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Competing on Analytics

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Abstract

We all know the power of the killer app. It's not just a support tool; it's 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 organization has upped the stakes: Amazon, Harrah's, Capital One, and the Boston Red Sox have all dominated their fields by deploying industrial-strength analytics across a wide variety of activities. At a time when firms in many industries offer similar products and use comparable technologies, business processes are among the few remaining points of differentiation--and analytics competitors wring every last drop of value from those processes. 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. In companies that compete on analytics, senior executives make it clear--from the top down--that analytics is central to strategy. Such organizations launch multiple initiatives involving complex data and statistical analysis, and quantitative activity is managed atthe enterprise (not departmental) level. In this article, professor Thomas H. Davenport 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 company-wide strategies for managing the data. But, at least as important, it also requires executives' vocal, unswerving commitment and willingness to change the way employees think, work, and are treated.
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Competing on
Analytics
by Thomas H. Davenport
Included with this full-text
Harvard Business Review
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The Idea in Brief—the core idea
The Idea in Practice—
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uttin
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Article Summary
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Competing on Analytics
A list of related materials, with annotations to guide further
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Further Reading
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.
Reprint R0601H
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Competing on Analytics
page 1
The Idea in Brief The Idea in Practice
COPYRIGHT © 2005 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.
It’s virtually impossible to differentiate your-
self from competitors based on products
alone. Your rivals sell offerings similar to
yours. And thanks to cheap offshore labor,
you’re hard-pressed to beat overseas com-
petitors on product cost.
How to pull ahead of the pack? Become an
analytics competitor:
Use sophisticated
data-collection technology and analysis to
wring every last drop of value from all your
business processes. With analytics, you dis-
cern not only what your customers want
but also how much they’re willing to pay
and what keeps them loyal. You look be-
yond compensation costs to calculate your
workforce’s exact contribution to your bot-
tom line. And you don’t just track existing
inventories; you also predict and prevent
future inventory problems.
Analytics competitors seize the lead in their
fields. Capital One’s analytics initiative, for
example, has spurred at least 20% growth
in earnings per share every year since the
company went public.
Make analytics part of
your
overarching
competitive strategy, and push it down to
decision makers at every level. You’ll arm
your employees with the best evidence
and quantitative tools for making the best
decisions—big and small, every day.
To become an analytics competitor:
Champion Analytics from the Top
Acknowledge and endorse the changes in
culture, processes, and skills that analytics
competition will mean for much of your work-
force. And prepare yourself to lead an analyt-
ics-focused organization: You will have to un-
derstand the theory behind various
quantitative methods so you can recognize
their limitations. If you lack background in sta-
tistical methods, consult experts who under-
stand your business and know how analytics
can be applied to it.
Create a Single Analytics Initiative
Place all data-collection and analysis activities
under a common leadership, with common
technology and tools. You’ll facilitate data
sharing and avoid the impediments of incon-
sistent reporting formats, data definitions, and
standards.
Example:
Procter & Gamble created a centrally man-
aged “überanalytics” group of 100 analysts
drawn from many different functions. It ap-
plies this critical mass of expertise to press-
ing cross-functional issues. For instance,
sales and marketing analysts supply data
on growth opportunities in existing mar-
kets to supply-chain analysts, who can then
design more responsive supply networks.
Focus Your Analytics Effort
Channel your resources into analytics initia-
tives that most directly serve your overarching
competitive strategy. Harrah’s, for instance,
aims much of its analytical activity at improv-
ing customer loyalty, customer service, and re-
lated areas such as pricing and promotions.
Establish an Analytics Culture
Instill a companywide respect for measuring,
testing, and evaluating quantitative evidence.
Urge employees to base decisions on hard
facts. Gauge and reward performance the
same way—applying metrics to compensa-
tion and rewards.
Hire the Right People
Pursue and hire analysts who possess top-
notch quantitative-analysis skills, can express
complex ideas in simple terms, and can inter-
act productively with decision makers. This
combination may be difficult to find, so start
recruiting well before you need to fill analyst
positions.
