and labor content do not enter directly into the equation. Of course, these and many other factors enter indirectly into pricing—sellers would not last long selling goods below cost, and a buyer will react to a price based on the value she places on the item—but these are 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. Just so, the value that buyers place on different goods changes 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 actual prices out in the marketplace, floating freely, based on the willingness of sellers to sell and buyers to buy.
While neoclassical economics solved the problem of the origin of price, it raised as many questions as it answered. In particular, if prices are not tied to fundamental values—if they have no anchor—why do they show any stability at all? Under normal circumstances, prices for most goods are reasonably stable most of the time. If prices are based only on the whims of buyers and sellers, why is the price of bread not subject to wild swings like the Dutch tulip market in 1637? Why does milk not 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 strive to allocate resources across the entire economy?
One of the great achievements of twentieth-century economics was to show mathematically how a largely unregulated economy could function: that an economy consisting of individuals who supply their labor in return for wages and maximize the utility of their earnings as they buy goods from firms who seek to maximize profitability can be remarkably stable and efficient. Under certain assumptions, this type of decentralized economy can be shown to be at least as efficient as any centrally planned economy at producing and allocating goods that satisfy consumers. 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 it. A seller offering an item for less than it cost would go bankrupt. 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 across the economy, prices equilibrate and change only as a result of exogenous factors or changes in resource availability, taxation, monetary policy, or consumer tastes.
This view of the world is based in part on the assumption that most markets are perfectly competitive, where the concept 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 homogenous 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. (Pearce 1992, 327–328)
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 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 in a sector that sellers enjoyed higher profits than the rest of the economy, more sellers would enter that sector, 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 a pricing manager but by the interplay of supply and demand—in fact, most financial 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 pricing and revenue optimization: the market sets the price.
As any shopper can tell you, most of the real world is much 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 November 2019. For a half gallon of nonorganic milk, prices ranged from a low of $2.69 to a high of $4.00—a variation of $1.31, or 49%. Furthermore, the price varied by $1.10 even for two stores on the same block (Duane Reade and Amsterdam Gourmet). How could this be? Why would anybody buy milk at a high price when they could walk less than a block and save $1.10? Why do arbitrageurs not buy all the milk at the lower price and sell it at the higher?3
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 because suppliers of the same (or similar) products often charge different prices. Furthermore, there are other ways than walking to the next store 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 online, or try to negotiate a lower price. In fact, not only do prices vary between sellers, but a single seller will sometimes sell the same product to different customers for different prices!
TABLE 1 .1
This provides the setting for pricing and revenue optimization—sellers have scope to adjust prices, and the right price to charge at a given time is not usually obvious. In this situation, pricing becomes a decision rather than a fait accompli. If, furthermore, a seller has many prices to set and these prices are subject to frequent change, then the use of a computerized system to manage pricing is almost always required. Pricing and revenue optimization can be viewed as an approach that uses mathematics to set and update multiple prices in a dynamic environment. The approaches used in pricing and revenue optimization are drawn from management science, operations research, marketing science, statistics, economics, and, increasingly, machine learning.
A further impetus to the growth of pricing and revenue optimization is that companies increasingly need to make pricing decisions more and more rapidly to respond to competitive actions, market changes, or their own inventory positions. Sellers often no longer have the luxury to perform market analyses or extended spreadsheet studies every time a pricing change needs to be considered. There is a premium on speed. While there has been a general acceleration of all aspects of business, 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 optimization (PRO) decisions—has been driven by four trends.
• The success of revenue management in the airline industry provides an example of how pricing and revenue optimization can increase profitability in a real-time pricing environment.
• Increasingly inexpensive data storage and computation makes it feasible to run sophisticated algorithms to calculate and update prices quickly.
• The rise of e-commerce necessitates 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.
• The success of machine learning provides new ways to use data to support effective decision making.
Because of their importance to the development of pricing and revenue optimization, we spend a little time considering 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 (now more commonly known as revenue 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 8. For now, the importance of the story is in the publicity it garnered. American Airlines featured its revenue management capabilities in its annual report, emphasizing that it was an application of advanced mathematics. The team that developed the system won a prestigious prize for the best application of management science in 1991. American Airlines’ revenue management system was widely touted as an important strategic application of mathematics (Anderson, Bell, and Kaiser 2003), and the tale of American using its superior capabilities to defeat PeopleExpress was the centerpiece of a popular business book (Cross 1997).
