Revenue Management - History

History

Before the emergence of Revenue Management, BOAC (now British Airways) experimented with differentiated fare products by offering capacity controlled “Earlybird” discounts to stimulate demand for seats that would otherwise fly empty. Taking it a step further, Robert Crandall, former Chairman and CEO of American Airlines, pioneered a practice he called Yield Management, which focused primarily maximizing revenue through analytics-based inventory control. Under Crandall’s leadership, American continued to invest in Yield Management’s forecasting, inventory control and overbooking capabilities. By the early 1980s, the combination of a mild recession and new competition spawned by airline deregulation posed an additional threat. Low-cost, low-fare airlines like PeoplExpress were growing rapidly because of their ability to charge even less than American’s Super Saver fares. After investing millions in the next generation capability which they would call DINAMO (Dynamic Inventory Optimization and Maintenance Optimizer), American announced Ultimate Super Saver Fares in 1985 that were priced lower than the PeoplExpress. These fares were non-refundable in addition to being advance-purchase restricted and capacity controlled. This Yield Management system targeted those discounts to only those situations where they had a surplus of empty seats. The system and analysts engaged in continual re-evaluation of the placement of the discounts to maximize their use. Over the next year, American’s revenue increased 14.5% and its profits were up 47.8%.

Other industries took note of American’s success and implemented similar systems. Robert Crandall discussed his success with Yield Management with Bill Marriott, CEO of Marriott International. Marriott International had many of the same issues that airlines did: perishable inventory, customers booking in advance, lower cost competition and wide swings with regard to balancing supply and demand. Since “yield” was an airline term and did not necessarily pertain to hotels, Marriott International and others began calling the practice Revenue Management. The company created a Revenue Management organization and invested in automated Revenue Management systems that would provide daily forecasts of demand and make inventory recommendations for each of its 160,000 rooms at its Marriott, Courtyard Marriott and Residence Inn brands. They also created “fenced rate” logic similar to airlines, which would allow them to offer targeted discounts to price sensitive market segments based on demand. To address the additional complexity created by variable lengths-of-stay, Marriott’s Demand Forecast System (DFS) was built to forecast guest booking patterns and optimize room availability by price and length of stay. By the mid-1990s, Marriott’s successful execution of Revenue Management was adding between $150 million and $200 million in annual revenue.

A natural extension of hotel Revenue Management was to rental car firms, which experienced similar issues of discount availability and duration control. In 1994, Revenue Management saved National Car Rental from bankruptcy. Their revival from near collapse to making profits served as an indicator of Revenue Management’s potential.

Up to this point, Revenue Management had focused on driving revenue from Business to Consumer (B2C) relationships. In the early 1990s UPS developed Revenue Management further by revitalizing their Business to Business (B2B) pricing strategy. Faced with the need for volume growth in a competitive market, UPS began building a pricing organization that focused on discounting. Prices began to erode rapidly, however, as they began offering greater discounts to win business. The executive team at UPS prioritized specific targeting of their discounts but could not strictly follow the example set by airlines and hotels. Rather than optimizing the revenue for a discrete event such as the purchase of an airline seat or a hotel room, UPS was negotiating annual rates for large-volume customers using a multitude of services over the course of a year. To alleviate the discounting issue, they formulated the problem as a customized bid-response model, which used historical data to predict the probability of winning at different price points. They called the system Target Pricing. With this system, they were able to forecast the outcomes of any contractual bid at various net prices and identify where they could command a price premium over competitors and where deeper discounts were required to land deals. In the first year of this Revenue Management system, UPS reported increased profits of over $100 million.

The concept of maximizing revenue on negotiated deals found its way back to the hospitality industry. Marriott’s original application of Revenue Management was limited to individual bookings, not groups or other negotiated deals. In 2007, Marriott introduced a “Group Price Optimizer” that used a competitive bid-response model to predict the probability of winning at any price point, thus providing accurate price guidance to the sales force. The initial system generated an incremental $46 million in profit. This led to an Honorable Mention for the Franz Edelman Award for Achievement in Operations Research and the Management Sciences in 2009.

By the early 1990s Revenue Management also began to influence television ad sales. Companies like Canadian Broadcast Corporation, ABC, and NBC developed systems that automated the placement of ads in proposals based on total forecasted demand and forecasted ratings by program. Today, many television networks around the globe have Revenue Management Systems.

Revenue Management to this point had been utilized in the pricing of perishable products. In the 1990s, however, the Ford Motor Company began adopting Revenue Management to maximize profitability of its vehicles by segmenting customers into micro-markets and creating a differentiated and targeted price structure. Pricing for vehicles and options packages had been set based upon annual volume estimates and profitability projections. The company found that certain products were overpriced and some were underpriced. Understanding the range of customer preferences across a product line and geographical market, Ford leadership created a Revenue Management organization to measure the price-responsiveness of different customer segments for each incentive type and to develop an approach that would target the optimal incentive by product and region. By the end of the decade, Ford estimated that roughly $3 billion in additional profits came from Revenue Management initiatives.

The public success of Pricing and Revenue Management at Ford solidified the ability of the discipline to address the revenue generation issues of virtually any company. Many auto manufacturers have adopted the practice for both vehicle sales and the sale of parts. Retailers have leveraged the concepts pioneered at Ford to create more dynamic, targeted pricing in the form of discounts and promotions to more accurately match supply with demand. Promotions planning and optimization assisted retailers with the timing and prediction of the incremental lift of a promotion for targeted products and customer sets. Companies have rapidly adopted markdown optimization to maximize revenue from end-of-season or end-of-life items. Furthermore, strategies driving promotion roll-offs and discount expirations have allowed companies to increase revenue from newly acquired customers.

By 2000, virtually all major airlines, hotel firms, cruise lines and rental car firms had implemented Revenue Management Systems to predict customer demand and optimize available price. These Revenue Management Systems had limited “optimize” to imply managing the availability of pre-defined prices in pre-established price categories. The objective function was to select the best blends of predicted demand given existing prices. The sophisticated technology and optimization algorithms had been focused on selling the right amount of inventory at a given price, not on the price itself. Realizing that controlling inventory was no longer sufficient, InterContinental Hotels Group (IHG) launched an initiative to better understand the price sensitivity of customer demand. IHG determined that calculating price elasticity at very granular levels to a high degree of accuracy still was not enough. Rate transparency had elevated the importance of incorporating market positioning against substitutable alternatives. IHG recognized that when a competitor changes its rate, the consumer’s perception of IHG’s rate also changes. Working with third party competitive data, the IHG team was able to analyze historical price, volume and share data to accurately measure price elasticity in every local market for multiple lengths of stay. These elements were incorporated into a system that also measured differences in customer elasticity based upon how far in advance the booking is being made relative to the arrival date. The incremental revenue from the system was significant as this new Price Optimization capability increased Revenue per Available Room (RevPAR) by 2.7%. IHG and Revenue Analytics, a Pricing and Revenue Management consulting firm, were selected as finalists for the Franz Edelman Award for Achievement in Operations Research and the Management Sciences for their joint effort in implementing Price Optimization at IHG.