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Executive Summary
This report describes the emergence of a new form of competition based on the extensive use of analytics, data, and fact-based decision making. The analytics— quantitative or statistical models to analyze business problems—may be applied to a variety of business problems, including cus- tomer management, supply chains, and financial performance. The research assessed 32 firms with regard to their orientation to analytics; about one-third were classified as fully engaged in analytically oriented strate- gies. Both demand and supply factors for analytical competition are described. Of the two, demand factors are the more difficult to create. The presence of one or more committed senior executives is a primary driver of analytical competition.
On What Basis Do
Companies Compete Today?
In virtually every industry, many former strategic alternatives are no longer viable or likely to be successful. Today, there are few regulated monopolies, or compa- nies with unique geographical access. Proprietary technologies are rapidly copied by competitors, and breakthrough inno- vation in either products or services is rare. Most of the competitive strate- g ies organizations are employing today involve optimization of key business processes. Instead of serving all customers, they want to serve optimal customers— those with the highest level of prof- itability and lifetime value. Instead of receiving goods and services whenever they happen to arrive, they attempt to optimize supply chains to minimize disruptions and in-process inventory. Instead of looking backward at their
b u s i n e s s p e r f o r m a n c e a n d m a k i n g ex post facto adjustments, they seek to understand how optimum nonfinancial per formance drives optimum financial per formance, and to make accurate forecasts of future per formance so they can react in advance of situations. Instead of throwing money at business problems, they seek to optimize their use of capital.
But strateg ies involving optimization require something different than those based on taking business as it comes. Above all, they require extensive data on the state of the business environ- ment and the company’s place within it, and extensive analysis of the data to model that environment, predict the consequences of alternative actions, and guide executive decision making. Moreover, they require analysts and decision makers who both understand the value of analy tics and know how best to apply these for driving enhanced per form- ance. Companies that strive to optimize their business per- formance using this data-intensive approach are compet- ing on analy tics and analy tical capabilities. Many companies are pursuing optimization-
W O R K I N G K N OW L E D G E R E S E A R C H R E P O RT
Competing on Analytics
THOMAS H. DAVENPORT, DON COHEN, AND AL JACOBSON MAY 2005
About This Research
This research report is based on analysis of 32 organizations from a variety of industries (Figure 1) that are successful both in terms of their overall performance and in their use of business analytics. The research was undertaken to investigate and document how and why these companies not only use sophisticated analytics, but also make them the basis of their competitive strategies, and adopt or move toward an enterprise- level approach to business intelligence. Telephone or in-person interviews were conducted with either IT or business execu- tives at 30 firms; three firms were analyzed solely on the basis of secondary research.
INDUSTRI ES RE PRESE NTED N U M BE R
Financial Services 10
Consumer Products and Retail 6
Travel, Transport, and Entertainment 5
Pharmaceutical and Chemical 4
Information Technology and Communications 3
Health Care 2
Engineering 1
Government 1
Figure 1:
Industries of Companies Surveyed
based strategies, but most have failed to develop the analytical capabilities neces- sary to make them succeed.
The idea of competing on analytics is not entirely new. A few organizations—most within financial services and particularly in financial investment and trading businesses— have competed on this basis for decades. The trading of stocks, bonds, currencies, and commodities has long been driven by analytics. What is new is the spreading of analytical competition to a variety of other industries—from consumer finance to retailing to travel and entertainment to consumer goods—and within compa- nies from individual business units to an enterprise-wide perspective. Even the most traditionally intuitive industries are moving in this direction—professional sports teams, for example.
Two of Boston’s sports teams have had some enviable success of late, at least in part because of their analytical capabilities. The New England Patriots football team, for example, won its third Super Bowl in four years. The team uses data and analyt- ical models extensively, both on and off the field. In-depth analytics help the team select its players, stay below the salary cap, decide whether to punt or “go for it” on fourth down, and try for one point or two after a touchdown. Both its coaches and players are renowned for their exten- sive study of game films and statistics, and Head Coach Bill Belichick peruses articles by academic economists on statistical probabilities of football outcomes. Off the field, the team uses detailed analytics to assess and improve the “total fan experi- ence.” At every home game, for example, 20 to 25 people have specific assign- ments to make quantitative measurements of the stadium food, parking, personnel, bathroom cleanliness, and other factors. External vendors of services are monitored for contract renewal and have incentives to improve their performance.
