ArticlePDF Available

The dark side of customer analytics



Health insurer IFA and grocery chain ShopSense have formed an intriguing partnership, but it threatens to test customers' tolerance for sharing personal information. For years, IFA's regional manager for West Coast operations, Laura Brickman, had been championing the use of customer analytics -drawing conclusions about consumer behaviors based on patterns found in collected data. She came away from a meeting with the grocer's analytics chief, Steve Worthington, convinced that ShopSense's customer loyalty card data could be meaningful. In a pilot test, Laura bought ten years' worth of data from the grocer and found some compelling correlations between purchases of unhealthy products and medical claims. Now she has to sell her company's senior team on buying more information. Her bosses have some concerns, however. If IFA came up with proprietary health findings, would the company have to share what it learned? Meanwhile, Steve is busy trying to work out details of the sale with executives at ShopSense. Many have expressed support, but COO Alan Atkins isn't so sure: If customers found out that the store was selling their data, they might stop using their cards, and the company would lose access to vital information. Though CEO Donna Greer agrees, she knows that if things go well, it could mean easy money. How can the two companies use the customer data responsibly? Commenting on this fictional case study are George L. Jones, the CEO of Borders Group; Katherine N. Lemon, an associate professor of marketing at Boston College; David Norton, the senior vice president of relationship marketing for Harrah's Entertainment; and Michael B. McCallister, the president and CEO of Humana.
The Dark Side of
Customer Analytics
by Thomas H. Davenport and Jeanne G. Harris
How can these
companies leverage
the customer data
Four commentators offer
expert advice.
Reprint R0705A
The Dark Side of
Customer Analytics
by Thomas H. Davenport and Jeanne G. Harris
harvard business review • may 2007 page 1
HBR’s cases, which are fictional, present common managerial dilemmas
and offer concrete solutions from experts.
An insurance company finds some intriguing patterns in the loyalty
card data it bought from a grocery chain—the correlation between
condom sales and HIV-related claims, for instance. How can both
companies leverage the data responsibly?
Laura Brickman was glad she was almost done
grocery shopping. The lines at the local
ShopSense supermarket were especially long
for a Tuesday evening. Her cart was nearly
overflowing in preparation for several days
away from her family, and she still had pack-
ing to do at home. Just a few more items to go:
“A dozen eggs, a half gallon of orange juice,
and—a box of Dip & Dunk cereal?” Her six-
year-old daughter, Maryellen, had obviously
used the step stool to get at the list on the
counter and had scrawled her high-fructose
demand at the bottom of the paper in bright-
orange marker.
Laura made a mental note to speak with Miss
Maryellen about what sugary cereals do to kids’
teeth (and to their parents’ wallets). Taking care
not to crack any of the eggs, she squeezed the
remaining items into the cart. She wheeled past
the ShopSense Summer Fun displays. “Do we
need more sunscreen?” Laura wondered for a
moment, before deciding to go without. She got
to the checkout area and waited.
As regional manager for West Coast opera-
tions of IFA, one of the largest sellers of life and
health insurance in the United States, Laura
normally might not have paid much attention
to Shop-Sense’s checkout procedures—except
maybe to monitor how accurately her pur-
chases were being rung up. But now that her
company’s fate was intertwined with that of the
Dallas-based national grocery chain, she had
less motivation to peruse the magazine racks
and more incentive to evaluate the scanning
and tallying going on ahead of her.
Some 14 months earlier, IFA and ShopSense
had joined forces in an intriguing venture.
Laura for years had been interested in the idea
of looking beyond the traditional sources of cus-
tomer data that insurers typically used to set
their premiums and develop their products.
She’d read every article, book, and Web site she
The Dark Side of Customer Analytics
page 2 harvard business review • may 2007
could find on customer analytics, seeking to
learn more about how organizations in other
industries were wringing every last drop of
value from their products and processes. Casi-
nos, credit card companies, even staid old in-
surance firms were joining airlines, hotels, and
other service-oriented businesses in gathering
and analyzing specific details about their cus-
tomers. And, according to recent studies, more
and more of those organizations were sharing
their data with business partners.
