Revenue Management
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[edit] Introduction
Revenue Management is a business process that has significantly altered the travel and hospitality industry since its inception in the mid 1980s. It requires analysts with detailed market knowledge and advanced computing systems who implement sophisticated mathematical techniques to analyze market behavior and capture revenue opportunities. It has evolved from Yield Management which the airlines invented as a response to deregulation and quickly spread to hotels, car rental firms, cruise lines, media, and energy to name a few. Its effectiveness in generating incremental revenues from an existing operation and customer base has made it particularly attractive to business leaders that prefer to generate return from revenue growth and enhanced capability rather than downsizing and cost cutting.
Revenue Management “ ....the single most important technical development in transportation management since we entered deregulation...”. - Robert Crandal, Chairman and CEO of American Airlines
Revenue Management implements the basic principles of supply and demand economics in a tactical way to generate incremental revenues. An essential condition for applicability is that a firm is able to repackage its basic inventory into virtual products that promote effective market segmentation. In the airline case this means implementing fences such as advanced purchase restrictions or length of stay requirements. Price and availability of these products is manipulated to maximize expected marginal revenue based on detailed statistical demand forecasts and mathematical optimization. The airlines achieve this by implementing demand based pricing and extremely sensitive and dynamic availability controls on their reservation systems.
Revenue is often ‘left on the table’ by firms that do not effectively segment their market. Although the exact mechanisms used to implement price and availability controls vary between industries the underlying business process and mathematical techniques have a remarkably broad applicability. Revenue Management is most effective in a high capital cost, low marginal cost environment, typically managing perishable inventory. This is because it focuses on maximizing expected marginal revenue for a given operation and planning horizon. It optimizes asset utilization by ensuring inventory availability to customers with the highest expected net revenue contribution and extracting the greatest level of ‘willingness to pay’ from the entire customer base. Revenue Management practitioners typically claim 3% to 7% incremental revenue gains due to revenue management activity. In many industries this can equate to over 100% increase in profits. A competent revenue management analyst with good decision support tools can generate $10,000 per hour.
[edit] History
Deregulation is generally regarded as the catalyst for RM in the airline industry, but this tends to overlook the role of Global Distribution Systems (GDS’s). It is arguable that the fixed pricing paradigm occurs as a result of decentralized consumption. With mass production, pricing became a centralized management activity and customer contact staff focused on customer service exclusively. Electronic commerce, of which the GDS's were the first wave, created an environment where large volumes of sales could be managed without large numbers of customer service staff. They also gave management staff direct access to price at time of consumption and rich data capture for future decision-making.
Although Yield Management had been a common practice in the airlines in the early 1980s, Revenue Management may reasonably assigned an inception date of January 17, 1985 when American Airlines launched Ultimate Super Saver fares in an effort to compete with low cost carrier PeopleExpress. Donald Burr, the CEO of PeopleExpress, is quoted in the book "Revenue Management" by Bob Cross saying "We were a vibrant, profitable company from 1981 to 1985, and then we tipped right over into losing $50 million a month...We had been profitable from the day we started until American came at us with Ultimate Super Savers." The Revenue Management systems developed at American Airlines were recognized by the Edelman Prize committee of INFORMS for contributing $1.4 billion in a three year period at the airline.
Revenue Management spread to other travel and transportation companies in the early 1990s. Notable was implementation of Revenue Management at National Car Rental. In 1993, General Motors Corporation was forced to take a $744 million charge against earnings related to its ownership of National Car Rental Systems. In response, National's program expanded the definition of Revenue Management to include capacity management, pricing and reservations control. As a result of this program, General Motors was able to sell National Car Rental Systems for an estimated $1.2 billion. Other notable Revenue Management implementations include the NBC which credits its system with $200 in improved ad sales from 1996 to 2000, the Target Pricing initiative at UPS, and Revenue Management at Texas Childrens Hospital. Since 2000, much of the dynamic pricing, promotions management and dynamic packaging that underly ecommerce sites leverage Revenue Management techniques. In 2002 GMAC launched an early implementation of web based revenue management in the financial services industry.
There have also been high profile failures and faux pas. Amazon.com was criticized for irrational price changes that resulted from a Revenue Management software bug. The Coca-Cola Company's plans for a dynamic pricing vending machine were put on hold as a result of negative consumer reactions. Revenue Management is also blamed for much of the financial difficulty currently experienced by legacy carriers. The reliance of the major carriers on high fares in captive markets arguably created the conditions for low cost carriers to thrive.
[edit] Econometrics
Revenue Management econometrics centers around detailed forecasting and mathematical optimization of marginal revenue opportunities. The opportunities arise from segmentation of consumer willingness to pay. If the market for a particular good follows the simple straight line Price/Demand relationship illustrated below, a single fixed price of $50 there is enough demand to sell 50 units of inventory. This results in $2,500 in revenues. However the same Price/Demand relationship yields $4,000 if consumers are presented with multiple prices.
