THE THEORY AND PRACTICE OF REVENUE MANAGEMENT PDF
Revenue management (RM) is one of the most successful applications in Operations Research. The RM The Theory and Practice of Revenue Management. Authors Introduction. Kalyan T. Talluri, Garrett J. Van Ryzin. Pages PDF. excerpts from our book The Theory and Practice of Revenue Management . Keywords revenue management; dynamic pricing; optimization; demand. Quantity-Based RM.- Single-Resource Capacity Control.- Network Capacity Control.- Overbooking.- Price-based RM.- Dynamic Pricing.- Auctions.- Common Elements.- Customer-Behavior and Market-Response Models.- The Economics of RM.- Estimation and Forecasting.- Industry Profiles.
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Request PDF on ResearchGate | The Theory and Practice of Revenue Management | The Theory and Practice of Revenue Management by Kalyan T. Talluri. Revenue management is a growing field with many theories and techniques the revenue obtained with a practice used in hotel chains today. "Simply put this is an outstanding book. It is the first book to fully articulate the various ways operations research may be applied to revenue management.
What price to ask? Which offers to accept? When to offer a lower price? In terms of business practice, the problems of RM are as old as business itself. In terms of theory, at a broad level the problems of RM are not new either.
Modern economic theory addresses many advanced and subtle demand-management decisions, such as nonlinear pricing, bundling, segmentation, and optimizing in the presence of asymmetric information between buyers and sellers. What is new about RM is not the demand-management decisions themselves but rather how these decisions are made. The true innovation of RM lies in the method of decision making—a technologically sophisticated, detailed, and intensely operational approach to making demand-management decisions.
Introduction 5 This new approach is driven by two complementary forces. First, scientific advances in economics, statistics, and operations research now make it possible to model demand and economic conditions, quantify the uncertainties faced by decision makers, estimate and forecast market response, and compute optimal solutions to complex decision problems.
Second, advances in information technology provide the capability to automate transactions, capture and store vast amounts of data, quickly execute complex algorithms, and then implement and manage highly detailed demand-management decisions. This combination of science and technology applied to age-old demand management is the hallmark of modern RM.
And both the science and technology used in RM are quite new. Much of the science used in RM today demand models, forecasting methods, optimization algorithms is less than fifty years old, most of the information technology large databases, personal computers, Internet is less than twenty years old, and most of the software technology Java, object-oriented programming is less than five years old. Prior to these scientific developments, it would have been unthinkable to accurately model real world phenomena and demand-management decisions.
Without the information technology, it would be impossible to operationalize this science. These two capabilities combined make possible an entirely new approach to decision making—one that has profound consequences for demand management. The first consequence is that science and technology now make it possible to manage demand on a scale and complexity that would be unthinkable through manual means or would require a veritable army of analysts to achieve.
A modern large airline, for example, can have thousands of flights a day and provide service between hundreds of thousands of origin-destination pairs, each of which is sold at dozens of prices—and this entire problem is replicated for hundreds of days into the future!
A similar complexity is found at most large retail chains, which can have tens of thousand of SKUs2 sold in hundreds of stores and over the Web with prices monitored and updated on a daily basis.
The sheer scale and complexity of the decision-making task in these cases is beyond the ability of human decision makers.
And if not automated, the task has to be so highly aggregated and simplified that significant opportunities for incremental gains—on particular products, at particular locations, at specific points in time—are simply lost. The management tasks that are involved—quantifying the risks and rewards in making demand-management decisions under uncertainty; working through the often subtle economics of pricing; accurately interpreting market conditions and trends and reacting to this information with timely, accurate, and consistent real-time decisions; optimizing a complex objective function subject to many constraints and business rules— are tasks most humans, even with many years of experience, are simply not good at.
Models and systems are better at separating market signals from market noise, evaluating complex tradeoffs, and optimizing and producing consistent decisions. The application of science and technology to demand decisions often produces an improvement in the quality of the decisions, resulting in a significant increase in revenues.
