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January 2006
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Competing on Analytics | |
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| | Some 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. | |
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| | by Thomas H. Davenport | |
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| | We
all know the power of the killer 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 heralded—and
coveted—applications 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 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 because they can—business today is awash in data and data
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 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 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 personnel contribute to or
detract from the bottom line and how salary levels 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 way, 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 best decisions: big and small, every day, over and over and
over.
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.
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Although numerous organizations are embracing analytics, only a handful have achieved this level of proficiency. But analytics
competitors are the leaders in their varied fields—consumer products,
finance, retail, and travel and entertainment 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 sports, the real
secret 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
purchases. Thanks to Michael Lewis’s best-selling book Moneyball,
which demonstrated the power of statistics in professional 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
competitors, I and two of my colleagues at Babson College’s Working
Knowledge Research Center studied 32 organizations that have made a
commitment to quantitative, fact-based analysis. Eleven 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 enterprise (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
investment 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 Loveman, CEO of analytics competitor 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 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 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 catering, 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 the system’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 computes
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 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 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 customers—plus
those with the greatest profit 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 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 relate 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 year, 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
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 company performs a regression analysis to
identify factors that most closely 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 profitably 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 approach. Analytics
competitors understand that most business functions—even those, like
marketing, that have historically depended on art rather than
science—can be improved with sophisticated quantitative 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
practitioners of operations research and industrial engineering, its
capabilities were, until fairly recently, narrowly focused. Today, UPS
is wielding its statistical skill to track the movement of packages and
to anticipate and influence 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 examining usage patterns and complaints.
When the data point to a potential defector, a salesperson contacts
that customer to review and resolve the problem, dramatically reducing
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 management” at Barclays Bank. These programs operate not just
under a common label but also under common leadership and with common
technology and tools. In traditional companies, “business intelligence”
(the term IT people use for analytics
and reporting processes and software) 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 organization. Furthermore, research has shown that
between 20% and 40% of spreadsheets contain errors; the more
spreadsheets floating around a company, therefore, the more fecund the
breeding ground for mistakes. Analytics
competitors, by contrast, field centralized groups to ensure that
critical data and other resources are well managed and that different
parts of the organization can share data easily, without the
impediments of inconsistent formats, definitions, and standards.
In traditional companies, departments manage analytics —number-crunching functions select their own tools and train their own people. But that way, chaos lies.
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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 research, and
marketing. Although most of the analysts are embedded in business
operating units, the group is centrally managed. As a result of this
consolidation, P&G can apply a critical mass of expertise to its
most pressing issues. So, for example, sales and marketing analysts
supply data on opportunities for growth in existing markets to analysts
who design corporate supply networks. The supply chain analysts, in
turn, apply their expertise in certain decision-analysis techniques to
such new areas as competitive intelligence.
The group at P&G also raises the visibility of analytical and
data-based decision making within the company. Previously, P&G’s
crack analysts had improved business processes 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 projects. Meanwhile, masterful number
crunching has become 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, behavior, 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
approach. 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 Amazon, and Rich Fairbank of Capital One.
Before he retired from the Sara Lee Bakery Group, former CEO Barry
Beracha kept a sign on his desk that summed up his personal and
organizational philosophy: “In God we trust. All others bring data.” We
did come across some companies in which a single functional or business
unit leader was trying 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 appreciation of and a familiarity with the
subject. A background in statistics isn’t necessary, but those leaders
must understand the theory behind various quantitative methods so that
they recognize those methods’ limitations—which factors are being
weighed and which ones aren’t. When the CEOs need help grasping
quantitative techniques, 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 explain 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, consultants, MIT graduates, and the like.
But that was a personal preference rather than a necessary practice.
 Going to Bat for Stats
Of course, not all decisions should be grounded in analytics—at
least not wholly so. Personnel matters, in particular, are often well
and appropriately informed by instinct 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 challenge boils down to knowing when to run with the numbers and when to run with their guts.
Their Sources of Strength
Analytics
competitors are more than simple number-crunching factories. Certainly,
they apply technology—with a mixture of brute force and finesse—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 technology, give
such organizations strength.
The right focus. Although analytics
competitors encourage 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 loyalty, customer service, and related
areas like pricing and promotions. UPS has broadened its focus from
logistics to customers, in the interest of providing superior service.
