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Assignment 2.1: HBR's Competing On Analytics In the Harvard Business Review article “Competing on Analytics,” author Tom Davenport talks about a blend of: a. the right focus b. the right culture c. the right people d. and the right technology for organizations to succeed in the use of analytics thereby generating value and gaining competitive advantage. In this question, comment on these aspects in relation to your own organization. In other words, does your organization have: a. the right focus b. the right culture c. the right people d. and the right technology to pursue (or embark upon a path of) analytics? If yes, explain how. If no, which aspects are lacking and why? Limit your answer to two 8.5x11 pages (single spaced, 12 font size, 1 inch margin on each side). Sketchy, incomplete analysis of an organization that does not draw from the HBR article won’t be awarded any credit. 2850 Jan06_TOC.qxp.pdf W Y E L M A G C YA N B L A C K LA SS E S K A R B O V IK january 2006 99 DECISION MAKING E ALL KNOW THE POWER of the killer app. Over the years, groundbreaking systems from compa- nies such as American Airlines (electronic reservations), Otis Elevator (predictive maintenance), and American Hospital Supply (online ordering) have dramatically boosted their creators’ revenues and reputations. These heralded – and coveted – applications amassed and ap- plied data in ways that upended customer expectations and optimized operations to unprecedented degrees. They transformed technology from a supporting tool into a strategic weapon. Companies questing for killer apps generally focus all their firepower on the one area that promises to create the greatest competitive advantage. But a new breed of company is upping the stakes. Organizations such as Amazon, Harrah’s, Capital One, and the Boston Red Sox have dominated their fields by deploying industrial- strength analytics across a wide variety of activities. In essence, they are transforming their organizations into armies of killer apps and crunching their way to victory. Organizations are competing on analytics not just be- cause they can–business today is awash in data and data Every company can learn from what these firms do. by Thomas H. Davenport Some companies have built their very businesses on their ability to collect, analyze, and act on data. COMPETING ON ANALYTICS crunchers–but also because they should. At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation. And analyt- ics competitors wring every last drop of value from those processes. So, like other companies, they know what prod- ucts their customers want, but they also know what prices those customers will pay, how many items each will buy in a lifetime,and what triggers will make people buy more. Like other companies, they know compensation costs and turnover rates, but they can also calculate how much per- sonnel contribute to or detract from the bottom line and how salary levels relate to individuals’ performance. Like other companies, they know when inventories are run- ning low, but they can also predict problems with demand and supply chains, to achieve low rates of inventory and high rates of perfect orders. And analytics competitors do all those things in a coor- dinated way, as part of an overarching strategy champi- oned by top leadership and pushed down to decision mak- ers at every level. Employees hired for their expertise with numbers or trained to recognize their importance are armed with the best evidence and the best quantitative tools. As a result, they make the best decisions: big and small, every day, over and over and over. Although numerous organizations are embracing ana- lytics, only a handful have achieved this level of profi- ciency. But analytics competitors are the leaders in their varied fields–consumer products,finance, retail, and travel and entertainment among them. Analytics has been in- strumental to Capital One, which has exceeded 20% growth in earnings per share every year since it became a public company. It has allowed Amazon to dominate on- line retailing and turn a profit despite enormous invest- ments in growth and infrastructure. In sports, the real se- cret weapon isn’t steroids, but stats, as dramatic victories by the Boston Red Sox, the New England Patriots, and the Oakland A’s attest. At such organizations, virtuosity with data is often part of the brand. Progressive makes advertising hay from its detailed parsing of individual insurance rates. Amazon customers can watch the company learning about them as its service grows more targeted with frequent pur- chases. Thanks to Michael Lewis’s best-selling book Mon- eyball, which demonstrated the power of statistics in pro- fessional baseball, the Oakland A’s are almost as famous for their geeky number crunching as they are for their athletic prowess. To identify characteristics shared by analytics compet- itors, I and two of my colleagues at Babson College’s Working Knowledge Research Center studied 32 organi- zations that have made a commitment to quantitative, fact-based analysis. Eleven of those organizations we clas- sified as full-bore analytics competitors, meaning top management had announced that analytics was key to their strategies; they had multiple initiatives under way involving complex data and statistical analysis, and they managed analytical activity at the enterprise (not depart- mental) level. This article lays out the characteristics and practices of these statistical masters and describes some of the very substantial changes other companies must undergo in order to compete on quantitative turf. As one would ex- pect, the transformation requires