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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

O ne analytics competitor that’s at the top of its

game is Marriott International. Over the past 20

years, the corporation has honed to a science its

system 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 ([email protected]) 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

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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 of people – assessing the likelihood of

customer attrition and identifying sources of problems.

The UPS Customer Intelligence Group, for example, is

able to accurately predict customer defections by examin-

ing usage patterns and complaints. When the data point

to a potential defector, a salesperson contacts that cus-

tomer to review and resolve the problem, dramatically re-

ducing the loss of accounts. UPS still lacks the breadth of

initiatives of a full-bore analytics competitor, but it is

heading in that direction.

Analytics competitors treat all such activities from all

provenances as a single, coherent initiative, often massed

under one rubric, such as “information-based strategy”

at Capital One or “information-based customer manage-

ment” at Barclays Bank. These programs operate not just

under a common label but also under common leader-

ship and with common technology and tools. In tradi-

tional companies, “business intelligence” (the term IT

people use for analytics and reporting processes and soft-

ware) is generally managed by departments; number-

crunching functions select their own tools, control their

own data warehouses, and train their own people. But

that way, chaos lies. For one thing, the proliferation of

user-developed spreadsheets and databases inevitably

leads to multiple versions of key indicators within an or-

ganization. Furthermore, research has shown that be-

tween 20% and 40% of spreadsheets contain errors; the

more spreadsheets floating around a company, therefore,

the more fecund the breeding ground for mistakes. Ana-

lytics competitors, by contrast, field centralized groups to

ensure that critical data and other resources are well man-

aged and that different parts of the organization can

share data easily, without the impediments of inconsis-

tent formats, definitions, and standards.

Some analytics competitors apply the same enterprise

approach to people as to technology. Procter & Gamble,

for example, recently created a kind of überanalytics

group consisting of more than 100 analysts from such

functions as operations, supply chain, sales, consumer re-

search, and marketing. Although most of the analysts are

embedded in business operating units, the group is cen-

102 harvard business review

DECISION MAKING

The analysis-versus-instinct debate, a favorite of political commentators during the last two U.S. presidential elec- tions, is raging in professional sports, thanks to several popular books and high-profile victories. For now, analysis seems to hold the lead.

Most notably, statistics are a major part of the selec- tion and deployment of players. Moneyball, by Michael Lewis, focuses on the use of analytics in player selection for the Oakland A’s – a team that wins on a shoestring. The New England Patriots, a team that devotes an enormous amount of attention to statistics, won three of the last four Super Bowls, and their payroll is currently ranked 24th in the league. The Boston Red Sox have embraced “sabermet- rics” (the application of analysis to baseball), even going so far as to hire Bill James, the famous baseball statistician who popularized that term. Analytic HR strategies are tak- ing hold in European soccer as well. One leading team, Italy’s A.C. Milan, uses predictive models from its Milan Lab research center to prevent injuries by analyzing physi- ological, orthopedic, and psychological data from a variety of sources. A fast-rising English soccer team, the Bolton

Wanderers, is known for its manager’s use of extensive data to evaluate players’ performance.

Still, sports managers – like business leaders – are rarely fact-or-feeling purists. St. Louis Cardinals manager Tony La Russa, for example, brilliantly combines analytics with in- tuition to decide when to substitute a charged-up player in the batting lineup or whether to hire a spark-plug per- sonality to improve morale. In his recent book, Three Nights in August, Buzz Bissinger describes that balance: “La Russa appreciated the information generated by com- puters. He studied the rows and the columns. But he also knew they could take you only so far in baseball, maybe even confuse you with a fog of overanalysis. As far as he knew, there was no way to quantify desire. And those num- bers told him exactly what he needed to know when added to twenty-four years of managing experience.”

That final sentence is the key. Whether scrutinizing someone’s performance record or observing the expres- sion flitting across an employee’s face, leaders consult their own experience to understand the “evidence” in all its forms.

GOING TO BAT FOR STATS

trally managed. As a result of this consolidation, P&G can

apply a critical mass of expertise to its most pressing is-

sues. So, for example, sales and marketing analysts supply

data on opportunities for growth in existing markets to

analysts who design corporate supply networks. The sup-

ply chain analysts, in turn, apply their expertise in certain

decision-analysis techniques to such new areas as compet-

itive intelligence.