Laura had read a profile of ShopSense in a
business publication and learned that it was
one of only a handful of retailers to conduct its
analytics in-house. As a result, the grocery
chain possessed sophisticated data-analysis
methods and a particularly deep trove of infor-
mation about its customers. In the article, ana-
lytics chief Steve Worthington described how
the organization employed a pattern-based ap-
proach to issuing coupons. The marketing de-
partment understood, for instance, that after
three months of purchasing nothing but Way-
Less bars and shakes, a shopper wasn’t suscep-
tible to discounts on a rival brand of diet aids.
Instead, she’d probably respond to an offer of a
free doughnut or pastry with the purchase of a
coffee. The company had even been experi-
menting in a few markets with what it called
Good-Sense messages—bits of useful health in-
formation printed on the backs of receipts,
based partly on customers’ current and previ-
ous buying patterns. Nutritional analyses of
some customers’ most recent purchases were
being printed on receipts in a few of the test
markets as well.
Shortly after reading that article, Laura had
invited Steve to her office in San Francisco.
The two met several times, and, after some fe-
vered discussions with her bosses in Ohio,
Laura made the ShopSense executive an offer.
The insurer wanted to buy a small sample of
the grocer’s customer loyalty card data to de-
termine its quality and reliability; IFA wanted
to find out if the ShopSense information
would be meaningful when stacked up against
its own claims information.
With top management’s blessing, Steve and
his team had agreed to provide IFA with ten
years’ worth of loyalty card data for customers
in southern Michigan, where ShopSense had a
high share of wallet—that is, the supermarkets
weren’t located within five miles of a “club”
store or other major rival. Several months after
receiving the tapes, analysts at IFA ended up
finding some fairly strong correlations be-
tween purchases of unhealthy products (high-
sodium, high-cholesterol foods) and medical
claims. In response, Laura and her actuarial
and sales teams conceived an offering called
Smart Choice, a low-premium insurance plan
aimed at IFA customers who didn’t indulge.
Laura was flying the next day to IFAs head-
quarters in Cincinnati to meet with members
of the senior team. She would be seeking their
approval to buy more of the ShopSense data;
she wanted to continue mining the informa-
tion and refining IFAs pricing and marketing
efforts. Laura understood it might be a tough
sell. After all, her industry wasn’t exactly
known for embracing radical change—even
with proof in hand that change could work.
The make-or-break issue, she thought, would
be the reliability and richness of the data.
“Your CEO needs to hear only one thing,
Steve had told her several days earlier, while
they were comparing notes. “Exclusive rights
to our data will give you information that
your competitors won’t be able to match. No
one else has the historical data we have or as
many customers nationwide. He was right, of
course. Laura also knew that if IFA decided
not to buy the grocer’s data, some other in-
surer would.
“Paper or plastic?” a young boy was asking.
Laura had finally made it to front of the line.
“Oh, paper, please,” she replied. The cashier
scanned in the groceries and waited while
Laura swiped her card and signed the touch
screen. Once the register printer had stopped
chattering, the cashier curled the long strip of
paper into a thick wad and handed it to Laura.
“Have a nice night, she said mechanically.
Before wheeling her cart out of the store
into the slightly cool evening, Laura briefly
checked the total on the receipt and the infor-
mation on the back: coupons for sunblock and
a reminder about the importance of UVA and
UVB protection.
Tell It to Your Analyst
“No data set is perfect, but based on what
we’ve seen already, the ShopSense info could
be a pretty rich source of insight for us, Archie
Stetter told the handful of executives seated
around a table in one of IFA’s recently reno-
vated conference rooms. Laura nodded in
agreement, silently cheering on the insurance
Thomas H. Davenport
(tdavenport@ is the President’s Distin-
guished Professor of Information Tech-
nology and Management at Babson
College, in Wellesley, Massachusetts,
and the director of research for Babson
Executive Education.
Jeanne G. Harris
( is an
executive research fellow and a direc-
tor of research at the Accenture Insti-
tute for High-Performance Business.
She is based in Chicago. Davenport
and Harris are the coauthors of
peting on Analytics
(Harvard Business
School Press, 2007).
The Dark Side of Customer Analytics
harvard business review • may 2007 page 3
company’s uberanalyst. Archie had been in-
valuable in guiding the pilot project. Laura
had flown in two days ahead of the meeting
and had sat down with the chatty statistics ex-
pert and some members of his team, going
over results and gauging their support for con-
tinuing the relationship with ShopSense.