Not surprisingly this publicity resulted in widespread interest. Companies began to investigate 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 successful program (Leibs 2000). Vendors arose selling revenue management software systems, and consultants offered to help companies set up their own programs. Over the next decade, revenue management spread well beyond the passenger airlines to fields such as hotels and rental cars that had similar problems of managing constrained and perishable capacity.
Under its strictest definition, revenue management has a fairly narrow field of application. In particular, revenue management is applicable when the following 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 industries 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 that the airlines developed. These systems allow airlines to establish 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 fare classes should we open and close?” but “What price should we offer now for each of our products to each market segment through each channel?” The difference is subtle and may not even be visible to consumers, but it leads to major differences in system design and implementation.
Many companies are understandably wary about adopting revenue management programs, protesting that “we are not an airline.” In general, this is the right view—the algorithms behind airline revenue management do not transfer directly to most other industries. However, the experience of the airlines contains several important lessons.
• Pricing and revenue optimization can deliver more than just short-term profitability benefits. Revenue management enabled American Airlines to meet the challenge posed by PeopleExpress. It also meant the difference between survival and bankruptcy 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. At that 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 forecasted supply and demand for each car type and 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, 107)
• E-commerce both necessitates and enables pricing and revenue optimization. The airlines pioneered electronic distribution—their computerized distribution systems, 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 information instantaneously transmitted worldwide. In effect, the airlines were wrestling with the complexities of e-commerce well before the arrival of the internet. As the internet becomes an ever more important channel for sales, the need to continually monitor demand and update prices will only increase.
• Effective segmentation is critical. The key to the success of revenue management in the airline industry is the ability of the airlines to segment customers between early-booking leisure passengers and late-booking business passengers. Note that, for the airlines, this segmentation was achieved not by direct discrimination—that is, trying to charge a different fare based on demographics, age, or other customer characteristics—but via product differentiation, creating different products that appealed to different 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 6.
At heart, airline revenue management systems are highly sophisticated opportunity cost calculators. 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 irrelevant in 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 optimization.
1.1.2 The Evolving Computational Environment
A major motivation for the development of more sophisticated pricing and revenue optimization systems has been the explosion of data available to companies coupled with the continued precipitous decline in the cost of both computation and data storage. Four developments were particularly important in this regard.
• Between 1985—the time that airlines began implementing revenue management systems—and 2019, both the cost of computation and the cost of storage dropped by a factor of about 10,000,000.4 This incredible rate of technological progress has had enormous implications not just for pricing and revenue management but for society as a whole.
• Starting in the early 1990s, companies began concerted efforts to organize scattered customer data into a usable format (customer relationship management) as well as to break down corporate data silos to make information available across the organization (enterprise resource planning). The idea behind enterprise resource planning (ERP) is to provide a corporate information backbone that supports all business users with consistent and timely data from a single source. Ideally information will then flow freely among business processes with data islands and fiefdoms eliminated. While ERP efforts have not been universally successful, and many organizations still suffer from data fiefdoms, substantial progress has been made by many organizations toward the goal of retaining all pertinent data and having them readily available to all parts of the organization when they are needed.
• The move from on-premises computing to cloud computing has made information technology more flexible and reduced costs even further. When computing was primarily on-premises, information technology (IT) departments needed to make vast investments in computers and the technology and personnel to maintain them. With cloud computing (also called computing as a service), companies pay to obtain IT services from a third party that purchases and maintains the needed hardware. Cloud computing uses economies of scale to reduce the cost of computing services below what many companies could achieve in-house. Furthermore, cloud computing converts IT costs from a combination of fixed and variable costs to variable cost only. This provides flexibility for companies to adjust their purchases of computational services to their changing demands without the need to maintain extensive internal IT departments.
• The development of agile methodologies for software development along with improved supporting software tools and languages has made development and testing of pricing approaches much easier at many organizations than in the past. Historically, implementing a new approach to pricing or pricing analytics usually required a major investment in software development and implementation often extending over a year or more. This meant that new approaches had to be rigorously tested and a strong business case established before a company would be willing to invest millions of dollars and years of development time in a new approach. Agile development and supporting tools have made it much easier to implement new ideas at less cost and in a much shorter time frame. Furthermore, it is now often easier for companies to revert to a previous approach if a new approach does not work as planned.