Use the Right Technology
Prepare to spend significant resources on tech-
nology such as customer relationship manage-
ment (CRM) or enterprise resource planning
(ERP) systems. Present data in standard formats,
integrate it, store it in a data warehouse, and
make it easily accessible to everyone. And ex-
pect to spend years gathering enough data to
conduct meaningful analyses.
Example:
It took Dell Computer seven years to create
a database that includes 1.5 million records
of all its print, radio, broadcast TV, and cable
ads. Dell couples the database with data on
sales for each region in which the ads ap-
peared (before and after their appearance).
The information enables Dell to fine-tune
its promotions for every medium—in every
region.
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Competing on
Analytics
by Thomas H. Davenport
harvard business review • january 2006 page 2
COPYRIGHT © 2005 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.
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.
We all know the power of the killer app. Over
the years, groundbreaking systems from com-
panies such as American Airlines (electronic
reservations), Otis Elevator (predictive main-
tenance), and American Hospital Supply (on-
line ordering) have dramatically boosted their
creators’ revenues and reputations. These her-
alded—and coveted—applications amassed
and applied data in ways that upended cus-
tomer expectations and optimized operations
to unprecedented degrees. They transformed
technology from a supporting tool into a stra-
tegic weapon.
Companies questing for killer apps generally
focus all their firepower on the one area that
promises to create the greatest competitive ad-
vantage. But a new breed of company is up-
ping the stakes. Organizations such as Ama-
zon, Harrah’s, Capital One, and the Boston Red
Sox have dominated their fields by deploying
industrial-strength analytics across a wide vari-
ety of activities. In essence, they are transform-
ing 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 be-
cause 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 differ-
entiation. 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 compensa-
tion 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.
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Competing on Analytics
harvard business review • january 2006 page 3
And analytics competitors do all those
things in a coordinated way, as part of an over-
arching 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 impor-
tance 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 em-
bracing analytics, only a handful have achieved
this level of proficiency. But analytics competi-
tors are the leaders in their varied fields—con-
sumer products, finance, retail, and travel and
entertainment among them. Analytics has been
instrumental to Capital One, which has ex-
ceeded 20% growth in earnings per share every
year since it became a public company. It has al-
lowed Amazon to dominate online retailing and
turn a profit despite enormous investments 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 En-
gland Patriots, and the Oakland A’s attest.
At such organizations, virtuosity with data is
often part of the brand. Progressive makes ad-
vertising hay from its detailed parsing of indi-
vidual 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
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 analyt-
ics competitors, I and two of my colleagues at
Babson College’s Working Knowledge Re-
search 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 de-
scribes 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
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 exec-
utives’ vocal, unswerving commitment and
willingness to change the way employees
think, work, and are treated. As Gary Love-
man, 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 am-
bitions are far grander. Through its Total Hotel
Optimization program, Marriott has expanded
its quantitative expertise to areas such as con-
ference facilities and catering, and made re-
lated tools available over the Internet to prop-
erty revenue managers and hotel owners. It has
developed systems to optimize offerings to fre-
quent 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 ar-
riving in Houston). The company has even cre-
ated 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 Mar-
riott’s revenue-management analytics has
taken root throughout the enterprise. The
word is out among property owners and fran-
chisees: If you want to squeeze the most reve-
nue 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; em-
ployees and vendors live the difference every
day. Our study found three key attributes
among analytics competitors:
Widespread use of modeling and optimiza-
tion.
Any company can generate simple de-
scriptive statistics about aspects of its busi-
Thomas H. Davenport
(tdavenport@
babson.edu) is the President’s Distin-
guished Professor of Information Tech-
nology and Management at Babson
College in Babson Park, Massachusetts,
the director of research at Babson Exec-
utive Education, and a fellow at Accen-
ture. He is the author of
Thinking for a
Living
(Harvard Business School Press,
2005).