Trans fats and heart disease—no surprise
there, I guess,” Archie said, using a laser
pointer to direct the managers’ attention to a
PowerPoint slide projected on the wall. “How
about this, though: Households that purchase
both bananas and cashews at least quarterly
seem to show only a negligible risk of devel-
oping Parkinson’s and MS.” Archie had at first
been skeptical about the quality of the gro-
cery chain’s data, but ShopSense’s well of in-
formation was deeper than he’d imagined.
Frankly, he’d been having a blast slicing and
dicing. Enjoying his moment in the spotlight,
Archie went on a bit longer than he’d in-
tended, talking about typical patterns in the
purchase of certain over-the- counter medica-
tions, potential leading indicators for diabe-
tes, and other statistical curiosities. Laura
noted that as Archie’s presentation wore on,
CEO Jason Walter was jotting down notes.
O.Z. Cooper, IFA’s general counsel, began to
clear his throat over the speakerphone.
Laura was about to rein in her stats guy
when Rusty Ware, IFA’s chief actuary, ad-
dressed the group. “You know, this deal isn’t re-
ally as much of a stretch as you might think.
He pointed out that the company had for years
been buying from information brokers lists of
customers who purchased specific drugs and
products. And IFA was among the best in the
industry at evaluating external sources of data
(credit histories, demographic studies, analyses
of socioeconomic status, and so on) to predict
depression, back pain, and other expensive
chronic conditions. Prospective IFA customers
were required to disclose existing medical con-
ditions and information about their personal
habits—drinking, smoking, and other high-risk
activities—the actuary reminded the group.
The CEO, meanwhile, felt that Rusty was
overlooking an important point. “But if we’re
finding patterns where our rivals aren’t even
looking, if we’re coming up with propri-
etary health indicators—well, that would be a
huge hurdle for everyone else to get over,
Jason noted.
Laura was keeping an eye on the clock; there
were several themes she still wanted to ham-
mer on. Before she could follow up on Jason’s
comments, though, Geneva Hendrickson, IFA’s
senior vice president for ethics and corporate
responsibility, posed a blue-sky question to the
group: Take the fruit-and-nut stat Archie cited.
Wouldn’t we have to share that kind of infor-
mation? As a benefit to society?”
Several managers at the table began talking
over one another in an attempt to respond.
“Correlations, no matter how interesting,
aren’t conclusive evidence of causality,” some-
one said. “Even if a correlation doesn’t hold up
in the medical community, that doesn’t mean
it’s not useful to us, someone else suggested.
Laura saw her opening; she wanted to get
back to Jason’s point about competitive advan-
tage. “Look at Progressive Insurance, she be-
gan. It was able to steal a march on its rivals
simply by recognizing that not all motorcycle
owners are created equal. Some ride hard
(young bikers), and some hardly ride (older,
middle-class, midlife crisis riders). “By putting
these guys into different risk pools, Progressive
has gotten the rates right, she said. “It wins all
the business with the safe set by offering low
premiums, and it doesn’t lose its shirt on the
more dangerous set.
Then O.Z. Cooper broke in over the speaker-
phone. Maybe the company should formally
position Smart Choice and other products and
marketing programs developed using the
Shop-Sense data as opt in, he wondered. A lot
of people signed up when Progressive gave dis-
counts to customers who agreed to put devices
in their cars that would monitor their driving
habits. “Of course, those customers realized
later they might pay a higher premium when
the company found out they routinely ex-
ceeded the speed limit—but that’s not a legal
problem,” O.Z. noted. None of the states that
IFA did business in had laws prohibiting the
sort of data exchange ShopSense and the in-
surer were proposing. It would be a different
story, however, if the company wanted to do
more business overseas.
At that point, Archie begged to show the
group one more slide: sales of prophylactics
versus HIV-related claims. The executives con-
tinued taking notes. Laura glanced again at the
clock. No one seemed to care that they were
going a little over.
“Exclusive rights to our
data will give you
information that your
competitors won’t be able
to match. No one else has
the historical data we
The Dark Side of Customer Analytics
page 4 harvard business review • may 2007
Data Decorum
Rain was in the forecast that afternoon for
Dallas, so Steve Worthington decided to drive
rather than ride his bike the nine and a half
miles from his home to ShopSense’s corporate
offices in the Hightower Complex. Of course,
the gridlock made him a few minutes late for
the early morning meeting with ShopSense’s
executive team. Lucky for him, others had
been held up by the traffic as well.
