Competing on Analytics HBR classic 10 哈佛商业评论经典案例 1001.docx

1、Competing on Analytics HBR classic 10 哈佛商业评论经典案例 1001Competing on AnalyticsbyThomas H. DavenportSome companies have built their very businesses on their ability to collect, analyze, and act on data. Every company can learn from what these firms do.Read the HBR In BriefWe all know the power of the ki

2、ller app. Over the years, groundbreaking systems from companies 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 heraldedand covetedappl

3、ications amassed and applied 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 prom

4、ises to create the greatest competitive advantage. But a new breed of company is upping the stakes. Organizations such as Amazon, Harrahs, 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

5、are transforming their organizations into armies of killer apps and crunching their way to victory. Organizations are competing on analytics not just because they canbusiness today is awash in data and data crunchersbut also because they should. At a time when firms in many industries offer similar

6、products and use comparable technologies, business processes are among the last remaining points of differentiation. And analytics competitors wring every last drop of value from those processes. So, like other companies, they know what products their customers want, but they also know what prices t

7、hose 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 personnel contribute to or detract from the bottom line and how salary levels

8、relate to individuals performance. Like other companies, they know when inventories are running 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 coordinated wa

9、y, as part of an overarching strategy championed by top leadership and pushed down to decision makers 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

10、 best decisions: big and small, every day, over and over and over. Although numerous organizations are embracing analytics, only a handful have achieved this level of proficiency. But analytics competitors are the leaders in their varied fieldsconsumer products, finance, retail, and travel and enter

11、tainment among them. Analytics has been instrumental 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 online retailing and turn a profit despite enormous investments in growth and infrastructure. In spo

12、rts, the real secret weapon isnt steroids, but stats, as dramatic victories by the Boston Red Sox, the New England Patriots, and the Oakland As attest. At such organizations, virtuosity with data is often part of the brand. Progressive makes advertising hay from its detailed parsing of individual in

13、surance rates. Amazon customers can watch the company learning about them as its service grows more targeted with frequent purchases. Thanks to Michael Lewiss best-selling book Moneyball, which demonstrated the power of statistics in professional baseball, the Oakland As are almost as famous for the

14、ir geeky number crunching as they are for their athletic prowess. To identify characteristics shared by analytics competitors, I and two of my colleagues at Babson Colleges Working Knowledge Research Center studied 32 organizations that have made a commitment to quantitative, fact-based analysis. El

15、even of those organizations we classified 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 enterpri

16、se (not departmental) 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 expect, the transformation requires a significant i

17、nvestment 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 requires executives vocal, unswerving commitment and willingness to change the way employees think, work, and are treated. As Gary L

18、oveman, CEO of analytics competitor Harrahs, frequently puts it, “Do we think this is true? Or do we know?” Anatomy of an Analytics Competitor One analytics competitor thats at the top of its game is Marriott International. Over the past 20 years, the corporation has honed to a science its system fo

19、r establishing the optimal price for guest rooms (the key analytics process in hotels, known as revenue 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 caterin

20、g, and made related tools available over the Internet to property revenue managers and hotel owners. It has developed systems to optimize offerings to frequent customers and assess the likelihood of those customers defecting to competitors. It has given local revenue managers the power to override t

21、he systems recommendations when certain local factors cant be predicted (like the large number of Hurricane Katrina evacuees arriving in Houston). The company has even created a revenue opportunity model, which computes actual revenues as a percentage of the optimal rates that could have been charge

22、d. That figure has grown from 83% to 91% as Marriotts 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, Marriotts approach is the ticket. Clearly, organizations su

23、ch as Marriott dont behave like traditional companies. Customers notice the difference in every interaction; employees and vendors live the difference every day. Our study found three key attributes among analytics competitors: Widespread use of modeling and optimization. Any company can generate si

24、mple descriptive statistics about aspects of its businessaverage 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 customersplus those with the greatest profit

25、 potential and the ones most likely to cancel their accounts. They pool data generated in-house and data acquired 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

26、chains and can thus determine the impact of an unexpected constraint, simulate alternatives, and route shipments around problems. They establish prices in real time to get the highest yield possible from each of their customer transactions. They create complex models of how their operational costs r

27、elate to their financial performance. Leaders in analytics also use sophisticated 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, conducts more than 30,000 experiments a y

28、ear, with different interest rates, incentives, direct-mail packaging, and other variables. Its goal is to maximize the likelihood both that potential customers will sign up for credit cards and that they will pay back Capital One. Progressive employs similar experiments using widely available insur

29、ance 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 company performs a regression analysis to identify factors that most clos

30、ely correlate with the losses that group engenders. It then sets prices for the cells, which should enable the company to earn a profit across a portfolio of customer groups, and uses simulation software to test the financial implications of those hypotheses. With this approach, Progressive can prof

31、itably insure customers in traditionally high-risk categories. Other insurers reject high-risk customers out of hand, without bothering to delve more deeply into the data (although even traditional competitors, such as Allstate, are starting to embrace analytics as a strategy). An enterprise approac

32、h. Analytics competitors understand that most business functionseven those, like marketing, that have historically depended on art rather than sciencecan be improved with sophisticated quantitative techniques. These organizations dont gain advantage from one killer app, but rather from multiple appl

33、ications supporting many parts of the businessand, 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 worlds most rigorous practitioners of operations research and industrial eng