The Intersection of BI and Revenue Management

The Intersection of BI and Revenue Management Hugh J. Watson with Jon A. Higbie Hugh J. Watson is a professor of MIS and holds a C. Herman and Mary Virginia Terry Chair of Business Administration in the Terry College of Business at the University of Georgia. He is senior editor of the Business Intelligence Journal. hwatson@uga.edu Jon A. Higbie is a managing partner and chief scientist at Revenue Analytics, Inc. jhigbie@revenueanalytics.com If you have been in analytics for a while, you know that revenue management is an old, well-established practice in some industries. Its primary goal is to sell the right product to the right customer at the right time for the right price. Revenue management combines forecasts, predictions of customer behavior, inventory data, price sensitivity, competitive data (e.g., prices), and analytics to optimize revenues and profits from the sale of products and services. Revenue management traces its origins to yield management systems in the airline industry in the 1970s. In the 1980s, deregulation allowed new, upstart carriers to enter the market and offer very low fares because of a much lower cost structure (Cross, et al, 2011). In response, carriers such as American Airlines intensified efforts to enhance what are now called revenue management systems. Revenue management is commonly used to price perishable goods such as airline seats, rental cars, and hotel rooms. After a recent conversation with a long-time friend, Jon Higbie, who is a partner and chief scientist at Revenue Analytics, I realized that much has taken place in the field that people in BI and analytics should know about. I asked Jon to collaborate with me to briefly describe how revenue analytics typically works, describe recent developments and trends, and explain why BI and analytics professionals should be interested in the topic. How Revenue Management Works The goal of revenue management is to reduce surplus by forecasting demand and supply, then optimizing prices and applying inventory controls to maximize incremental positive cash flow. Contrary to what many consumers may think, the goal of revenue management is not to 4 BUSINESS INTELLIGENCE JOURNAL VOL. 19, NO. 4

Segment customers and products Forecast demand and estimate price response Optimize product price and availability Figure 1: Standard revenue management analytics process. Measure gouge customers. In fact, the optimal pricing tends to be fairly equally divided between price increases and decreases. A significant bias toward price increases almost always indicates the model is wrong. This was a problem with the first generation of price optimization applied to non-perishable goods. The models sought to optimize the net margin from each transaction. Today, the models seek to optimize net positive cash flow. When approached this way, there are often more price decreases than increases for consumers, as well as faster inventory turns and much higher net profits for the company (Harris, 2011; Wilson, 2014). Figure 1 illustrates the fundamental process, and Figure 2 shows a typical architecture for a revenue management system. Revenue Management Examples From the earliest applications of revenue management for perishable assets, the field s influence has evolved and expanded tremendously. Early systems used by airlines were better described as yield management systems because they focused on optimizing the inventory controls for products to maximize the yield, with prices determined by an external, highly manual process. These systems also focused on operations. Today, price is increasingly part of the package of recommendations, Users Executive Sales exec / ops Sales reps / supervisors RM and pricing Finance BI Layer OBIEE / Tableau / Qlikview / Excel Data Layer Demand forecast Supply forecast Sales universe RM Pricing Promotions Product inventory and availability Analytics universe Revenue priority Analytics and Modules ETLs modules Clustering/ tree segmentation Supply and demand forecasting Customer response to price Analytical modules Price and availability optimization Performance measurement Data Mart Data Sources B2C TRANX system B2B TRANX system Competitor price Market share Socioeconomic and environmental factors Figure 2: General high-level revenue management system architecture. BUSINESS INTELLIGENCE JOURNAL VOL. 19, NO. 4 5