The group at P&G also raises the visibility of analytical

and data-based decision making within the company. Pre-

viously, P&G’s crack analysts had improved business pro-

cesses and saved the firm money; but because they were

squirreled away in dispersed domains, many executives

didn’t know what services they offered or how effective

they could be. Now those executives are more likely to

tap the company’s deep pool of expertise for their proj-

ects. Meanwhile, masterful number crunching has be-

come part of the story P&G tells to investors, the press,

and the public.

Senior executive advocates. A companywide embrace of analytics impels changes in culture, processes, behav-

ior, and skills for many employees. And so, like any major

transition, it requires leadership from executives at the

very top who have a passion for the quantitative ap-

proach. Ideally, the principal advocate is the CEO. Indeed,

we found several chief executives who have driven the

shift to analytics at their companies over the past few

years, including Loveman of Harrah’s, Jeff Bezos of Ama-

zon, and Rich Fairbank of Capital One. Before he retired

from the Sara Lee Bakery Group, former CEO Barry Be-

racha kept a sign on his desk that summed up his personal

and organizational philosophy: “In God we trust. All oth-

ers bring data.” We did come across some companies in

which a single functional or business unit leader was try-

ing to push analytics throughout the organization, and

a few were making some progress. But we found that

these lower-level people lacked the clout, the perspective,

and the cross-functional scope to change the culture in

any meaningful way.

CEOs leading the analytics charge require both an ap-

preciation of and a familiarity with the subject. A back-

ground in statistics isn’t necessary, but those leaders must

understand the theory behind various quantitative meth-

ods so that they recognize those methods’ limitations –

which factors are being weighed and which ones aren’t.

When the CEOs need help grasping quantitative tech-

niques, they turn to experts who understand the business

and how analytics can be applied to it. We interviewed

several leaders who had retained such advisers, and these

executives stressed the need to find someone who can ex-

plain things in plain language and be trusted not to spin

the numbers. A few CEOs we spoke with had surrounded

themselves with very analytical people – professors, con-

sultants, MIT graduates, and the like. But that was a per-

sonal preference rather than a necessary practice.

Of course, not all decisions should be grounded in ana-

lytics – at least not wholly so. Personnel matters, in partic-

ular, are often well and appropriately informed by in-

stinct and anecdote. More organizations are subjecting

recruiting and hiring decisions to statistical analysis (see

the sidebar “Going to Bat for Stats”). But research shows

that human beings can make quick, surprisingly accurate

assessments of personality and character based on simple

observations. For analytics-minded leaders, then, the chal-

lenge boils down to knowing when to run with the num-

bers and when to run with their guts.

Their Sources of Strength

A nalytics competitors are more than simple num-

ber-crunching factories. Certainly, they apply

technology – with a mixture of brute force and fi-

nesse – to multiple business problems. But they

also direct their energies toward finding the right focus,

building the right culture, and hiring the right people to

make optimal use of the data they constantly churn. In

the end, people and strategy, as much as information tech-

nology, give such organizations strength.

The right focus. Although analytics competitors en- courage universal fact-based decisions, they must choose

where to direct resource-intensive efforts. Generally, they

pick several functions or initiatives that together serve an

overarching strategy. Harrah’s, for example, has aimed

much of its analytical activity at increasing customer loy-

alty, customer service, and related areas like pricing and

promotions. UPS has broadened its focus from logistics

to customers, in the interest of providing superior ser-

vice. While such multipronged strategies define analytics

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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.

competitors, executives we interviewed warned compa-

nies against becoming too diffuse in their initiatives or

losing clear sight of the business purpose behind each.

Another consideration when allocating resources is

how amenable certain functions are to deep analysis.

There are at least seven common targets for analytical ac-

tivity, and specific industries may present their own (see

“Things You Can Count On”). Statistical models and algo-

rithms that dangle the possibility of performance break-

throughs make some prospects especially tempting. Mar-

keting, for example, has always been tough to quantify

because it is rooted in psychology. But now consumer

products companies can hone their market research using

multiattribute utility theory – a tool for understanding

and predicting consumer behaviors and decisions. Simi-

larly, the advertising industry is adopting econometrics –

statistical techniques for measuring the lift provided by

different ads and promotions over time.