It is worth emphasizing that these trends have affected different companies to vastly different degrees. Long-established companies are often saddled with legacy systems that are hard to replace or modify and that can inhibit adoption of new approaches. The airlines represent a classic example—many of their IT systems are built around the concept of a fare class. Each fare class can have a different fare and airlines can open or close fare classes in order to set different prices according to algorithms described in Chapter 9. However, this is not true dynamic pricing because prices are limited to the preset fares. The concept of a fare class is embedded so deeply into many airline systems from reservations to distribution to accounting that it can be prohibitively difficult for any single airline to move to a pure dynamic pricing system even if it wanted to do so.5 Similarly, television networks and their customers, advertising agencies, have developed extensive systems to support a system of up-front pricing and spot pricing that dates from the early days of the commercial television industry. While this system seems outdated, it persists in part because both ad agencies and networks have heavily invested in software systems based on the current market structure.6
By contrast, many more recently founded tech companies have implemented more modern, flexible information technology infrastructure that allows them to move more quickly in introducing new pricing approaches. For example, ride-sharing apps such as Uber and Lyft are not restricted to a predefined set of fares and can adjust prices much more flexibly and dynamically than the airlines. The same is true of many online retailers such as Alibaba, Amazon, and Walmart.com. There is irony in this situation—the global distribution infrastructure that the airlines spent billions of dollars to implement in the 1960s and 1970s was revolutionary for the time, but in some ways this once pathbreaking infrastructure is now a barrier to innovation.
1.1.3 The Rise of E-commerce
By the late 1990s, the internet was widely predicted to be a revolutionary and transformative technology that would change everything. The fact that the internet drives a 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: The internet would bring about the world of perfect competition, in which sellers lose control of prices. This was part of the vision behind Bill Gates’s concept of “friction-free capitalism” (Gates 1996, 180). However, reality has turned out to be quite different. Studies consistently show that most online buyers actually do little comparison shopping—for example, a McKinsey study showed that 89% of online book purchasers buy from the first site they visit, as do 81% of music buyers. As a result, online prices often vary considerably, even for identical items. Table 1.2 shows the base price and shipped price for 13 online vendors as well as a list and an in-store price for Stephen King’s novel The Institute; the prices were gathered using an online shopping application.7 Note that the delivered price varies by almost $19.00 across the vendors, with none of the 13 vendors offering the book at the same delivered price. Note also that the two largest online booksellers—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 be perpetuating rather than eliminating price variability!
The resemblance between the distribution of online book prices in Table 1.2 and the distribution of milk prices in Table 1.1 is not coincidental. It shows that, just like any other channel, the internet supports price differentials. 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—should it be 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 or even millions of items and a need to update frequently, and the full magnitude of the pricing problem faced by online merchants becomes clearer.
While one group of analysts was predicting that the internet would eliminate price differentials 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 website would immediately be identified, and, based on her past buying patterns, a pricing engine would calculate personalized prices reflecting an estimate of her willingness to pay. While various online sellers have been rumored at various times to be practicing individualized pricing—for example, the Amazon DVD example in Chapter 14—in reality pure market-of-one pricing is not widely practiced, and there are several reasons to believe it will never become standard practice. For one thing, there is strong buyer resistance to pricing discrimination that is perceived as unfair or arbitrary. People seem no more inclined to accept online price discrimination than they are willing to accept variable pricing at the time of checkout based on a clerk’s estimate of their willingness to pay. (Chapter 14 digs more deeply into customer reaction to pricing that seems unfair.) The transparency of the internet means that whatever online pricing system a company adopts, the details will soon be widely known by customers. Price differentiation by online sellers is always rapidly discovered. As a result, market-of-one pricing has not taken over the internet, and when price differentiation is employed, it is usually within careful guardrails.
NOTE: All prices for new purchase.
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Given that the internet has neither driven prices to their lowest common denominator nor led to real-time personalized pricing, can we conclude that it has no impact on pricing? Not at all. On the contrary, e-commerce has been a major motivator for companies to improve their pricing capabilities. Four specific characteristics of internet commerce increase the urgency of pricing and revenue optimization.