The group gradually came together in a
slightly cluttered room off the main hallway
on the 18th floor. One corner of the space was
being used to store prototypes of regional in-
store displays featuring several members of the
Houston Astros’ pitching staff. “I don’t know
whether to grab a cup of coffee or a bat,” Steve
joked to the others, gesturing at the life-size
cardboard cutouts and settling into his seat.
Steve was hoping to persuade CEO Donna
Greer and other members of the senior team
to approve the terms of the data sale to IFA.
He was pretty confident he had majority sup -
port; he had already spoken individually with
many of the top executives. In those one-on-
one conversations, only Alan Atkins, the gro-
cery chain’s chief operations officer, had raised
any significant issues, and Steve had dealt pa-
tiently with each of them. Or so he thought.
At the start of the meeting, Alan admitted
he still had some concerns about selling data to
IFA at all. Mainly, he was worried that all the
hard work the organization had done building
up its loyalty program, honing its analytical
chops, and maintaining deep customer rela-
tionships could be undone in one fell swoop.
“Customers find out, they stop using their
cards, and we stop getting the information that
drives this whole train, he said.
Steve reminded Alan that IFA had no inter-
est in revealing its relationship with the grocer
to customers. There was always the chance an
employee would let something slip, but even if
that happened, Steve doubted anyone would
be shocked. “I haven’t heard of anybody can-
celing based on any of our other card-driven
marketing programs, he said.
That’s because what we’re doing isn’t visi-
ble to our customers—or at least it wasn’t until
your recent comments in the press,” Alan
grumbled. There had been some tension
within the group about Steve’s contribution to
several widely disseminated articles about
ShopSense’s embrace of customer analytics.
“Point taken, Steve replied, although he
knew that Alan was aware of how much posi-
tive attention those articles had garnered for
the company. Many of its card-driven market-
ing programs had since been deemed cutting-
edge by others in and outside the industry.
Steve had hoped to move on to the finan-
cial benefits of the arrangement, but Denise
Baldwin, ShopSense’s head of human re-
sources, still seemed concerned about how
IFA would use the data. Specifically, she won-
dered, would it identify individual consumers
as employees of particular companies? She re-
minded the group that some big insurers had
gotten into serious trouble because of their
profiling practices.
IFA had been looking at this relationship
only in the context of individual insurance cus-
tomers, Steve explained, not of group plans.
“Besides, it’s not like we’d be directly drawing
the risk pools,” he said. Then Steve began dis-
tributing copies of the spreadsheets outlining
the five-year returns ShopSense could realize
from the deal.
“‘Directly’ being the operative word here,
Denise noted wryly, as she took her copy and
passed the rest around.
Parsing the Information
It was 6:50 pm, and Jason Walters had can-
celed his session with his personal trainer—
again—to stay late at the office. Sammy will
understand, the CEO told himself as he sank
deeper into the love seat in his office, a yellow
legal pad on his lap and a pen and cup of
espresso balanced on the arm of the couch. It
was several days after the review of the
ShopSense pilot, and Jason was still weighing
the risks and benefits of taking this business
relationship to the next stage.
He hated to admit how giddy he was—
almost as gleeful as Archie Stetter had been—
about the number of meaningful correlations
the analysts had turned up. “Imagine what that
guy could do with an even larger data set, O.Z.
Cooper had commented to Jason after the
meeting. Exclusive access to ShopSense’s data
would give IFA a leg up on competitors, Jason
knew. It could also provide the insurer with
proprietary insights into the food-related driv-
ers of disease. The deal was certainly legal.
And even in the court of public opinion, peo-
ple understood that insurers had to perform
risk analyses. It wasn’t the same as when that
“Customers find out,
they stop using their
cards, and we stop
etting the information
that drives this whole
The Dark Side of Customer Analytics
harvard business review • may 2007 page 5
online bookseller got into trouble for charging
customers differently based on their shop-
ping histories.
But Jason also saw dark clouds on the hori-
zon: What if IFA took the pilot to the next level
and found out something that maybe it was
better off not knowing? As he watched the
minute hand sweep on his wall clock, Jason
wondered what risks he might be taking with-
out even realizing it.