The Boston Red Sox baseball team is also a convert to analytics (following, in many ways, the lead of the pioneering but less well-financed Oakland A’s). The ability to extract knowledge from data presum- ably helped the Sox win the World Series in 2004 for the first time in 86 years. Boston has begun to select players less on traditional criteria such as batting aver- age, but rather on newer, more subtle
factors such as on-base percentage. Bill James, considered the godfather of base- ball statistics or “sabermetrics,” was hired by the Red Sox as an adviser. The Sox also have become more analytical off the field. Like the Patriots, they map and monitor key aspects of the fan experi- ence—from the decision to go to a game, to the routes taken by fans to the game, to the effectiveness of the cleaning crew. The team’s management has maximized revenues from Fenway Park, the smallest baseball park in the major leagues, by cal- culating ticket price elasticities, establish- ing an online market for season ticket resales, and modeling revenue increases from adding seats in unused locations (including on top of the Green Monster, the towering left field wall).
Analytical competition is not only taking root in U.S. sports. Some soccer teams in Europe also have begun to employ similar techniques. AC Milan, one of the leading teams in Europe, uses predictive models to prevent player injuries by analyzing physiological, orthopedic, and mechanical data from a variety of sources. Bolton, a fast-rising English soccer team, is known for its manager’s use of extensive data to evaluate player performance and team strategies.
Analytical cultures and processes are appearing not only in professional sports teams, but in any business that can har- ness extensive data, complex statistical processing, and fact-based decision mak- ing. Analytics is becoming a primary basis for competition for these firms. They use analytical tools to change the way they compete or to perform substantially better in the existing business model. The gam- ing firm Harrah’s, for example, has chosen to compete on analytics for customer loy- alty and service, rather than on building the mega-casinos in which its competitors have invested. Its CEO, Gary Loveman, has commented, “We use database mar- keting and decision-science-based analyti- cal tools to widen the gap between us and casino operators who base their cus- tomer incentives more on intuition than evidence.” Amazon.com uses extensive analytics to predict what products will be successful, and to wring every bit of effi- ciency out of its supply chain. Amazon CEO Jeff Bezos notes, ”For every leader in the company, not just for me, there are decisions that can be made by analy- sis. These are the best kinds of decisions. They’re fact-based decisions.” At the mutual fund company Dreyfus, analysis of customer information defined segmenta- tion that helped reduce fund attrition from 22 to 7 percent a year. These companies, and a variety of others, are clearly com- peting on analytics.
Analytical cultures and
processes are appearing
not only in professional
sports teams, but in any
business that can harness
extensive data, complex
statistical processing, and
fact-based decision making.
test the financial implications of these hypotheses. Through this analytical approach, Progressive can profitably insure customers in traditionally high-risk categories, such as motorcyclists.
Use of Analytics across Multiple Functions and Business Units One of the hallmarks of an analytically oriented firm is the use of sophisticated analytics not just in one business function or process, but across multiple aspects of the business. Successful analytical competitors have realized the power of these tools and approaches, and are adopting them across their businesses. UPS, for example, has traditionally focused on analytics for operations and logistics. More recently, it has developed a strong analytical focus on customers, assessing the likelihood of customer attri- tion, or identifying sources of problems for customers. Several firms, described below, are even extending their analytical orientations to direct use by customers.
As we will argue later, however, there is a balance to be maintained in terms of broadening the focus on analytics, and employing them to address a specific busi- ness domain. Executives at several analytical competitors warned against losing a clear business purpose for analytics. Harrah’s, for example, has targeted much of its analysis on increasing customer loyalty, although it has extended it into such related areas as pricing and promotions as well. Analytical competitors can broaden their focus beyond a narrow function, but they are careful not to become too diffuse in their analytical targeting so that they continue to support their primary strategies.
An Enterprise-Level Management Approach Business intelligence applications often have been managed at the departmental level, with analytically oriented business functions selecting their own tools, managing their own data warehouses, and training their own people. However, if analytics are to be a company’s basis for competition, and
if they are to be broadly adopted across the firm, it makes more sense to manage them at an enterprise level. This ensures that there is a critical mass of skills, that critical data and other resources are protected, and that data from multiple business functions can be correlated. The enterprise approach may include both organizational and technical capabilities for business intelligence. At the organiza- tional level, for example, Procter & Gamble recently consolidated its analytical organizations for operations and supply chain, marketing, and other functions. This will allow a critical mass of analytical expertise to be deployed to address P&G’s most critical business issues.