• The internet increases the velocity of pricing decisions. Many companies that changed prices once a quarter or less now find they face the daily—or even hourly—challenge of determining what prices to display on their website or to transmit to e-commerce intermediaries. Many companies are only now beginning to struggle with this increased price velocity—and it is likely that the pace of price changes will continue to accelerate. For example, one article estimated that in 2018 Amazon changed 2.5 million prices every day (Mehta, Detroja, and Agashe 2018). All indications are that online pricing is likely to become even more dynamic in the future.
• 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 information on not just who bought what but also who clicked on what, and who looked at what and for how long. This information can be captured and analyzed by companies both to support cross-selling and up-selling and to understand customer behavior and segmentation.
• The internet provides the ability to experiment rapidly and effectively with pricing alternatives and different pricing models. As McKinsey pricing consultants point out, “Traditional price-sensitivity research 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” (Baker, Marn, and Zawada 2001, 121). Many e-commerce companies experiment frequently with different prices and different pricing approaches.
• 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. A 2019 Cox Automotive survey found that car buyers spend 61% of their shopping time online rather than in a dealership and 80% visited at least one third-party site (Cox Automotive 2019). This means that more and more buyers arrive at a dealership with full information on what is available and what the dealer paid for a car. In this environment, sellers need to be able to use intelligent targeted pricing to remain profitable.
Because the internet has had such an important influence on pricing, we pay special attention to the problems of optimizing prices on the internet throughout the book.
1.1.4 Machine Learning Reaches Maturity
One of the consequences of the rapid evolution of the computational environment described in Section 1.1.2 was a revival of interest in and rapid development of a number of computational approaches that once went under the name of artificial intelligence (AI). Artificial intelligence is a somewhat vague term used for computational agents that can perceive the environment and make decisions in order to attain a goal. Artificial intelligence had experienced a surge of interest and funding from both investors and government research agencies in the mid-1980s into the early 1990s. At the time, the results failed to live up to the absurdly high expectations set by the technology’s advocates, and both interest and funding waned for more than a decade—the so-called AI winter. The fortunes of AI began to revive around the turn of the millennium as increased computational capabilities enabled faster and more efficient implementation of existing algorithms and stimulated the development of new algorithms.
The subfield of artificial intelligence that is most relevant to pricing and revenue optimization is machine learning, which can be defined as the ability of computers to detect patterns in data and make recommendations from data with a minimum of domain-specific modeling.8 Machine learning is now commonplace within most large organizations. It has proven to be extremely effective at supervised learning tasks that involve classifying objects into different categories. A widely publicized example is Google’s Inception, which can identify objects, people, and animals in photographs with extremely high levels of reliability. Machine learning approaches have proven equally or even more proficient than traditional methods at more prosaic tasks such as evaluating credit risk or determining whether a transaction is fraudulent. Machine learning has become the weapon of choice for such applications and is increasingly supplanting previous approaches.
Because of this success, machine learning is often offered as a panacea for any complex business decision. For pricing, this sometimes manifests itself in the view that a neural net should simply be trained to determine the best price for any type of product under any circumstances and then turned loose to set and update prices over time. Unfortunately, things are not quite so simple. In particular, training a neural net generally requires observations that span the range of combinations of possible decisions and environmental factors with enough observations to enable statistically reliable recommendations of price for all future combinations that might occur. For most companies, this is still infeasible because of the high dimension—the large number of variables—of the pricing problem. But if we combine machine learning with some straightforward and plausible assumptions about how people respond to prices—for example, higher prices lead to lower demand—we can develop approaches that are both feasible and reasonably efficient.
All four factors that have accelerated the field of pricing and revenue optimization—the success of revenue management, the evolving computational environment, the rise of e-commerce, and the increasing sophistication of machine learning—point in the same direction, toward a future in which pricing will be increasingly dynamic and supported by a wealth of information and sophisticated algorithms. 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 obsolete before they can be completed because the world moves too quickly. Automated pricing and revenue optimization systems will increasingly be required to deal with the speed and complexity of pricing decisions.