a significant invest- ment in technology, the accumulation of massive stores of data, and the formulation of companywide strategies for managing the data. But at least as important, it re- quires executives’ vocal, unswerving commitment and willingness to change the way employees think, work, and are treated. As Gary Loveman, CEO of analytics competi- tor Harrah’s, frequently puts it,“Do we think this is true? Or do we know?” Anatomy of an Analytics Competitor One analytics competitor that’s at the top of itsgame is Marriott International. Over the past 20years, the corporation has honed to a science itssystem for establishing the optimal price for guest rooms (the key analytics process in hotels, known as rev- enue management). Today, its ambitions are far grander. Through its Total Hotel Optimization program, Marriott has expanded its quantitative expertise to areas such as conference facilities and catering, and made related tools available over the Internet to property revenue managers and hotel owners. It has developed systems to optimize of- ferings to frequent customers and assess the likelihood of those customers’ defecting to competitors. It has given local revenue managers the power to override the sys- tem’s recommendations when certain local factors can’t be predicted (like the large number of Hurricane Katrina evacuees arriving in Houston). The company has even created a revenue opportunity model, which com- putes actual revenues as a percentage of the optimal rates that could have been charged. That figure has grown from 83% to 91% as Marriott’s revenue-management analytics has taken root throughout the enterprise. The word is out among property owners and franchisees: If you want to squeeze the most revenue from your inventory, Marriott’s approach is the ticket. Clearly, organizations such as Marriott don’t behave like traditional companies. Customers notice the differ- ence in every interaction; employees and vendors live the 100 harvard business review DECISION MAKING Thomas H. Davenport (tdavenport@babson.edu) is the President’s Distinguished Professor of Information Technol- ogy and Management at Babson College in Babson Park, Massachusetts, the director of research at Babson Executive Education, and a fellow at Accenture. He is the author of Thinking for a Living (Harvard Business School Press, 2005). difference every day. Our study found three key attributes among analytics competitors: Widespread use of modeling and optimization. Any company can generate simple descriptive statistics about aspects of its business–average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics. These companies use predictive modeling to identify the most profitable cus- tomers – plus those with the greatest profit potential and the ones most likely to cancel their accounts. They pool data generated in-house and data ac- quired from outside sources (which they analyze more deeply than do their less statistically savvy competitors) for a comprehensive understanding of their customers. They optimize their supply chains and can thus determine the impact of an unexpected con- straint, simulate alternatives, and route shipments around problems. They es- tablish prices in real time to get the highest yield possible from each of their customer transactions. They cre- ate complex models of how their oper- ational costs relate to their financial performance. Leaders in analytics also use sophis- ticated experiments to measure the overall impact or “lift” of intervention strategies and then apply the results to continuously improve subsequent analyses. Capital One, for example, con- ducts more than 30,000 experiments a year, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential cus- tomers will sign up for credit cards and that they will pay back Capital One. Progressive employs similar experi- ments using widely available insurance industry data. The company defines narrow groups, or cells, of customers: for example, motorcycle riders ages 30 and above, with college educations, credit scores over a certain level, and no accidents. For each cell, the com- pany performs a regression analysis to identify factors that most closely corre- late with the losses that group engen- ders. It then sets prices for the cells, which should enable the company to earn a profit across a portfolio of cus- tomer groups, and uses simulation soft- ware to test the financial implications of those hypotheses. With this approach, Progressive can profitably insure customers in traditionally high-risk cat- egories. Other insurers reject high-risk customers out of hand, without bothering to delve more deeply into the data (although even traditional competitors, such as All- state, are starting to embrace analytics as a strategy). An enterprise approach. Analytics competitors under- stand that most business functions–even those, like mar- keting, that have historically depended on art rather than science–can be improved with sophisticated quantitative january 2006 101 Y E L M A G C YA N B L A C K Compet ing on Analyt ics techniques. These organizations don’t gain advantage from one killer app, but rather from multiple applications supporting many parts of the business – and, in a few cases, being rolled out for use by customers and suppliers. UPS embodies the evolution from targeted analytics user to comprehensive analytics competitor. Although the company is among the world’s most rigorous practi- tioners of operations research and industrial engineering, its capabilities were, until fairly recently, narrowly fo- cused. Today, UPS is wielding its statistical skill to track the movement of packages and to anticipate and influ- ence the actions