From a technology standpoint, many firms have had highly dispersed analytical technology in the form of many spread- sheets. However, one researcher suggests that between 20 and 40 percent of spreadsheets contain errors. Furthermore, the proliferation of user-developed spreadsheets and databases inevitably leads to multiple versions of key indica- tors within an organization.
Because of these problems, many firms are attempting to consolidate and inte- grate their technologies for business ana- lytics. Adopting such an approach means
IT organizations must develop new and broader capabilities for extracting and cleaning data, loading and maintaining data warehouses, data mining, and query and reporting. These tools historically have come from separate vendors and have been difficult to integrate. However, the leading vendors of business intelli- gence tools and applications are beginning to broaden and integrate their offerings themselves, and to market and sell them at the enterprise level.
Stages of Analytical
Competition
Analytical competition is not a binary attribute, which an organization either has or lacks. There are several stages of ana- lytical orientation that we observed in the companies we interviewed (Figure 2). The percentages of organizations at these stages are by no means representative of any larger population; we intentionally sought out companies at the higher end of the analytical spectrum. A random sample of organizations would probably look like an inverted version of Figure 2, with the highest frequencies at the lower stages.
Stage 1 (“Major Barriers”) organizations have some desire to become more analyt- ical, but thus far they lack the will and
Stage 3 Vision Not Yet Realized
Stage 5
Stage 4 Almost There
Stage 2 Local Activity
Stage 1 Major Barriers
Analytical
Competitors
F I R M S
F I R M S
F I R M S7/
F I R M S
F I R M S
Figure 2:
Stages of Analytical Competition among Study Organizations
skill to do so. They face some substantial barriers—both organizational and techni- cal—to analytical competition, and are still focused on putting basic, integrated trans- action functionality in place. As a result they are not yet on the path to becoming analytical competitors. Because we attempted to interview only organizations that compete on analytics, we encountered only two Stage 1 organizations—a state government agency and an engineering firm (and even that firm is becoming more analytical about its human resources). However, Stage 1 organizations probably constitute the majority of all large organizations.
Stage 2 (“Local Activity”) organizations have made substantial progress in becom- ing more analytical, but it is primarily local, within particular functions or units. Marketing, for example, may be identifying optimal customers or modeling demand, but the example has not spread to other parts of the company. Their business intelligence activities produced economic benefits, but not enough to affect the company’s competitive strategy. We found six of these firms. What they primarily lacked was a vision of analytical competi- tion that came from senior executives. Several of the firms had some of the same technology as firms at higher stages of analytical activity, but they had not put it to strategic use.
The organizations in Stage 3 (“Vision Not Yet Realized”) do grasp the value and the promise of analytical competition, but they are a long way from actually suc- ceeding with it. We found seven organiza- tions in this position. Some only recently have articulated the vision, and have not begun implementing it. Others have very high levels of functional or business unit autonomy, and are having difficulty mounting a cohesive approach to analytics across the enterprise. One multiline insur- ance company, for example, had a CEO with the vision of using data, analytics, and a strong customer orientation in the
fashion of Progressive, an auto insurance company with a history of technological and analytical innovation. But the company only recently had begun to expand its analytical orientation beyond the tradition- ally quantitative actuarial function, and there was little cooperation across the life and property and casualty business units.
Stage 4 (“Almost There”) organizations have the vision, and are close to achieving it. Six organizations fell into this category. Some only recently had adopted an enterprise- wide approach to analytical competition, and had yet to fully realize it in terms of marshaling the necessary resources. Others were competing on the basis of analytics, but also were competing on the basis of other factors, such as maintaining strong personal relationships with cus- tomers. Only a small degree of added emphasis on analytical capability would place these companies in the top rank.
The top rank is Stage 5 (“Analytical Competitors”), which describes organiza- tions that have embarked upon analytical competition as a primary dimension of strategy. These are the organizations we primarily sought to uncover in our research, and therefore we identified 11 of them. They include such large and small organizations as Apex Management Group (a health care actuarial firm), Barclays Consumer Finance, Capital One, Harrah’s, Marriott, Owens & Minor, Progressive, Wal- Mart, a consumer products firm, and the sports teams, the New England Patriots and the Boston Red Sox. These firms exhibited each of the attributes described above as the components of analytical competition. They are also all highly suc- cessful within their industries, and attrib- ute their success at least in part to their analytical strategies. Barclays, for example, increased its revenue per active account by 25 percent, while reducing delinquent accounts by 23 percent, by following its analytically oriented “Information Based Customer Management” strategy.