and the systems drive operational and strategic decision making in marketing (promotional spend), budgeting, and investment. A great example is the experience at InterContinental Hotels Group (IHG), which was the first in the hotel industry to implement automated price recommendations along with inventory controls (Koushik, et al, 2012). Today, the Revenue Management Group at IHG has evolved to become the forecasting and analytics center of excellence for the entire company. They are the go-to team for advanced analytics for marketing, finance, and other business functions. Two key drivers were behind recent innovations in revenue management. The first was the post-9/11 travel slump, when occupancy and room rates tanked. This proved a painful experience for businesses that relied on old-style yield management systems (see Cooper, et al, 2006 for a good theoretical explanation). (Interestingly, when the financial crisis hit in December 2007, occupancy dropped again, but room rates did not decrease in direct proportion because hotel industry leaders were beginning to develop and use elasticity-based models of consumer demand to set prices.) The second driver was the growing success of using different revenue methods in non-perishable asset industries. United Parcel Service (UPS) was a leader when it began using an elasticity-based system to set prices for small to midsize commercial customers (Agrawal and Ferguson, 2007; Higbie, 2011). At first glance, the UPS logistic network might appear to be a perishable or constrained asset. Indeed, this was the general thinking that first led UPS to explore revenue management. On closer examination, the network was seldom constrained. To paraphrase a common statement from UPS operations at the time: We can always throw another box on the truck or in the plane. Further analysis led to the development of an elasticity-based B2B price-quote solution that is still being enhanced and leveraged at UPS today. Another B2B price optimization example is Marriott, which took the concept and applied it to their group business (Hormby, et al, 2010). Consulting companies such as PROS, Revenue Analytics, and Zilliant offer B2B pricing solutions for a wide variety of industries. Beginning in the late 1990s, the Ford Motor Company successfully applied elasticity-based models to optimize their incentive spending, making the firm the first movers in the consumer goods sector (Cross, et al, 2011), and netting Ford $3 billion in extra profit in 1999 (Leibs, 2000). Although many major automotive original equipment manufacturers (OEMs) have revenue management systems, the practice has only slowly been adopted by the broader community of consumer goods. This is partly because of the lack of connection between the typical consumer goods company and the end consumer; most consumer goods products are sold through third parties. In the automotive industry, the OEM knows each buyer specifically as well as what promotions are applied to sales. General consumer goods companies must rely on third-party data aggregators and seldom have visibility into individual transactions. Another factor is that trade spend analytics thinking at most consumer goods companies is dominated by traditional marketing science, where information about consumer preferences and price sensitivity is measured by conducting small but wellcontrolled experiments. One head of marketing analytics at a consumer goods firm said, I can learn nothing from my historical transactions because they were not controlled experiments. This statement is astonishing and foolish. By leveraging transaction data, a revenue management system can build models that are highly automated and much more economical than running frequent controlled experiments. In addition, the analyst can be selective about which transactions are used to calculate elasticity emulating the conditions of a controlled experiment, but still gathering huge volumes of data, thus leading to more precise and powerful (in the statistical sense) estimates of customer response. Believers in revenue management have a saying: I have more faith in what people say with their wallets than in what they say or do in a laboratory. The retail industry is experiencing a renaissance in revenue management. The earliest applications were 6 BUSINESS INTELLIGENCE JOURNAL VOL. 19, NO. 4