Of course, even with the best science and technology, there will always be decisions that are better left to human decision makers. They cannot reason through the consequences of a demand shock, new technologies, a sudden shift in consumer preferences, or the surprise price war of a competitor.
These higher-level analyses are best left to experienced, human analysts. Most RM systems recognize this fact and parse the decision-making task, with models and systems handling routine demand-management decisions on an automated basis and human analysts overseeing these decisions and intervening based on flags or alerts from the system when extraordinary conditions arise. Such man-machine interaction offers a firm the best of both human and automated decision making.
In short, the airline industry. There are few business practices whose origins are so intimately connected to a single industry. Here we briefly review the history of airline RM and then discuss the implications of this history for the field. With this act, the U. Civil Aviation Board CAB loosened control of airline prices, which had been strictly regulated based on standardized Introduction 7 price and profitability targets.
Passage of the act led to rapid change and a rash of innovation in the industry. Established carriers were now free to change prices, schedules, and service without CAB approval.
Large airlines accelerated their development of computerized reservation systems CRSs and global distribution systems GDSs , and the CDS business became profitable in its own right.
The majors developed huband-spoke networks, which allowed them to offer service in many more markets than was possible with point-to-point service but also made pricing and operations more complex. At the same time, new low-cost and charter airlines entered the market.
Many of these upstarts—because of their lower labor costs, simpler point-to-point operations, and no-frills service—were able to profitably price much lower than the major airlines. These new entrants tapped into an entirely new and vast market for discretionary travel—families on a holiday, couples getting away for the weekend, college students visiting home—many of whom might otherwise have driven their cars or not traveled at all.
It turned out quite surprisingly to some at the time that air travel was quite price elastic; with prices sufficiently low, people switched from driving to flying, and demand from this segment surged.
While these developments resulted in a significant migration of pricesensitive discretionary travelers to the new, low-cost carriers, the major airlines had strengths that these new entrants lacked. They offered more frequent schedules, service to more city pairs and an established brand name and reputation.
For many business travelers, schedule convenience and service was and still is more important than price, so the threat posed by low-cost airlines was less acute in the business-traveler segment of the market. Nevertheless, the cumulative losses in revenue from the shift in traffic were badly damaging the profits of major airlines. A strategy to recapture the leisure passenger was needed. Yet, for the majors, a head-to-head, across-the-board price war with the upstarts was deemed almost suicidal; with their much lower costs, airlines like PeopleExpress could earn a profit at the new low prices, while most majors would lose money at a staggering rate.
However, two problems had to be solved to execute this strategy. The scheme would not be profitable if a sale of a low-price seats displaced high-paying business customers. American solved these problems using a combination of purchase restrictions and capacity-controlled fares.
First, they designed discounts that had significant restrictions for purchase: they had to be purchased 30 days in advance of departure, were nonrefundable, and required a seven day minimum stay. These restrictions were designed to prevent most business travelers from utilizing the new low fares. At the same time, American limited the number of discount seats sold on each flight: they capacity-controlled the fares.
This combination provided the means to compete on price with the upstart airlines without damaging their core business-traveler revenues. The new pricing scheme was launched in as American SuperSaver Fares. The fares were quite successful at stemming the tide of defections of discretionary travelers to the low-cost airlines.
Despite this initial success, American experienced some significant problems implementing its new strategy. But as American gained experience with its Super-Saver fares, it realized that not all flights were the same. Flights on different days and at different times had very different patterns of demand. Some had many excess seats and could profitably support a higher allocation of discount seats; others had sufficient demand for regular-priced seats and warranted very little if any allocation to the new, discounted products.
American realized that a more intelligent approach was needed to realize the full potential of capacity-controlled discounts.
Though on a more modest scale, the capacity-control problem dates back to the mids, and other airlines and the Boeing Aircraft Company were experimenting with 3 As we show in the chapters that follow, a notion of this sort of displacement cost is central to the theory of RM.