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 management of pricing is the fastest and most cost-effective way to increase profits” (Marn and Rosiello 1992, 85). So concluded a pioneering study by McKinsey and Company, which concluded that a 1% improvement 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 produce operating improvements of 7.8%, 3.3%, and 2.3%, respectively. This analysis has often been replicated: a 2018 book gave an example of a case in which a 5% improvement in price would lead to a profit improvement of 50%, while 5% improvements in unit costs, sales volume, and fixed costs would lead to profit gains of 30%, 20%, and 11.5%, respectively (Simon and Fassnacht 2018).9 While other studies have had different results, the overall pattern is clear: improving pricing can have a significant positive impact on profit and may be a more profitable area for investment than cost reduction.
When it comes to the benefits of implementing an actual system, the passenger airlines typically claimed between 8% and 11% revenue increase from the initial implementation of revenue management systems (Smith, Leimkuhler, and Darrow 1992).10 A Harvard Business Review article noted that some retailer early adopters achieved “gains in gross margins in the range of 5 –15%” from the use of optimization-based assortment and pricing optimization systems (Friend and Walker 2001, 138). Similar gains from markdown optimization have been reported elsewhere—an A/B test measured an increase of 6% in revenue from distressed inventory at the fast-fashion retailer Zara (Caro and Gallien 2012). More recently, a 2019 Gartner report noted that “Pricing Optimization and Management” software “has been observed to deliver a rapid return on investment when well-implemented and enthusiastically adopted” (Lewis 2019, 8). The same report noted that vendors report increased revenue of 1–2% and increased margins of 2–10%.11
Putting it all together, we can see there is a strong case for many companies to consider investing in pricing and revenue optimization. Not only is improving pricing already the “fastest and most cost-effective way to increase profits,” but it is gaining in importance as the velocity and complexity of pricing decisions inexorably increase. Furthermore, a new generation of information technology provides the information and algorithmic power needed to analyze and exploit market opportunities. Finally, pricing is often the corporate function that can generate the most return with the least investment.
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 contrasts 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 introduces 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 discusses the mathematical representation of customer response to price. It introduces the idea of a price-response function and shows how a price-response function is based on the distribution of willingness to pay across a population. The chapter presents several common forms of price-response function and shows how the price-response function can incorporate competitive pricing. Chapter 4 shows how price-response functions can be estimated using historical data on prices and demand. It also discusses data-free approaches to estimating a price-response function.
Chapter 5 shows how optimal (profit-maximizing or revenue-maximizing) prices can be calculated given a price-response function. It defines incremental cost and how it influences pricing. The chapter also shows how optimal prices can be calculated using explicit optimization as well as using an adaptive reinforcement learning approach. In addition, Chapter 5 introduces the concept of an efficient frontier as a way to visualize the trade-offs between competing objective functions such as maximizing profit and maximizing revenue or market share.
Price differentiation is one of the key concepts in pricing and revenue optimization. Chapter 6 discusses 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. Chapter 6 discusses how to optimize differentiated prices in the face of potential cannibalization.
Chapter 7 introduces another major theme in pricing and revenue optimization—pricing 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, restaurant, 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. Chapter 7 also introduces the key concept of an opportunity cost—the incremental contribution lost because of a supply constraint. Chapter 7 also gives examples of pricing with constrained supply in several different industries including airlines, ride-share services, electric power, and theater.
Revenue management has been one of the most important and publicized applications of pricing and revenue optimization over the past three 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 of the core techniques are gaining acceptance and use well beyond these industries. Four chapters in this book are devoted to revenue management. The first, Chapter 8, describes the background, history, and business setting of revenue management. Chapter 9 is devoted to capacity allocation, the techniques used to determine which fare classes should be open and which closed at any time for a simple product using only a single product—for example, a single airline flight or a single hotel room night. Chapter 10 extends this analysis to the case of a network, in which an individual product may use many different resources—for instance, multiple connecting flights or multiple-night hotel stays. Chapter 11 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 12, is a popular application of pricing and revenue optimization. In a markdown industry, a merchant has a stock of inventory with a value that 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, to consumer electronics and durables, to theater tickets. Chapter 12 describes basic markdown optimization models and some of the challenges in implementing markdown management in the real world, including the issue of strategic customers who may anticipate the markdown and thus wait for the sale.
Chapter 13 treats customized prices. Customized pricing is common in business-to-business settings where goods and services are sold either through long-term contracts or as part of large individual transactions. In these settings, list prices may not exist or may serve only as guidelines to which discounts are applied. The pricing and revenue optimization challenge is to determine the discount to provide to each customer in order to maximize the expected profitability of the deal. This requires estimating the trade-off between the probability of winning the deal and the marginal contribution if the deal is won.