in optimizing markdowns to sell off excess inventory (Phillips, 2005). Today, online and brick-and-mortar retailers are creating new solutions centered on everyday and promotional pricing. One company that has spoken publicly is Sonic Automotive (Harris, 2011; Wilson, 2014), which has increased sales and profits by applying analytics to set optimal prices for new and used cars. Their success has been a big win for consumers, too. The system more often than not recommends lower prices than the prior approach and generates higher profits for Sonic by increasing inventory turnover. The Trend toward Built-for-Purpose Solutions In the 1980s through the early 2000s, the emphasis was on businesses buying off-the-shelf solutions. Starting in the 2000s, many companies became disillusioned with this approach. The packaged solution never quite seemed to fit the business, and the implementations were long, painful, and more expensive than promised. Also, the intellectual property was locked inside the software black box. If a company requested an enhancement, it was added to the queue of requests from other customers; the company would have to wait until the vendor implemented the change. If the enhancements were finally added, the upgrade to the new version of the software was often painful and expensive because the original software configuration was highly customized. Many vendors have discontinued enhancing their systems because the base of installed customers is so small. In some cases, support for the product was dropped by the vendor, and some companies have had to pay millions of dollars for the source code to the product they had already purchased just so they could keep the revenue management system functioning when they upgraded their servers. These companies learned the painful way that owning intellectual property is an important consideration. As companies have evolved their IT capabilities, particularly in databases and business intelligence, they have become more capable of supporting and extending their own revenue management systems. The most expensive software components are the database and BI capability. Many companies already own analytics software, and for those that don t, there are suitable open source alternatives. R is an excellent platform for statistics and is reliable and capable of handling big data. Many large companies are crunching billions of rows per day to update their predictive analytics. Furthermore, most universities are now teaching R. The current and future generations of statisticians will be more familiar with R than any other statistical package. Implications for BI Managers and Professionals As shown in Figure 2, BI technology and skills are a central component of any revenue management system. The foundation is a well-designed and scalable data mart or warehouse. This data store should be an open architecture that allows for the addition of new data sources and new outputs from the analytic modules. One mistake is trying to build the analytics layer on top of the reporting/bi layer. This adds development complexity and impedes implementation and extensions to the system. The BI layer is the primary presentation layer for today s cutting-edge revenue management solutions. There may be some custom user interface screens for certain functionality, particularly analytic model management. Older systems presented much of the data in custom screens, but this is a mistake with today s advanced BI tools. It leads to wasted development effort and makes the overall system less extensible. It is easier to modify or add a BI report than to build a new screen in.net, Java, or some other language. End user requirements gathering (to drive data modeling and reporting) is a critical activity that BI professionals should lead in partnership with end users as well as pricing and revenue management analytics professionals. Conclusion Pricing and revenue management is a key driving force behind many new big data and BI initiatives. Why? Revenue management can deliver a huge ROI. These solutions deliver net revenue gains of 2 to 15 percent with negligible incremental costs. The costs arise from integrating BI and database tools that most companies already BUSINESS INTELLIGENCE JOURNAL VOL. 19, NO. 4 7

own, building the analytic models (often leveraging open source tools), and, in some cases, a minimal increase in headcount. For the most part, the revenue management system results in more effective execution of jobs that already exist, so there is rarely a large increase in headcount. In the hotel industry, revenue management technology is reducing headcount by empowering directors of revenue management to manage multiple properties, as opposed to the old model of one director per hotel. A business case built on these returns can fund the development of the data mart or data warehouse, the acquisition of new BI technology, and the development of new skills in the organization. These foundational elements can fuel an upward spiral in analytics sophistication and the ultimate profitability of the organization. Increases Revenue by Implementing a Group Pricing Optimizer, Interfaces, 40(1), pp. 47 57. Koushik, Dev, Jon Higbie, and Craig Eister [2012]. Retail Price Optimization at InterContinental Hotels Group, Interfaces, 42(1), pp. 45 57. Leibs, Scott [2000]. Ford Heeds the Profits, CFO Magazine, August. Phillips, Robert [2005]. Pricing and Revenue Optimization, Stanford University Press, pp. 249 258. Wilson, Amy [2014]. Sonic targets 1-price, 45-minute transactions, Automotive News, June 23 24. References Agrawal, Vishal, and Mark Ferguson [2007]. Bidresponse Models for Customized Pricing, Journal of Revenue and Pricing Management, 6(3), pp. 212 228. Cooper, William L., Tito Homem-de-Mello, and Anton J. Kleywegt [2006]. Models of the Spiral-Down Effect in Revenue Management, Operations Research, 54(5), pp. 968 987. Cross, Robert, Jon Higbie, and Zachary Cross [2011]. Milestones in the Application of Analytical Pricing and Revenue Management, Journal of Revenue Management and Pricing, 10(1), pp. 8 18. Harris, Donna [2011]. Sonic rolls out market-pricing plan, Automotive News, February 28. Higbie, Jon A. [2011]. B2B Price Optimization Analytics, in Revenue Management: A Practical Pricing Perspective, Ian Yeoman and Una McMahon-Beattie (eds.), pp. 120 135. Hormby, Sharon, Julia Morrison, Prashant Dave, Michele Meyers, and Tim Tenca [2010]. Marriott International 8 BUSINESS INTELLIGENCE JOURNAL VOL. 19, NO. 4