Introduction 9 similar ideas at the time. DINAMO was implemented in full in January along with a new fare program entitled Ultimate Super-Saver Fares, which matched or undercut the lowest discount fares available in every market American served. American could now be much more aggressive on price. It could announce low fares that spanned a large swath of individual flights, confident in its capability to accurately capacity-control the discounts on each individual departure.
This feature of pricing aggressively and competitively at an aggregate, market level, while controlling capacity at a tactical, individual-departure level still characterizes the practice of RM in the airline industry today.
The effect of this new capability was dramatic. PeopleExpress was especially hard hit as American repeatedly matched or beat their prices in every market it served. It soon went bankrupt as a result of mounting losses, and in September the company was sold to Continental Airlines.
The Theory and Practice of Revenue Management
We were still the same company. We had been profitable from the day we started until American came at us with Ultimate Super Savers. That was the end of our run because they were able to under-price us at will and surreptitiously. Obviously PeopleExpress failed … We did a lot of things right.
And airlines that did not have similar RM capabilities scrambled to get them. As a result of this history, the practice of RM in the airline industry today is both pervasive and mature, and RM is viewed as critical to running a modern airline profitably. Many other carriers also attribute similar improvements in their revenue due to RM. The blessing is that RM can point to a major industry in which the practice of RM is pervasive, highly developed, and enormously effective.
Indeed, a large, modern airline today would just not be able to operate profitably without RM. Therefore, the airline success story validates both the economic importance of RM and the feasibility of executing it reliably in a complex business environment. This is the good-news story for the field from the airline experience. The bad news—the curse if you will—of the strong association of RM with airlines is that it has created a certain myopia inside the field. Many practitioners and researchers view RM solely in airline-specific terms, and this has at times tended to create biases that have hampered both research and implementation efforts in other industries.
A second problem with the airline-specific association of RM is that airline pricing has something of a bad reputation among consumers. While on the one hand customers love the very low fares made possible by RM practices, the fact that fares are complex, are available one minute and gone the next, and can be drastically different for two people sitting side by side on the same flight, has led to a certain hostility toward the way airlines price.
As a result, managers outside the industry are at times, quite naturally, somewhat reluctant to try RM practices for fear of engendering a similar hostile reaction among their customers. However, because its pricing structure is simpler than most other airlines the use of RM is less obvious to consumers and casual observers. Introduction 11 Yet the reality is that, in most cases, applying RM does not involve radically changing the structure of pricing and sales practices; rather, it is a matter of making more intelligent decisions.
A short answer is: in any business where tactical demand management is important and the technology and management culture exists to implement it. But this in turn begs the question: when do these conditions arise?
To answer this question, it helps to begin with a conceptual framework for thinking about the demand management process. Figure 1. RM addresses the structural, price, timing and quantity decisions a firm makes in trying to exploit the potential of this multidimensional demand landscape. For example, some RM problems look at exploiting heterogeneity in valuations among customers for a single product at a single point in time: they fix the product and time dimension and try to optimize over the customer dimension.
This problem is characteristic of the classical auction-design problems discussed in Chapter 6 and classical pricediscrimination problems discussed in Chapter 8. Other RM problems look at dynamically pricing a single product to heterogeneous customers over time: they fix the product dimension and optimize over the customer and time dimensions.
Such problems are addressed in Chapter 5. Others, such as the network problems in Chapter 3, address managing demand decisions for multiple products over multiple time periods, and the customer-behavior dimension is not explicitly considered. Of course, all three dimensions are important factors in practice. However, methodologically one often has to decompose and simplify the problem to develop implementable solutions. However, typically one or more of the following three factors link the demand across these dimensions.
First, multiple products may share production capacity or have joint production costs. In such cases, the demand-management decision for different products or for a given product in different periods of time are interrelated. For example, because of joint capacity constraints, accepting demand from a customer for a particular product at a specific point in time may mean giving up opportunities to accept demand at later points in time, or because lowering the price of one product increases its demand, this may reduce the capacity available for producing other products.