Chapter 14 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 maximizers of utility and that prices are emotionally neutral signals that guide purchasing decisions. However, both common sense and recent research in behavioral economics have shown that this idea is incomplete at best and misguided at worst. Consumers can care—sometimes deeply—about prices and the way they are presented. In particular, pricing that is perceived as unfair can trigger an emotional rejection. Chapter 14 discusses the implication of consumer irrationality for pricing.
A popular and accessible account of the development of economic thought in the West is Robert Heilbroner’s The Worldly Philosophers (1999). 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 seventeenth-century Dutch Republic. John Carswell’s The South Sea Bubble (2001) is the definitive account of the eighteenth-century British stock scandal. A Conspiracy of Paper (2000), by David Liss, is an entertaining fictional treatment of the bubble.
For a history of pricing in the West (as opposed to what philosophers, theologians, and economists thought about prices) and the emergence of different pricing modalities, see Phillips 2012c.
1. “He who sells anything . . . shall not ask two prices for that which he sells, but he shall ask one” (Plato 2008, bk. 11). “The guardians of the law . . . shall consult with persons of experience, and find out what prices will yield the traders a moderate profit, and fix them” (intro.). Plato not only disapproved of dynamic pricing; he disliked hotel revenue management as well: “But now that a man goes to desert places and builds houses . . . and receives strangers who are in need of the welcome resting-place, and gives them peace and calm when they are tossed by the storm, or cool shade in the heat; and instead of behaving to them as friends, and showing the duties of hospitality to his guests, treats them as enemies and captives who are at his mercy, and will not release them until they have paid the most unjust, abominable, and extortionate ransom—these are the sorts of practises, and foul evils they are, which cast a reproach upon the succor of adversity” (bk. 11).
2. Robert Heilbroner 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” (1999, 23).
3. Milk prices for the same nine stores were reported for May 2002 in the first edition of this book. Milk was cheaper in 2002, with an average price of $1.75 versus $3.23 in 2019, and the organic option was uncommon. However, there was still significant price dispersion in 2002, with prices showing a standard deviation of $0.20. One way to compare the amount of dispersion for distributions with different means is to use the coefficient of variation, which is defined as the standard deviation divided by the mean. The coefficient of variation of (nonorganic) milk prices in 2002 was 11%, while in 2019 it was 16%. This illustrates the general trend for price dispersion to increase as prices increase.
4. The cost factors for computation and storage are from Muehlhauser and Rieber 2014. They have been extrapolated slightly from 2014 to 2019. Other estimates of the decline in costs for storage and computation vary slightly, but all agree that these costs have dropped by a factor of more than 1 million over the period from 1985 to 2019.
5. In October 2019, Business Travel News reported that six airlines were piloting a dynamic pricing system that was built on top of the existing fare-class system as a tentative first step toward true dynamic pricing (Business Travel News 2019). This would suggest that widespread use of true dynamic pricing seems to be far in the future for the industry.
6. For example, I argue elsewhere that “a key element in the persistence of the upfront market appears to be the institutional and human capital infrastructure that has developed around it” (Phillips 2012c). For more on the up-front price/spot price structure in the television advertising industry, see Phillips and Young 2012.
7. I chose the popular novel The Institute for three reasons. First, it was widely available as new from multiple vendors. Second, it was also only available in a single physical edition, ensuring apples-to-apples comparison, and third, as a best seller, it had a large market, which means that changing its price would have a meaningful influence on sales.
8. The definition of machine learning is mine. It emphasizes that machine learning approaches to pricing are distinguished from older approaches by the fact that they require fewer assumptions about the nature of customer response—for example, that some prechosen functional form captures customer response to different prices.
9. Similar calculations with the same general conclusion can be found in many books and articles. The calculations need to be taken with a grain of salt because they typically compare the profit implications of a 1% increase in price with a 1% change in other metrics such as a 1% decrease in cost. This does not reflect the fact that pricing optimization involves both raising and lowering prices.
10. It is difficult to obtain more recent figures because every major airline uses some form of automated revenue management, which means that manual baselines for comparison are no longer available.
11. Since these figures were reported by vendors who have a vested interest in advertising substantial improvements from their systems, they are likely to be biased upward.