While such multipronged strategies define analytics
competitors, executives we interviewed warned companies 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 activity, and specific industries may present
their own (see “Things You Can Count On”). Statistical models and
algorithms that dangle the possibility of performance breakthroughs
make some prospects especially tempting. Marketing, 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. Similarly, 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 product movement
by store, to plan promotions and layouts within stores, and to reduce
stock-outs. E.&J. Gallo provides distributors with data and
analysis on retailers’ costs and pricing so they can calculate the
per-bottle profitability 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 responsiveness and reduce costs. Hospital
supplier Owens & Minor furnishes similar services, enabling
customers and suppliers to access and analyze their buying and selling
data, track ordering patterns in search of consolidation opportunities,
and move off-contract purchases to group contracts 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
locations or help them see the trade-offs between increasing delivery
frequency and carrying inventory.
The most proficient analytics practitioners don’t just measure their own navels—they also help customers and vendors measure theirs.
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The right culture. Culture is a soft concept; analytics is a hard discipline. Nonetheless, analytics
competitors must instill a companywide respect for measuring, testing,
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 resource organizations within analytics
competitors are rigorous about applying metrics to compensation and
rewards. 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 employees as a “data dog”
because he hounded them for data to support any assertion or
hypothesis.
Not surprisingly, in an analytics
culture, there’s sometimes tension between innovative or
entrepreneurial impulses and the requirement for evidence. Some
companies place less emphasis on blue-sky development, in which
designers or engineers chase after a gleam in someone’s eye. In these
organizations, R&D, like other functions, 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 example, might create profiles of managers’ personality traits
and leadership styles and then test those managers in different
situations. It could then compare data on individuals’ performance with
data about personalities to determine 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 possible to gather data beforehand. For example, even
though Amazon’s Jeff Bezos greatly prefers to rigorously quantify
users’ reactions before rolling out new features, he couldn’t test the
company’s search-inside-the-book offering without applying it to a
critical mass of books (120,000, 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 people—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 entrepreneur 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 processes,” 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 experiments and, with the results in hand, design credit
card and other financial offers. These efforts call for a specialized
skill set, as you can see from this job description (typical for a
Capital One analyst):
High conceptual problem-solving and quantitative analytical
aptitudes…Engineering, financial, consulting, and/or other analytical
quantitative educational/work background. Ability to quickly learn how
to use software 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 currently
seeking three times as many analysts as operations people—hardly the
common practice for a bank. “We are really a company of analysts,” one
executive there noted. “It’s the primary job in this place.”
Good analysts must also have the ability to express complex ideas
in simple terms and have the relationship skills to interact well with
decision makers. One consumer products company with a 30-person analytics
group looks for what it calls “PhDs with personality”—people with
expertise in math, statistics, and data analysis who can also speak the
language of business and help market their work internally and
sometimes externally. The head of a customer analytics
group at Wachovia Bank describes the rapport with others his group
seeks: “We are trying to build our people as part of the business
team,” he explains. “We want them sitting at the business table,
participating in a discussion of what the key issues are, determining
what information needs the businesspeople have, and recommending
actions to the business partners. We want this [analytics group] to be not just a general utility, but rather an active and critical part of the business unit’s success.”
Of course, a combination of analytical, business, and relationship
skills may be difficult to find. When the software company SAS (a
sponsor of this research, along with Intel) knows it will need an
expert in state-of-the-art business applications such as predictive
modeling or recursive partitioning (a form of decision tree analysis
applied to very complex data sets), it begins recruiting up to 18
months before it expects to fill the position.
In fact, analytical talent may be to the early 2000s what
programming talent was to the late 1990s. Unfortunately, the U.S. and
European labor markets aren’t exactly teeming with analytically
sophisticated job candidates. Some organizations cope by contracting
work to countries such as India, home to many statistical experts. That
strategy may succeed when offshore analysts work on stand-alone
problems. But if an iterative discussion with business decision makers
is required, the distance can become a major barrier.