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Competing on Analytics
harvard business review • january 2006 page 4
ness—average revenue per employee, for ex-
ample, 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 an-
alyze more deeply than do their less statisti-
cally savvy competitors) for a comprehensive
understanding of their customers. They opti-
mize their supply chains and can thus deter-
mine 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 cre-
ate 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 subse-
quent 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
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 acci-
dents. For each cell, the company performs a
regression analysis to identify factors that most
closely correlate with the losses that group en-
genders. 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 impli-
cations of those hypotheses. With this ap-
proach, Progressive can profitably insure cus-
tomers 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 compet-
itors understand that most business func-
tions—even those, like marketing, that have
historically depended on art rather than sci-
ence—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 com-
petitor. Although the company is among the
world’s most rigorous practitioners of opera-
tions research and industrial engineering, its ca-
pabilities 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 peo-
ple—assessing the likelihood of customer attri-
tion and identifying sources of problems. The
UPS Customer Intelligence Group, for exam-
ple, is able to accurately predict customer de-
fections by examining usage patterns and com-
plaints. 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 ini-
tiative, 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 tech-
nology and tools. In traditional 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 inevita-
bly 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 spread-
sheets floating around a company, therefore,
the more fecund the breeding ground for mis-
takes. Analytics competitors, by contrast, field
centralized groups to ensure that critical data
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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Competing on Analytics
harvard business review • january 2006 page 5
and other resources are well managed and that
different parts of the organization can share
data easily, without the impediments of incon-
sistent formats, definitions, and standards.
Some analytics competitors apply the same
enterprise approach to people as to technol-
ogy. Procter & Gamble, for example, recently
created a kind of überanalytics group consist-
ing of more than 100 analysts from such func-
tions as operations, supply chain, sales, con-
sumer research, and marketing. Although
most of the analysts are embedded in business
operating units, the group is centrally man-
aged. 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 opportuni-
ties for growth in existing markets to analysts
who design corporate supply networks. The
supply chain analysts, in turn, apply their ex-
pertise 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 of-
fered 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 cul-
ture, 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 quantita-
tive approach. Ideally, the principal advocate
is the CEO. Indeed, we found several chief ex-
ecutives who have driven the shift to analytics
at their companies over the past few years, in-
cluding 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 ana-
lytics 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 nec-
essary, but those leaders must understand the
theory behind various quantitative methods so
Going to Bat for Stats
The analysis-versus-instinct debate, a favor-
ite of political commentators during the last
two U.S. presidential elections, 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 selection and deployment of players.
Mon-
eyball,
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
“sabermetrics” (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 taking hold in European soccer as well.
One leading team, Italy’s A.C. Milan, uses pre-
dictive models from its Milan Lab research
center to prevent injuries by analyzing physio-
logical, 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 eval-
uate players’ performance.
Still, sports managers—like business lead-
ers—are rarely fact-or-feeling purists. St. Louis
Cardinals manager Tony La Russa, for exam-
ple, brilliantly combines analytics with intu-
ition to decide when to substitute a charged-
up player in the batting lineup or whether to
hire a spark-plug personality to improve mo-
rale. In his recent book,
Three Nights in August,
Buzz Bissinger describes that balance: “La
Russa appreciated the information generated
by computers. 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 numbers 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 expression flitting across an
employee’s face, leaders consult their own ex-
perience to understand the “evidence” in all
its forms.
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Competing on Analytics
harvard business review • january 2006 page 6
that they recognize those methods’ limita-
tions—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 lan-
guage and be trusted not to spin the numbers.
A few CEOs we spoke with had surrounded
themselves with very analytical people—pro-
fessors, consultants, MIT graduates, and the
like. But that was a personal preference rather
than a necessary practice.
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 an-
ecdote. More organizations are subjecting re-
cruiting and hiring decisions to statistical anal-
ysis (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 prob-
lems. But they also direct their energies to-
ward 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 in-
formation technology, give such organizations
strength.
The right focus.