What’s the Business Value
of Analytics?
Analytics can be used to pull almost every lever of organizational performance. However, we found several business objectives and issues that were driving most of the analytical activity in the firms we studied. They include the following:
- Customers or consumer— Several organi- zations were focused on customer or consumer analytics, which encompass a variety of specific objectives. They might include, for example, identifying the most prof- itable or desirable customers, or those with the lowest risk of nonpayment. Customer analytics also include identifying the current customers who are most like- ly to stop being customers. They also might include customer-specific pricing or product/ service offerings based on the customer’s past or predicted future buying frequency and habits. Companies that pursued this set of analytics among our study respondents included Harrah’s, Procter & Gamble, Progressive Insurance, Barclays, and Capital One.
- Supply chain— Analytics for logistics and the supply chain are well-established in many large firms, with the primary orientation usually being reduction of in-process inventory. Supply chain analysis also might encompass matching demand and supply, routing shipments around logistical problems, reducing stockouts and overstocks, alternative supply simu- lations, plant and distribution center siting decisions, and price optimization. Among the companies in our study, Wal-Mart is the leading exponent of supply chain analytics.
- Financial performance and cost management— One domain of business value for analytics can revolve around performance management. Monitoring and decision making on financial infor- mation is not often thought of as a
addressed with detailed analysis. Procter & Gamble, for example, pulls together an analytical team whenever it considers the supply chain opportunities an acquisi- tion might offer to drive synergy savings. One might hope that more analytical approaches will improve the dismal record of success many companies have experienced in mergers and acquisitions.
How Do Firms Become
Analytical Competitors?
In order for a firm to become an analytical competitor, the supply of and demand for data and analysis must be in alignment. The supply issues are much easier to deal with and are generally available in the marketplace, although their absence in a firm is certainly problematic. The supply factors for analytical competition include the following:
High-Quality Data The most important factor in being prepared for sophisticated analytics is the availability of sufficient volumes of high-quality data. This is less of a prob- lem today than it was previously for many organizations, which have made substan- tial progress in accumulating transaction data the past several years. Whether the data come from ERP systems, point-of-sale systems, or Internet transactions, many organizations have a greater volume of data than ever before. The difficulty is pri- marily in ensuring data quality, integrating and reconciling it across different systems, and deciding what subsets of data to make easily available in data warehouses (i.e., having a clear strategy for data access). Many organizations remain highly fragmented, and have issues involving integration across their diverse business functions and units. Even such basic points as agreeing on the definition of who is a customer can be problematical across lines of business. As we noted above, the lowest-ranking firms in our scale of analytical competition still face significant difficulties with these basic
data issues. The leading firms, however, have largely overcome them.
Previous studies of firms’ analytical capa- bilities have found even leading-edge companies tend to be good at either qualitative knowledge management or quantitative data management, but rarely both. Companies still wrestle with this combination, but we found a few more examples of firms that do both well— particularly in the realm of consumer information. Procter & Gamble historically has been an industry leader in customer analytics, but it also tries to develop a detailed understanding of consumer behaviors through ethnographic (close observation) and psychographic analysis. Wachovia Bank combines knowledge from customer relationships and quantitative data analysis of customers (primarily cus- tomer segmentation analysis and market- ing campaign targeting) to determine what services to offer a particular customer, what markets to target, and what new ini- tiatives to undertake at particular financial centers. The importance of personal busi- ness relationships is deeply embedded in the Wachovia culture, and CEO Ken Thompson insists it remains there even as the culture also embraces analytics. Particularly where customers are con- cerned, it’s important to remember that marketing and service processes involve more than the application of statistics. A Capable Technology Environment In order to take advantage of good data, an organization also needs a capable hardware and software environment. Complex analytics chew up a good deal of processing power, so the workstations and servers used for this purpose need to be substantially more powerful than those used for conventional office tasks. Apex Management Group, for example, a health care actuarial firm, is transitioning to a 64-bit computing environment to deal with the complex and data-intensive sta- tistical analyses it performs for its clients.