• • •
Donna Greer gently swirled the wine in her
glass and clinked the stemware against her
husband’s. The two were attending a wine
tasting hosted by a friend. The focus was on
varieties from Chile and other Latin American
countries, and Donna and Peter had yet to find
a sample they didn’t like. But despite the lively
patter of the event and the plentiful food.
Donna couldn’t keep her mind off the IFA
deal. “The big question is, Should we be charg-
ing more?” she mused to her husband.
ShopSense was already selling its scanner data
to syndicators, and, as her CFO had reminded
her, the company currently made more money
from selling information than from selling
meat. Going forward, all ShopSense would
have to do was send IFA some tapes each
month and collect a million dollars annually
of pure profit. Still, the deal wasn’t without
risks: By selling the information to IFA, it
might end up diluting or destroying valuable
and hard-won customer relationships. Donna
could see the headline now: “Big Brother in
Aisle Four.” All the more reason to make it
worth our while, she thought to herself.
Peter urged Donna to drop the issue for a
bit, as he scribbled his comments about the
wine they’d just sampled on a rating sheet.
“But I’ll go on record as being against the
whole thing, he said. “Some poor soul puts po-
tato chips in the cart instead of celery, and look
what happens.
“But what about the poor soul who buys the
celery and still has to pay a fortune for medical
coverage,” Donna argued, “because the premi-
ums are set based on the people who can’t eat
just one?”
“Isn’t that the whole point of insurance?”
Peter teased. The CEO shot her husband a
playfully peeved look—and reminded herself
to send an e-mail to Steve when they got home.
How can these companies leverage the
customer data responsibly?
• Four
Case Commentary
commentators offer expert advice.
What if IFA took the
ilot to the next level and
ound out something that
maybe it was better off
not knowing?
page 6 harvard business review • may 2007
The Dark Side of Customer Analytics •
Case Commentary
by George L. Jones
How can these companies leverage the customer data
Sure, a customer database has value, and a
company can maximize that value in any
number of ways—growing the database, min-
ing it, monetizing it. Marketers can be tempted,
despite pledges about privacy, to use collected
information in ways that seem attractive but
may ultimately damage relationships with
The arrangement proposed in this case
study seems shortsighted to me. Neither com-
pany seems to particularly care about its cus-
tomers. Instead, the message coming from the
senior teams at both IFA and ShopSense is
that any marketing opportunity is valid—as
long as they can get away with it legally and
customers don’t figure out what they’re doing.
In my company, this pilot would never have
gotten off the ground. The culture at Borders
is such that the managers involved would have
just assumed we wouldn’t do something like
that. Like most successful retail companies, our
organization is customer focused; we’re always
trying to see a store or an offer or a transaction
through the customer’s eyes. It was the same
way at both Saks and Target when I was with
those companies.
At Borders, we’ve built up a significant data-
base through our Borders Rewards program,
which in the past year and a half has grown to
17 million members. The data we’re getting are
hugely important as a basis for serving custom-
ers more effectively (based on their purchase
patterns) and as a source of competitive advan-
tage. For instance, we know that if somebody
buys a travel guide to France, that person might
also be interested in reading Peter Mayle’s
Year in Provence
. But we assure our customers
up front that their information will be handled
with the utmost respect. We carefully control
the content and frequency of even our own
communications with Rewards members. We
don’t want any offers we present to have nega-
tive connotations—for instance, we avoid bom-
barding people with e-mails about a product
they may have absolutely no interest in.
I honestly don’t think these companies have
hit upon a responsible formula for mining and
sharing customer data. If ShopSense retained
control of its data to some degree—that is, if
the grocer and IFA marketed the Smart Choice
program jointly, and if any offers came from
ShopSense (the partner the customer has built
up trust with) rather than the insurance com-
pany (a stranger, so to speak)—the relation-
ship could work. Instead of ceding complete
control to IFA, ShopSense could be somewhat
selective and send offers to all, some, or none
of its loyalty card members, depending on how
relevant the grocer believed the insurance
offer would be to a particular set of customers.