Second, even if production constraints do not link demand decisions, customer behavior often does. Customers may choose among substitute products at any given point in time, or customers may strategize over their timing in purchasing a given product. As a result, the price or quantity decisions that a firm makes about one product may affect demand for related products—or may affect the future demand for the same product. The most common link is over time; observed demand to date may reveal information about future demand.
Thus, a decision about price today may affect the information we gain about demand sensitivity, which will affect future pricing decisions. Also, a firm selling the same product in geographically separated markets or in different channels may gain information in one location or channel as a result of observing demand that impacts its decisions in other locations and channels.
Such linkages complicate demand-management decisions, and managing the often subtle tradeoffs they create is a key motivation for RM. Here, we discuss a few such conditions. As a result, there is less potential to exploit variations in willingness to pay, variations in preference for different products, and variations of purchase behavior over time.
Therefore, the more heterogeneity in customers, the more potential there is to exploit this heterogeneity strategically and tactically to improve revenues. Customers in the airline and hotel industries certainly exhibit this characteristic. They have widely varying patterns of usage and behavior in terms of when they purchase and how flexible their plans are, and they place very different valuations on the need to travel.
Hence, the potential to make bad decisions rises, and it becomes important to have sophisticated tools to evaluate the resulting complex tradeoffs. It exhibits significant variations by season, time of day, day of week, holidays and even correcting for this predictable seasonal variation is highly uncertain for a given flight.
However, the more inflexible the production—the more delays involved in producing units, the more fixed costs or economies of scale involved in production, the more the switch-over costs, the more capacity constraints—the more difficult or costly it becomes to match demand variations with supply variations. As a result, inflexibility leads to more interaction in the demand management at different points in time, between different segments of customers, across different products of a product line, and across different channels of distribution the different cells in Figure 1.
The complexity increases and the consequences of poor decisions become more acute. Hence, RM becomes more beneficial. Again, the airline industry is one in which production is very inflexible. Essentially, when committing to fly a flight from A to B, an airline both fixes the level of its output the number of seats and, for all practical purposes, the total cost of that output—independent of how many customer actually fly on the flight.
Its unit cost per seat sold, therefore, varies tremendously with the volume of sales, and once the capacity constraint is reached, no more production is possible. Worse yet, like all services, output cannot be inventoried, so production of air transport output in one period cannot be used to satisfy demand in later periods an unsold seat on Monday cannot be used to supply the need of an excess passenger on Tuesday.
All these factors combine to create extreme inflexibility in the technology of air transport service, and this is one of the key driving factors in the importance of RM in this industry. The price is a key feature of the watch, as it is with most luxury goods. They are status symbols, and to lower or manipulate the price risks damaging this status. A more subtle case is observed in situations where it is hard to assess quality through other, objective means.
For example, the hourly rate Introduction 15 of a prominent attorney or consultant, the tuition at an Ivy League university, and the price of a bottle of wine on a dinner menu—all play important roles as signals of quality to consumers. Again, tampering with prices for tactical reasons in such settings jeopardizes the signaling value of prices.
Airlines are arguably a good example.
While different airlines position themselves differently with respect to price and quality e. Moreover—despite what some airline marketers might like to believe— most consumer do not have strong quality preferences among airlines, at least not sufficient to outweigh even relatively small differences in schedule and price.
An Introduction to Revenue Management
It also requires systems to collect and store the data and to implement and monitor the resulting real-time decisions. In most industries it is usually feasible—in theory, at least—to collect and store demand data and automate demand decisions.
However, attempting to apply RM in industries that do not have databases or transactions systems in place can be a time-consuming, expensive, and risky proposition. RM, therefore, tends to be more suited to industries where and transaction-processing systems are already employed as part of incumbent business processes. Again, the airline industry is a perfect case in point. In fact, it is one of the earliest industries to move almost entirely to electronic selling and distribution—decades before the advent of e-commerce.