An analytics group at a consumer prod- ucts firm rented time on a supercomputer to do some of its more complex analyses. From a software perspective, “business intelligence” software offers a variety of capabilities, including data warehouse management, query and reporting, data mining, and various forms of statistical analysis. Ideally all these capabilities would be well-integrated and easy to use. From the end user perspective, ease of analysis, reporting, and data visualization were often mentioned as important in the firms we interviewed. For some firms focused on real-time analytics (such as real-time pricing and yield management), the speed of data management and analysis is a critical factor for software and hardware. Quantitative Expertise While analytical software becomes increasingly easy to use, firms that com- pete on analytics still require substantial quantitative skills—either in-house or con- tracted from outside. The statistical expert, in order to be useful, also will need to be familiar with the business problems in the function and industry; the quantitative skills of a good analyst are rarely equally applicable across diverse businesses. One pharmaceutical company, for example, attempted to use several bioinformatics experts to pursue analysis of commercial problems in marketing and operations, and found they were neither motivated nor expert at the applications. While sta- tistical analysts who also understand busi- ness issues always have been difficult to find, it is increasingly possible to hire analytical expertise outside of a company— even from India or China in some cases.
However, some firms we interviewed stressed the importance of a close and trusting relationship between quantitative analysts and decision makers. The need is for statistical experts who also understand the business in general, and the particular business need of a specific decision maker.
As one manager at Wachovia Bank put it with regard to the relationships his analyti- cal group tries to maintain:
We are trying to build our people as part of the business team; we want them sitting at the business table, participating in a discussion of what the key issues are, determining what information needs the business people have, and recommending actions to the business partners. We want this [analytical group] to be more than a general utility, but rather an active and critical part of the business unit’s success.
A consumer products firm we interviewed hires what it calls “PhD’s with personality” for its analytical group—individuals with heavy quantitative skills, but also the ability to speak the language of the business and market their work to internal (and in some cases, external) customers. To find these types of people and develop these types of relationships would surely be much more difficult in an outsourcing sit- uation, and virtually impossible with the analysts half a world away from the deci- sion makers.
Demand—The Critical Factor
in Analytical Competition
More difficult to create than supply is the demand for analysis and fact-based deci- sion making within a company. In the earliest stages of analytical competition (Stage 1 and 2 organizations), demand is created by particular business problems. As analytics becomes more central to the competitive strategy, demand becomes more generalized across an organization. Yet, unlike the supply factors described above, demand—the desire to use analyt- ics as a primary competitive factor—cannot be bought in the marketplace. The key demand factors we identified include:
Willing Senior Executives Several lower-stage firms we interviewed
that made some use of business intelli- gence said the lack of demand from top- level senior executives was their single most significant barrier to engaging in analytical competition. These executives were more comfortable with intuitive deci- sions, or weren’t aware of the possibilities for analytical competition within their industry. Some were not averse to analyt- ics, but didn’t have enough personal ana- lytical experience to base their strategies on analytics and fact-based decisions. Without executives who want to use data and analysis to make decisions, even the best business intelligence applications will not be used. We saw several patterns of involvement by senior executives on the demand side, which we describe below.
Some organizations’ leaders had the desire to compete analytically from their beginning. Capital One, for example, was created in a 1994 IPO in order to apply the founders’ information-based strategy to the credit card business. Amazon.com was viewed by founder Jeff Bezos as competing on analytics from its start. Its concept of personalization was based on statistical algorithms and Web transaction data, and it quickly moved into analytics on supply chain and marketing issues as well. Amazon recently used analytics to explore whether it should advertise on tel- evision, and concluded it would not be a successful use of its resources. The vision of the founders of these startup businesses led to analytical competition.
In other cases, the demand for analytical competition came from a new senior exec- utive arriving at an established company. At Harrah’s, for example, the recruitment of Gary Loveman as chief operating officer, and eventually CEO, greatly accelerated the company’s analytical orientation and led to a new basis for competition— competing on customer loyalty and service, rather than building the most expensive casino properties. Sometimes the change comes from a new generation of managers
in a family business. At the winemaker E&J Gallo, when Joe Gallo, the son of one of the firm’s founding brothers, became CEO, he intensified the firm’s focus on data and analysis—first in sales, and later in other functions, including the assess- ment of consumer taste.
At the New England Patriots National Football League team, the involvement in the team by Jonathan Kraft, the son of the owner Bob Kraft and a former manage- ment consultant, helped move the team in a more analytical direction both in terms of on-field issues such as play selection and team composition, and off- field issues affecting the fan experience.