A big hole in these data, though, is that peo-
ple buy food for others besides themselves. I
rarely eat at home, but I still buy tons of
groceries—some healthy, some not so healthy—
for my kids and their friends. If you looked at a
breakdown of purchases for my household,
you’d say “Wow, they’re consuming a lot.” But
the truth is, I hardly ever eat a bite. That may
be an extreme example, but it suggests that
IFA’s correlations may be flawed.
Both CEOs are subjecting their organiza-
tions to a possible public relations backlash,
and not just from the ShopSense customers
whose data have been dealt away to IFA. Every
ShopSense customer who hears about the
deal, loyalty card member or not, is going to
lose trust in the company. IFA’s customers
might also think twice about their relationship
with the insurer. And what about the employ-
ees in each company who may be uncomfort-
able with what the companies are trying to
pull off? The corporate cultures suffer.
What the companies are proposing here is
very dangerous—especially in the world of re-
tail, where loyalty is so hard to win. Customers’
information needs to be protected.
George L. Jones
is the president and chief executive
officer of Borders Group, a global retailer of books, mu-
sic, and movies based in Ann Arbor, Michigan.
The message coming
rom both IFA and
ShopSense is that any
marketing opportunity is
valid—as long as they
can get away with it.
harvard business review • may 2007 page 7
The Dark Side of Customer Analytics •
Case Commentary
by Katherine N. Lemon
How can these companies leverage the customer data
As the case study illustrates, companies will
soon be able to create fairly exhaustive, highly
accurate profiles of customers without having
had any direct interaction with them. They’ll
be able to get to know you intimately without
your knowledge.
From the consumer’s perspective, this trend
raises several big concerns. In this fictional ac-
count, for instance, a shopper’s grocery pur-
chases may directly influence the availability
or price of her life or health insurance
products—and not necessarily in a good way.
Although the customer, at least tacitly, con-
sented to the collection, use, and transfer of
her purchase data, the real issue here is the
use of the in-
formation (from the customer’s point of
view). Most customers would probably be
quite surprised to learn that their personal
information could be used by companies in a
wholly unrelated industry and in other ways
that aren’t readily foreseeable.
If consumers lose trust in firms that collect,
analyze, and utilize their information, they
will opt out of loyalty and other data-driven
marketing programs, and we may see more
regulations and limitations on data collection.
Customer analytics are effective precisely
because firms do
violate customer trust.
People believe that retail and other organiza-
tions will use their data wisely to enhance
their experiences, not to harm them. Angry
customers will certainly speak with their wal-
lets if that trust is violated.
Decisions that might be made on the basis
of the shared data represent another hazard
for consumers—and for organizations. Take
the insurance company’s use of the grocer’s
loyalty card data. This is limited information at
best and inaccurate at worst. The ShopSense
data reflect food bought but not necessarily
consumed, and individuals buy food at many
stores, not just one. IFA might end up drawing
erroneous conclusions—and exacting unfair
rate increases. The insurer’s general counsel
should investigate this deal.
Another concern for consumers is what I call
“battered customer syndrome.” Market analyt-
ics allow companies to identify their best and
worst customers and, consequently, to pay spe-
cial attention to those deemed to be the most
valuable. Looked at another way, analytics en-
able firms to understand how poorly they can
treat individual or groups of customers before
those people stop doing business with them.
Unless you are in the top echelon of customers—
those with the highest lifetime value, say—you
may pay higher prices, get fewer special offers,
or receive less service than other consumers.
Despite the fact that alienating 75% to 90% of
customers may not be the best idea in the long
run, many retailers have adopted this “top tier”
approach to managing customer relationships.
And many customers seem to be willing to live
with it—perhaps with the unrealistic hope that
they may reach the upper echelon and reap
the ensuing benefits.
Little research has been done on the nega-
tive consequences of using marketing ap-
proaches that discriminate against customer
segments. Inevitably, however, customers will
become savvier about analytics. They may be-
come less tolerant and take their business (and
information) elsewhere.
If access to and use of customer data are to
remain viable, organizations must come up
with ways to address customers’ concerns
about privacy. What, then, should IFA and
ShopSense do? First and foremost, they need
to let customers opt in to their data-sharing ar-
rangement. This would address the “unin-
tended use of data” problem; customers would
understand exactly what was being done with
their information. Even better, both firms
would be engaging in trust-building—versus
trust-eroding—activities with customers. The
result: improvement in the bottom line and in
the customer experience.