Although analytics compet-
itors 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 in-
creasing 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 supe-
rior service. While such multipronged strate-
FUNCTION DESCRIPTION EXEMPLARS
Supply chain Simulate and optimize supply chain flows; reduce Dell, Wal-Mart, Amazon
inventory and stock-outs.
Customer selection, Identify customers with the greatest profit potential; Harrah’s, Capital One,
loyalty, and service increase likelihood that they will want the product or Barclays
service offering; retain their loyalty.
Pricing Identify the price that will maximize yield, or profit. Progressive, Marriott
Human capital Select the best employees for particular tasks or jobs, New England Patriots,
at particular compensation levels. Oakland A’s, Boston Red Sox
Product and service Detect quality problems early and minimize them. Honda, Intel
quality
Financial Better understand the drivers of financial performance MCI, Verizon
performance and the effects of nonfinancial factors.
Research and Improve quality, efficacy, and, where applicable, safety Novartis, Amazon, Yahoo
development of products and services.
Analytics competitors make expert use of statistics and modeling to improve a wide variety of functions.
Here are some common applications:
THINGS YOU CAN COUNT ON
Copyright © 2005 Harvard Business School Publishing Corporation. All rights reserved.
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Competing on Analytics
harvard business review • january 2006 page 7
gies define analytics competitors, executives
we interviewed warned companies against be-
coming too diffuse in their initiatives or losing
clear sight of the business purpose behind
each.
Another consideration when allocating re-
sources is how amenable certain functions are
to deep analysis. There are at least seven com-
mon targets for analytical activity, and specific
industries may present their own (see “Things
You Can Count On”). Statistical models and al-
gorithms that dangle the possibility of perfor-
mance breakthroughs make some prospects es-
pecially tempting. Marketing, for example, has
always been tough to quantify because it is
rooted in psychology. But now consumer prod-
ucts companies can hone their market re-
search using multiattribute utility theory—a
tool for understanding and predicting con-
sumer behaviors and decisions. Similarly, the
advertising industry is adopting economet-
rics—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 op-
timize their mixes while persuading them to
add shelf space for Gallo products. Procter &
Gamble offers data and analysis to its retail cus-
tomers, as part of a program called Joint Value
Creation, and to its suppliers to help improve
responsiveness and reduce costs. Hospital sup-
plier Owens & Minor furnishes similar services,
enabling customers and suppliers to access and
analyze their buying and selling data, track or-
dering patterns in search of consolidation op-
portunities, and move off-contract purchases
to group contracts that include products dis-
tributed by Owens & Minor and its competi-
tors. 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 fre-
quency and carrying inventory.
The right culture.
Culture is a soft concept;
analytics is a hard discipline. Nonetheless, an-
alytics competitors must instill a company-
wide respect for measuring, testing, and evalu-
ating 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 ser-
vice results. Senior executives also set a consis-
tent example with their own behavior, exhibit-
ing 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 asser-
tion or hypothesis.
Not surprisingly, in an analytics culture,
there’s sometimes tension between innovative
or entrepreneurial impulses and the require-
ment 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 vali-
dated. That approach, well established within
various academic and business disciplines (in-
cluding 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 man-
agers’ personality traits and leadership styles
and then test those managers in different situ-
ations. It could then compare data on individu-
als’ 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 deci-
sion 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 Ama-
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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Competing on Analytics
harvard business review • january 2006 page 8
zon’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 ana-
lytical people—and like all companies that
compete on talent, they pursue the best.
When Amazon needed a new head for its glo-
bal 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 pro-
motions, 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 pro-
cesses. And while he tosses around phrases like
“nonstationary stochastic processes, he’s also
good at explaining the new approaches to Am-
azon’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 fi-
nancial offers. These efforts call for a special-
ized skill set, as you can see from this job de-
scription (typical for a Capital One analyst):
High conceptual problem-solving and
quantitative analytical aptitudes…Engineer-
ing, financial, consulting, and/or other analyti-
cal quantitative educational/work background.