The prime mover for analytical demand doesn’t always have to be the CEO. At Procter & Gamble, for example, the pri- mary impetus for more analysis is coming from a vice chairman. However, we did observe two cases in which a single func- tional executive with a strong demand for an analytical orientation was unable to change the culture in that direction. At a consumer products firm, an analytically focused marketing executive made his own function more analytical, but was unsuccessful in moving the entire firm in that direction. Another analytical market- ing and sales executive at an information technology firm was similarly unable to change his firm’s entire culture, although other executives were certainly aware of his strongly data-based management style. In both firms, business intelligence is alive and well, but it has not yet become a key element of strategy. Stimulating Demand Even with willing executives, there is often a need to stimulate demand on an ongo- ing basis. Several firms have created organizational units for this purpose. At Quaker Chemical, each business unit has a “business adviser”—an analytical specialist— reporting to the head of the business unit. The role acts as an intermediary between
Bank of America is facing this issue head-on by incorporating models into its executive development programs that encourage leaders to look at the “three I’s”—insight, intelligence, and ideas—when looking for opportunities to
grow their businesses. The program chal- lenges leaders to look more broadly at the data available to them, both data available internally as well as external data related to customers, competitors, and the broader environment. This program builds on a solid cultural and strategic foundation of using data to drive the business.
Analytical Targets: The
Fine Line Between Spread
and Focus
One challenge in using analytical capabilities to advance strategy is maintaining a balance between depth and focus. Several execu- tives commented in our interviews that a focus on particular business problems and outcomes is necessary if an analytical strategy is to be successful. There is only so much analytical expertise to go around, and only so many business problems on which ana- lytical supply and demand can be focused. Harrah’s, as mentioned above, focuses its efforts on the management of customer loy- alty, and its management team is reluctant to venture very far outside of that orienta- tion. Capital One briefly diversified its appli- cation of analytics into such businesses as cellular phones and flowers, but concluded credit cards and other consumer financial services should remain its focus.
Virtually every firm we interviewed that had built up its analytical capabilities finds demand for them exceeds the supply. Therefore, the use of analytical resources must be prioritized and allocated. Procter &
Gamble, for example, ensures that the efforts of its Global Analytics group are devoted to issues that are highly strategic and worthy of the scarce talent. Although Wachovia has invested significantly in ana- lytical resources, it must still go through an annual planning process (with quarterly adjustments) to ensure that its initiatives are well-targeted.
Customer and Supplier Use
As we noted above, analytical tools and techniques are often used to enhance rela- tions with customers. The most obvious uses of customer analytics are internal, to inform decisions about internal strategies and operations. Quaker Chemical, for exam- ple, uses detailed analysis of its product performance with current customers to win new ones by offering both documentary proof of product quality and evidence of its extensive, experience-based expertise.
Yet we found several of the more advanced analytical competitors offer some elements of their data and analytics directly to their customers and suppliers. Perhaps the best- known example is Wal-Mart, which uses its voluminous data and product demand analyses not only for internal purposes, but also to share with its suppliers through its Retail Link private exchange. All suppliers are required to use the system.
Wal-Mart is not alone in sharing data. Progressive Insurance, for example, shares pricing data—its own and that of competi- tors—with customers. The company also offers customers the possibility of lower rates if they accept a device in their cars that gathers data about driving activity.
Some firms share both data and analyses with their customers. Procter & Gamble offers data and analytics as a service to its retail customers as part of a program it calls “Joint Value Creation,” and to its sup- pliers in order to help them improve their responsiveness and costs. The hospital sup- plier Owens & Minor provides data and analyses for its customers and suppliers, enabling them to access and analyze their buying and selling data, track ordering pat- terns to look for ways to consolidate orders, and move off-contract product purchases to a group contract—for products distributed by Owens & Minor or its competitors. The winemaker E&J Gallo provides its distribu- tors with data and analytics that lets them determine how best to convince retailers to add shelf space for Gallo wines. Finally, the Hong Kong-based Octopus Cards, a provider of electronic stored value cards for public transport, provides retailers with data on the customers who pass nearby the retailers’ facilities, and runs promotions encouraging customers to use the Octopus Cards for retail purchases.
How Long Does the
Change Take?