In practice the segmentation approach relies on adequate fences between consumers so that everyone doesn't buy at the lowest price offered. The airlines use time of purchase to create this segmentation, with later booking customers paying the higher fares. The fashion industry uses time in the opposite direction, discounting later in the selling season once the item is out of fashion or inappropriate for the time of year. Other approaches to fences involve attributes that create substantial value to the consumer at little or no cost to the seller. A backstage pass at a concert is a good example of this. Intially Revenue Management avoided the complexity caused by the interaction of absolute price and price position by to using surrogates for price such as booking class. By the mid 1990s most implementation incorporated some measures of price elasticity. The airlines were exceptional in this case, preferring to focus on more detailed segmentation by implementing O&D ( Origin and Destination ) systems.
At the heart of the Revenue Management decisionmaking process is the trade-off of marginal revenues from segments that are competing for the same inventory. In capacity constrained cases there is a bird-in-the-hand decision that forces the seller to reject lower revenue generating customers in the hopes that the inventory can be sold in a higher valued segment. The trade-off is sometimes mistakenly identified as occurring at the intersection of the marginal revenue curves for the competing segments. While this is accurate when it supports marketing decisions where access to both segments is equivalent, it is wrong for inventory control decisions. In these cases the intersection of the marginal revenue curve of the higher valued segment with the actual value of the lower segment is the point of interest.
In the case illustrated here, a car rental company must set up protection levels for its higher valued segments. By estimating where the marginal revenue curve of the luxury segment crosses the actual rental value of the midsize car segment the company can decide how many luxury cars to make available to midsize car renters. Where the vertical line from this intersection point crosses the demand (horizontal) axis determines how many luxury cars should be protected for genuine luxury car renters. The need to calculate protection levels has led to a number of heuristic solutions, most notable EMSRa and EMSRb. The balancing point of interest is found by the equation
R2 = R1 * Prob(D1)
where R2 is the value of the lower valued segment R1 is the value of the higher valued segment D1 is the demand for the higher valued segment
This equation is re-arranged to compute protection levels as follows:
D1 = Prob-1(R2 / R1)
In words, you want to protect D1 units of inventory for the higher valued segment where D1 is equal to the inverse probability of demand of the revenue ratio of the lower valued segment to the higher valued segment. This equation defines the EMSRa algorithm which handles the two segment case. EMSRb is smarter and handles multiple segments by comparing the revenue of the lower segment to a demand weighted average of the revenues of the higher segments. Neither of these heuristics produces the exact right answer and increasingly implementations make use of Monte Carlo simulation to find optimal protection levels.
Since the mid 1990s increasingly sophisticated mathematical models have been developed such as the dynamic programming formulation pioneered by Talluri and Van Ryzin which has led to more accurate estimates of bid prices. Bid prices represent the minimum price a seller should accept for a single piece of inventory and are popular control mechanisms for Hotels and Car Rental firms. Models derived from developments in financial engineering are intriguing but have been unstable and difficult to parameterize in practice. Revenue Management tends to focus on environments that are less rational than the financial markets.
[edit] See Also
INFORMS Revenue Management Section
[edit] Bibliography
Belobaba, Peter P. 1989, “Application of a Probabilistic Decision Model to Airline Seat Inventory Control,” Operations Research, Vol. 37, No. 2 (March-April 1989) pp. 183-196.
Cross, Robert G. 1986, “Strategic Selling: Yield Management Techniques to Enhance Revenue” Presentation to the Shearson Lehman Brothers, Inc. 1986 Airline Industry Seminar, Key Largo, Florida, February 14, 1986.
Curry, Renwick E. 1993, “Kalman Filtering and Exponential Smoothing” Presentation to AGIFORS Reservations and Yield Management Study Group, May 1993 Sydney, Australia.
Curry, Renwick E. 1990 ,“Optimal Airline Seat Allocation with Fare Classes Nested by Origins and Destinations,” Transportation Science, Vol. 24, No. 3 (August 1990) pp. 193-204.
Johnson, Ernest and Geraghty, M. Kevin 1997, "Revenue Management Saves National Car Rental." Interfaces 27: pp. 107-127,
Littlewood, Kenneth 1972, “Forecasting and Control of Passenger Bookings,” AGIFORS Symposium Proc. 12, 1972, pp. 95-117.
Ramaekers, Lawrence 1995, “National Car Rental Systems, Inc.,” SCORECARD™, the Revenue Management Quarterly, First Quarter 1995, pp. 2-3.
Smith, Barry C., Leimkuhler, John F. and Ross M Darrow 1992, “Yield Management at American Airlines”, Interfaces, Vol. 22, No. 1 (January-February 1992) pp. 8-31.
Weatherford, Lawrence R. and Bodily, S. E. “A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking and Pricing,” Operations Research, Vol. 40, (1992) pp. 831-844.
Williamson, Elizabeth L. “Comparison of Optimization Techniques for Origin-Destination Seat Inventory Control” Report FTL-R88-2, Flight Transportation Laboratory, MIT, Cambridge, MA 1988.