Ability to quickly learn how to use software ap-
plications. Experience with Excel models. Some
graduate work preferred but not required (e.g.,
MBA). Some experience with project manage-
ment 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 opera-
tions 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 personal-
ity”—people with expertise in math, statistics,
and data analysis who can also speak the lan-
guage 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, partici-
pating in a discussion of what the key issues
are, determining what information needs the
businesspeople have, and recommending ac-
tions to the business partners. We want this
[analytics group] to be not just a general util-
ity, but rather an active and critical part of the
business unit’s success.
Of course, a combination of analytical, busi-
ness, and relationship skills may be difficult to
find. When the software company SAS (a spon-
sor of this research, along with Intel) knows it
will need an expert in state-of-the-art business
applications such as predictive modeling or re-
cursive partitioning (a form of decision tree
analysis applied to very complex data sets), it
begins recruiting up to 18 months before it ex-
pects 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 Euro-
pean 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 sta-
tistical 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 dis-
tance can become a major barrier.
The right technology.
Competing on ana-
lytics means competing on technology. And
while the most serious competitors investigate
the latest statistical algorithms and decision
science approaches, they also constantly mon-
itor 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 seri-
The most proficient
analytics practitioners
don’t just measure their
own navels—they also
help customers and
vendors measure theirs.
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Competing on Analytics
harvard business review • january 2006 page 9
ous analytics does require the following:
A data strategy.
Companies have invested
many millions of dollars in systems that snatch
data from every conceivable source. Enter-
prise resource planning, customer relation-
ship management, point-of-sale, and other sys-
tems 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 for-
mats, integrate it, store it in a data warehouse,
and make it easily accessible to anyone and ev-
eryone. And they will need
a lot
of it. For ex-
ample, a company may spend several years ac-
cumulating data on different marketing
approaches before it has gathered enough to
reliably analyze the effectiveness of an adver-
tising campaign. Dell employed DDB Matrix,
a unit of the advertising agency DDB World-
wide, 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 ap-
peared (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, ana-
lyze, and disseminate data, all in the interests
of better decision making. Business intelli-
gence tools allow employees to extract, trans-
form, and load (or ETL, as people in the indus-
try would say) data for analysis and then make
those analyses available in reports, alerts, and
scorecards. The popularity of analytics compe-
tition 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 serv-
ers. Many analytics competitors are convert-
ing their hardware to 64-bit processors that
churn large amounts of data quickly.
The Long Road Ahead
Most companies in most industries have excel-
lent reasons to pursue strategies shaped by an-
alytics. Virtually all the organizations we iden-
tified as aggressive analytics competitors are
clear leaders in their fields, and they attribute
much of their success to the masterful exploi-
tation of data. Rising global competition in-
tensifies the need for this sort of proficiency.
Western companies unable to beat their In-
dian or Chinese competitors on product cost,
for example, can seek the upper hand through
optimized business processes.
Companies just now embracing such strate-
gies, 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 ana-
lytics 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, con-
trolling fraud, cross selling, and so on. On the
technical side, it had to integrate data on 10
million Barclaycard customers, improve the
You K no w Yo u Compete on Analytics
When...
1.
You apply sophisticated information systems and rigorous analysis not only to
your core capability but also to a range of functions as varied as marketing and
human resources.
2.
Your senior executive team not only recognizes the importance of analytics capa-
bilities but also makes their development and maintenance a primary focus.
3.
You treat fact-based decision making not only as a best practice but also as a part
of the culture that’s constantly emphasized and communicated by senior executives.
4.
You hire not only people with analytical skills but a lot of people with
the very best
analytical skills—and consider them a key to your success.
5.
You not only employ analytics in almost every function and department but also
consider it so strategically important that you manage it at the enterprise level.
6.
You not only are expert at number crunching but also invent proprietary metrics
for use in key business processes.
7.
You not only use copious data and in-house analysis but also share them with cus-
tomers and suppliers.
8.