Firms desiring to compete on analytics will naturally wonder how long it takes to implement such a strategy. The best advice is to begin working on it now, because it typically requires several years for analytical competitive strategies to come to fruition. Barclays Consumer Finance, for example, embarked upon a five-year plan to apply analytical approaches to marketing credit cards and other financial products to its customers. It takes time to refine the systems that produce transaction data, to
“We’ve been collecting data for six or seven years,
but it's only become usable in the last two or
three, with enough time and experience to validate
conclusions based on the data.”
— Manager of Customer Data at UPS
make the data available in warehouses, to select and implement analytical software, and to build a robust hardware and com- munications environment. Firms planning to embark upon analytical competition should have a hardware and software plan for how they will achieve the needed capabilities. It should address such issues as the amount of data to be processed, the number of users of the analytical systems, and the speed of response necessary to meet the business need.
Even more time-consuming at most firms is coming up to speed in human capabilities, to optimize business processes based on the outputs of analysis, and, in some cases, to build a sufficient body of data to support reliable predictive results. At UPS, one man- ager of customer data analytics noted that:
“We’ve been collecting data for six or seven years, but it’s only become usable in the last two or three, with enough time and experience to vali- date conclusions based on data.”
Several executives at other firms noted that it takes time for managers to understand data and be comfortable with the analytics based on it. An analytical executive at Procter & Gamble suggested firms might begin to keep managers in their jobs for longer periods because of the time required to master analytical approaches to their businesses.
One manager of an analytical group in a consumer products firm pointed out that the longevity of analytical capabilities is crit- ical to their value; his firm has been pursuing analytical capabilities for more than 50 years. This executive pointed out that not all proj- ects will be successful, so analytical groups need to build up a broad portfolio of exec- utive relationships, projects, and analytical technologies. He also suggested that short- term, project-based funding of analytical resources is inconsistent with the long-term nature of analytical competition.
However, despite the difficulty and expense of establishing these capabilities, many of the firms we have identified as early adopters of analytical strategies are clear leaders in their industries. This suggests the time and trouble necessary to become ana- lytical competitors are definitely worthwhile.
Summary
This study has provided a glimpse into a new form of competition. Instead of competing on traditional factors, companies are beginning to employ statistical and quantitative analysis and predictive modeling as primary elements of competition. These firms have overcome the historical barriers to gathering and managing transaction data and some of the cultural resistance in organizations accustomed to “gut-feel” decision making, and are using com- plex analysis and data-intensive decisions to change the way they manage themselves and compete in the marketplace. They have mar- shaled both supply and demand factors for analytical competition, and are employing their capabilities across multiple functions.
Opportunities for analytical competition are possible in every industry. Therefore, virtually every firm should consider how it might adopt analytical methods and capabilities. Figure 3 summarizes key action steps that firms should consider in moving toward analytical competi- tion. While not all of the steps will be applica- ble to all organizations, it’s likely everyone would find some of them appropriate.
There is every reason to believe this approach will grow in acceptance. The necessary data will become increasingly available, and the analytical resources are increasingly accessible to all. Yet the move to analytical competitive- ness is typically a journey of several years. Companies that do not rapidly embrace these possibilities risk falling dramatically behind. No business can afford to lose its best cus- tomers, to spend more on logistics and inven- tory, to miss out on opportunities for new products and services, and to hire less capa- ble employees than its more analytically astute competitors.
Figure 3:
Action Steps for Analytical
Competition
1. Begin to build analytical skills —It’s often difficult to find individuals with the requisite quantitative and business skills. Organizations should start looking for them as soon as possible, and hire them in sufficient volume to create “critical mass.” 2. Get your data in shape— Analytical environments require large amounts of high-quality data. Figure out what data you really need to advance your strategy, make sure it’s being gathered, and clean it up. 3. Implement analytical technology— You’ll need heavy-duty hardware and software to do serious analytical work. Start putting it in place today. 4. Examine your business strategy— Analytical competition requires a clear business strategy that is optimized with data and analysis. Your executives should begin to consider what key processes and strategic initiatives would be advanced if the right analytics were available. 5. Find an executive partner— Since the most difficult factor to put in place in analytical competition is demand from senior executives, you should begin to cultivate that demand by finding an exec- utive partner and embarking with him or her on some analytical initiatives.
This research report is part of an ongoing research study at Babson on how compa- nies compete with analytics. The research was carried out independently, but was sponsored by SAS and Intel. To learn more about or participate in the research, contact Tom Davenport at tdavenport@babson.edu.