The right technology. Competing on analytics means competing
on technology. And while the most serious competitors investigate the
latest statistical algorithms and decision science approaches, they
also constantly monitor and push the IT frontier. The analytics
group at one consumer products company went so far as to build its own
supercomputer because it felt that commercially available models were
inadequate for its demands. Such heroic feats usually aren’t necessary,
but serious analytics does require the following:
A data strategy. Companies
have invested many millions of dollars in systems that snatch data from
every conceivable source. Enterprise resource planning, customer
relationship management, point-of-sale, and other systems ensure that
no transaction or other significant exchange occurs without leaving a
mark. But to compete on that information, companies must present it in
standard formats, integrate it, store it in a data warehouse, and make
it easily accessible to anyone and everyone. And they will need a lot
of it. For example, a company may spend several years accumulating data
on different marketing approaches before it has gathered enough to
reliably analyze the effectiveness of an advertising campaign. Dell
employed DDB Matrix, a unit of the advertising agency DDB Worldwide, to
create (over a period of seven years) a database that includes 1.5
million records on all the computer maker’s print, radio, network TV,
and cable ads, coupled with data on Dell sales for each region in which
the ads appeared (before and after their appearance). That information
allows Dell to fine-tune its promotions for every medium in every
region.
Business intelligence software. The
term “business intelligence,” which first popped up in the late 1980s,
encompasses a wide array of processes and software used to collect,
analyze, and disseminate data, all in the interests of better decision
making. Business intelligence tools allow employees to extract,
transform, and load (or ETL, as people in the industry would say) data
for analysis and then make those analyses available in reports, alerts,
and scorecards. The popularity of analytics competition is partly a response to the emergence of integrated packages of these tools.
Computing hardware. The volumes of data required for analytics applications may strain the capacity of low-end computers and servers. Many analytics competitors are converting their hardware to 64-bit processors that churn large amounts of data quickly.
The Long Road Ahead
Most companies in most industries have excellent reasons to pursue strategies shaped by analytics. Virtually all the organizations we identified as aggressive analytics
competitors are clear leaders in their fields, and they attribute much
of their success to the masterful exploitation of data. Rising global
competition intensifies the need for this sort of proficiency. Western
companies unable to beat their Indian or Chinese competitors on product
cost, for example, can seek the upper hand through optimized business
processes.
Companies just now embracing such strategies, however, will find
that they take several years to come to fruition. The organizations in
our study described a long, sometimes arduous journey. The UK Consumer
Cards and Loans business within Barclays Bank, for example, spent five
years executing its plan to apply analytics
to the marketing of credit cards and other financial products. The
company had to make process changes in virtually every aspect of its
consumer business: underwriting risk, setting credit limits, servicing
accounts, controlling fraud, cross selling, and so on. On the technical
side, it had to integrate data on 10 million Barclaycard customers,
improve the quality of the data, and build systems to step up data
collection and analysis. In addition, the company embarked on a long
series of small tests to begin learning how to attract and retain the
best customers at the lowest price. And it had to hire new people with
top-drawer quantitative skills.
Much of the time—and corresponding expense—that any company takes to become an analytics
competitor will be devoted to technological tasks: refining the systems
that produce transaction data, making data available in warehouses,
selecting and implementing analytic software, and assembling the
hardware and communications environment. And because those who don’t
record history are doomed not to learn from it, companies that have
collected little information—or the wrong kind—will need to amass a
sufficient body of data to support reliable forecasting. “We’ve been
collecting data for six or seven years, but it’s only become usable in
the last two or three, because we needed time and experience to
validate conclusions based on the data,” remarked a manager of customer
data analytics at UPS.
You Know You Compete on Analytics When...
And, of course, new analytics
competitors will have to stock their personnel larders with fresh
people. (When Gary Loveman became COO, and then CEO, of Harrah’s, he
brought in a group of statistical experts who could design and
implement quantitatively based marketing campaigns and loyalty
programs.) Existing employees, meanwhile, will require extensive
training. They need to know what data are available and all the ways
the information can be analyzed; and they must learn to recognize such
peculiarities and shortcomings as missing data, duplication, and
quality problems. An analytics-minded
executive at Procter & Gamble suggested to me that firms should
begin to keep managers in their jobs for longer periods because of the
time required to master quantitative approaches to their businesses.
The German pathologist Rudolph Virchow famously called the task of science “to stake out the limits of the knowable.” Analytics
competitors pursue a similar goal, although the universe they seek to
know is a more circumscribed one of customer behavior, product
movement, employee performance, and financial reactions. Every day,
advances in technology and techniques give companies a better and
better handle on the critical minutiae of their operations.
The Oakland A’s aren’t the only ones playing moneyball. Companies of every stripe want to be part of the game.
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Reprint Number R0601H
| Harvard Business Review OnPoint edition 3005
| Harvard Business Review OnPoint collection 3048
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Thomas H. Davenport (tdavenport@babson.edu)
is the President’s Distinguished Professor of Information Technology
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). |
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