You not only avidly consume data but also seize every opportunity to generate in-
formation, creating a “test and learn” culture based on numerous small experiments.
9.
You not only have committed to competing on analytics but also have been build-
ing your capabilities for several years.
10.
You not only emphasize the importance of analytics internally but also make
quantitative capabilities part of your company’s story, to be shared in the annual re-
port and in discussions with financial analysts.
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Competing on Analytics
harvard business review • january 2006 page 10
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 re-
tain 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 ex-
pense—that any company takes to become an
analytics competitor will be devoted to techno-
logical tasks: refining the systems that produce
transaction data, making data available in
warehouses, selecting and implementing ana-
lytic software, and assembling the hardware
and communications environment. And be-
cause 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 ex-
perience to validate conclusions based on the
data, remarked a manager of customer data
analytics at UPS.
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 em-
ployees, meanwhile, will require extensive train-
ing. They need to know what data are available
and all the ways the information can be ana-
lyzed; and they must learn to recognize such pe-
culiarities and shortcomings as missing data, du-
plication, and quality problems. An analytics-
minded executive at Procter & Gamble sug-
gested to me that firms should begin to keep
managers in their jobs for longer periods be-
cause of the time required to master quantita-
tive approaches to their businesses.
The German pathologist Rudolph Virchow
famously called the task of science “to stake
out the limits of the knowable. Analytics com-
petitors pursue a similar goal, although the
universe they seek to know is a more circum-
scribed one of customer behavior, product
movement, employee performance, and finan-
cial reactions. Every day, advances in technol-
ogy 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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Competing on Analytics
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page 11
Further Reading
ARTICLES
Diamonds in the Data Mine
by Gary Loveman
Harvard Business Review
May 2003
Product no. 3647
Gaming giant Harrah’s CEO Loveman de-
scribes how his company uses analytics to win
its clientele’s devotion and supercharge reve-
nues. Harrah’s acquires extensive customer in-
formation through a transactional database
that records each customer’s activity at vari-
ous points of sale, then slices and dices the
data finely to develop strategies for encourag-
ing customers to visit Harrah’s casinos regu-
larly. It identifies core customers by calculat-
ing their lifetime value, and rewards them for
spending more. Thanks to analytics, Harrah’s
scored 16 straight quarters of same-store rev-
enue growth.
The Surprising Economics of a “People
Business”
by Felix Barber and Riner Strack
Harvard Business Review
June 2005
Product no. R0506D
The authors explain how to use analytics to
manage your company’s human resources
more effectively. In “people businesses”—
companies with high employee costs, low
capital investment, and limited spending on
activities intended to generate future reve-
nue—you need to use the right metrics to
assess performance. Avoid relying on capital-
oriented metrics (such as return on assets or
return on equity); they mask weak perfor-
mance or indicate market volatility where it
may not exist. Instead, use financially rigorous,
people-oriented metrics—such as a reformu-
lation of a conventional calculation of eco-
nomic profit—so you’re gauging people’s pro-
ductivity. Reward excellent performance
through variable compensation schemes, and
price products and services in ways that cap-
ture a share of the additional value your peo-
ple create for customers.
Countering the Biggest Risk of All
by Adrian J. Slywotzky and John Drzik
Harvard Business Review
April 2005
Product no. 977X
This article presents ideas for using analytics
to understand and mitigate a particularly
grave strategic risk—sudden shifts in cus-
tomer tastes that redefine your industry. Miti-
gate this risk by gathering and analyzing pro-
prietary information to detect potential shifts.
And conduct fast, cheap experiments to iden-
tify attractive offerings for different customer
microsegments. For example, Coach won-
dered whether its customers would remain
loyal if it offered trendier styles. It conducted
in-store product tests and market experi-
ments to gauge the impact of new pricing,
features, and offers by competitive brands. It
used the information to quickly alter product
designs, drop unappealing items, and create
new lines featuring different fabrics and col-
ors. The upshot? Coach retained its traditional
fans
and
attracted new customers.
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