The most proficient analytics practitioners don’t just

measure their own navels – they also help customers and

vendors measure theirs. Wal-Mart, for example, insists

that suppliers use its Retail Link system to monitor prod-

uct movement by store, to plan promotions and layouts

within stores, and to reduce stock-outs. E.&J. Gallo pro-

vides distributors with data and analysis on retailers’ costs

and pricing so they can calculate the per-bottle profitabil-

ity for each of Gallo’s 95 wines. The distributors, in turn,

use that information to help retailers optimize their

mixes while persuading them to add shelf space for Gallo

products. Procter & Gamble offers data and analysis to its

retail customers, as part of a program called Joint Value

Creation, and to its suppliers to help improve responsive-

ness and reduce costs. Hospital supplier Owens & Minor

furnishes similar services, enabling customers and suppli-

ers to access and analyze their buying and selling data,

track ordering patterns in search of consolidation oppor-

tunities, and move off-contract purchases to group con-

tracts that include products distributed by Owens & Minor

and its competitors. For example, Owens & Minor might

show a hospital chain’s executives how much money they

could save by consolidating purchases across multiple lo-

cations or help them see the trade-offs between increas-

ing delivery frequency and carrying inventory.

The right culture. Culture is a soft concept; analytics is a hard discipline. Nonetheless, analytics competitors

must instill a companywide respect for measuring, test-

ing, and evaluating quantitative evidence. Employees are

urged to base decisions on hard facts. And they know that

their performance is gauged the same way. Human re-

source organizations within analytics competitors are rig-

orous about applying metrics to compensation and re-

wards. Harrah’s, for example, has made a dramatic change

from a rewards culture based on paternalism and tenure

to one based on such meticulously collected performance

measurements as financial and customer service results.

Senior executives also set a consistent example with their

own behavior, exhibiting a hunger for and confidence in

fact and analysis. One exemplar of such leadership was

Beracha of the Sara Lee Bakery Group, known to his em-

ployees as a “data dog” because he hounded them for data

to support any assertion or hypothesis.

Not surprisingly, in an analytics culture, there’s some-

times tension between innovative or entrepreneurial im-

pulses and the requirement for evidence. Some compa-

nies place less emphasis on blue-sky development, in

which designers or engineers chase after a gleam in some-

one’s eye. In these organizations, R&D, like other func-

tions, is rigorously metric-driven. At Yahoo, Progressive,

and Capital One, process and product changes are tested

on a small scale and implemented as they are validated.

That approach, well established within various academic

and business disciplines (including engineering, quality

management, and psychology), can be applied to most

corporate processes – even to not-so-obvious candidates,

like human resources and customer service. HR, for exam-

ple, might create profiles of managers’ personality traits

and leadership styles and then test those managers in dif-

ferent situations. It could then compare data on individ-

uals’ performance with data about personalities to de-

termine what traits are most important to managing a

project that is behind schedule, say, or helping a new

group to assimilate.

There are, however, instances when a decision to

change something or try something new must be made

too quickly for extensive analysis, or when it’s not possi-

ble to gather data beforehand. For example, even though

Amazon’s Jeff Bezos greatly prefers to rigorously quan-

tify users’ reactions before rolling out new features, he

couldn’t test the company’s search-inside-the-book offer-

ing without applying it to a critical mass of books (120,000,

104 harvard business review

DECISION MAKING

In traditional companies, departments manage analytics – number-crunching functions select their own tools

and train their own people. But that way, chaos lies.

to begin with). It was also expensive to develop, and that

increased the risk. In this case, Bezos trusted his instincts

and took a flier. And the feature did prove popular when

introduced.

The right people. Analytical firms hire analytical peo- ple – and like all companies that compete on talent, they

pursue the best. When Amazon needed a new head for

its global supply chain, for example, it recruited Gang Yu,

a professor of management science and software entre-

preneur who is one of the world’s leading authorities on

optimization analytics. Amazon’s business model requires

the company to manage a constant flow of new products,

suppliers, customers, and promotions, as well as deliver

orders by promised dates. Since his arrival, Yu and his team

have been designing and building sophisticated supply

chain systems to optimize those processes. And while he

tosses around phrases like “nonstationary stochastic pro-

cesses,” he’s also good at explaining the new approaches to

Amazon’s executives in clear business terms.

Established analytics competitors such as Capital One

employ squadrons of analysts to conduct quantitative ex-

periments and, with the results in hand, design credit card

and other financial offers. These efforts call for a special-

ized skill set, as you can see from this job description (typ-

ical for a Capital One analyst):

High conceptual problem-solving and quantitative an-

alytical aptitudes…Engineering, financial, consulting,

and/or other analytical quantitative educational/work

background. Ability to quickly learn how to use soft-

ware applications. Experience with Excel models.

Some graduate work preferred but not required (e.g.,

MBA). Some experience with project management

methodology, process improvement tools (Lean, Six

Sigma), or statistics preferred.

Other firms hire similar kinds of people, but analytics

competitors have them in much greater numbers. Capital

One is