This long history of using information systems to automate business processes meant that it was quite natural to implement RM in the airline industry when the time came. The culture of the firm may not be receptive to innovation or may value more intuitive approaches to problem solving. This is often due to the culture of the industry and its managers: their educational backgrounds, their professional experiences and responsibilities en route to leadership positions, and the skills required to succeed in the industry.
Again, the airline industry serves as a good example. Industries that use yield management include airlines, hotels, stadiums and other venues with a fixed number of seats, and advertising. With an advance forecast of demand and pricing flexibility, buyers will self-sort based on their price sensitivity using more power in off-peak hours or going to the theater mid-week , their demand sensitivity must have the higher cost early morning flight or must go to the Saturday night opera or their time of purchase usually paying a premium for booking late.
In this way, yield management's overall aim is to provide an optimal mix of goods at a variety of price points at different points in time or for different baskets of features. The system will try to maintain a distribution of purchases over time that is balanced as well as high.
While yield management systems tend to generate higher revenues, the revenue streams tends to arrive later in the booking horizon as more capacity is held for late sale at premium prices.
Firms faced with lack of pricing power sometimes turn to yield management as a last resort. After a year or two using yield management, many of them are surprised to discover they have actually lowered prices for the majority of their opera seats or hotel rooms or other products. That is, they offer far higher discounts more frequently for off-peak times, while raising prices only marginally for peak times, resulting in higher revenue overall.
By doing this, they have actually increased quantity demanded by selectively introducing many more price points, as they learn about and react to the diversity of interests and purchase drivers of their customers. Ethical issues and questions of efficacy[ edit ] This article contains weasel words : vague phrasing that often accompanies biased or unverifiable information. Such statements should be clarified or removed. December Some consumers are concerned[ citation needed ] that yield management could penalize them for conditions which cannot be helped and are unethical to penalize.
For example, the formulas, algorithms, and neural networks that determine airline ticket prices could feasibly consider frequent flyer information, which includes a wealth of socio-economic information such as age and home address. The airline then could charge higher prices to consumers who are between certain ages or who live in neighborhoods with higher average wealth, even if those neighborhoods also include poor households.
Very few if any airlines using yield management are able[ citation needed ] to employ this level of price discrimination because prices are not set based on characteristics of the purchaser, which are in any case often not known at the time of purchase.
Some consumers may object that it is impossible for them to boycott yield management when buying some goods, such as airline tickets. Yield Management also includes many noncontroversial and more prevalent practices, such as varying prices over time to reflect demand.
This level of yield management makes up the majority of yield management in the airline industry. For example, airlines may price a ticket on the Sunday after Thanksgiving at a higher fare than the Sunday a week later. Alternatively, they may make tickets more expensive when bought at the last minute than when bought six months in advance.
The goal of this level of yield management is essentially trying to force demand to equal or exceed supply.Worse yet, like all services, output cannot be inventoried, so production of air transport output in one period cannot be used to satisfy demand in later periods an unsold seat on Monday cannot be used to supply the need of an excess passenger on Tuesday.
Again, tampering with prices for tactical reasons in such settings jeopardizes the signaling value of prices. You want the price to be right—not so high that you put off potential buyers and not so low that you lose out on potential profits. The most common link is over time; observed demand to date may reveal information about future demand.
It soon went bankrupt as a result of mounting losses, and in September the company was sold to Continental Airlines.
[PDF] FREE The Theory and Practice of Revenue Management (International Series in Operations
For instance, while most airlines commit to fixed prices and tactically allocate capacity, low-cost carriers tend to use price as the primary tactical variable. Successful hardcover version published in April Part II covers, in Chapters 5 and 6, both dynamic pricing and auctions.
However, for new employees in an industry, for academics, and for industry practitioners looking at a different industry, the chapter provides useful information on the institutional context in which RM is practiced.
They have widely varying patterns of usage and behavior in terms of when they purchase and how flexible their plans are, and they place very different valuations on the need to travel.
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