Katherine N. Lemon
( is an associ-
ate professor of marketing at Boston College’s Carroll
School of Management. Her expertise is in the areas
of customer equity, customer management, and
customer-based marketing strategy.
Customer analytics are
effective precisely
because firms do not
violate customer trust.
page 8 harvard business review • may 2007
The Dark Side of Customer Analytics •
Case Commentary
by David Norton
How can these companies leverage the customer data
Transparency is a critical component of any
loyalty card program. The value proposition
must be clear; customers must know what
they’ll get for allowing their purchase behav-
ior to be monitored. So the question for the
CEOs of ShopSense and IFA is, Would custom-
ers feel comfortable with the data-sharing ar-
rangement if they knew about it?
ShopSense’s loyalty card data are at the cen-
ter of this venture, but the grocer’s goal here is
not to increase customer loyalty. The value of
its relationship with IFA is solely financial. The
company should explore whether there are
some customer data it should exclude from the
transfer—information that could be perceived
as exceedingly sensitive, such as pharmacy and
alcohol purchases. It should also consider
doing market research and risk modeling to
evaluate customers’ potential reaction to the
data sharing and the possible downstream ef-
fect of the deal.
The risk of consumer backlash is lower for
IFA than for ShopSense, given the information
the insurance company already purchases.
IFA could even put a positive spin on the
creation of new insurance products based on
the ShopSense data. For instance, so-called
healthy purchases might earn customers a dis-
count on their standard insurance policies. The
challenge for the insurer, however, is that
there is no proven correlation between the
purchase of certain foods and fewer health
problems. IFA should continue experimenting
with the data to determine their richness and
predictive value.
Some companies have more leeway than
others to sell or trade customer lists. At Har-
rah’s, we have less than most because our cus-
tomers may not want others to know about
their gaming and leisure activities. We don’t
sell information, and we don’t buy a lot of ex-
ternal data. Occasionally, we’ll buy demo-
graphic data to fine-tune our marketing mes-
sages (to some customers, an offer of tickets to
a live performance might be more interesting
than a dining discount, for example). But we
think the internal transactional data are much
more important.
We do rely on analytics and models to help
us understand existing customers and to en-
courage them to stick with us. About ten years
ago, we created our Total Rewards program.
Guests at our hotels and casinos register for a
loyalty card by sharing the information on
their driver’s license, such as their name, ad-
dress, and date of birth. Each time they visit
one of our 39 properties and use their card,
they earn credits that can be used for food and
merchandise. They also earn Tier Credits that
give them higher status in the program and
make them eligible for differentiated service.
With every visit, we get a read on our cus-
tomers’ preferences—the types of games they
play, the hotels and amenities they favor, and
so on. Those details are stored in a central da-
tabase. The company sets rules for what can be
done with the information. For instance, man-
agers at any one of our properties can execute
their own marketing lists and programs, but
they can target only customers who have vis-
ited their properties. If they want to dip into
the overall customer base, they have to go
through the central relationship-marketing
group. Some of the information captured in
our online joint promotions is accessible to both
Harrah’s and its business partners, but the pro-
motions are clearly positioned as opt in.
We tell customers the value proposition up
front: Let us track your play at our properties,
and we can help you enjoy the experience bet-
ter with richer rewards and improved service.
They understand exactly what we’re capturing,
the rewards they’ll get, and what the company
will do with the information. It’s a win-win for
the company and for the customer.
Companies engaging in customer analyt-
ics and related marketing initiatives need to
keep “win-win” in mind when collecting and
handling customer data. It’s not just about
what the information can do for you; it’s
about what you can do for the customer
with the information.
David Norton
( is the senior
vice president of relationship marketing at Harrah’s
Entertainment, based in Las Vegas.
Would customers feel
comfortable with the
arrangement if they
knew about it?
harvard business review • may 2007 page 9
The Dark Side of Customer Analytics •
Case Commentary
by Michael B. McCallister
How can these companies leverage the customer data
Companies that can capitalize on the informa-
tion they get from their customers hold an ad-
vantage over rivals. But as the firms in the case
study are realizing, there are also plenty of
risks involved with using these data. Instead
of pulling back the reins, organizations
should be nudging customer analytics for-
ward, keeping in mind one critical point: Any
collection, analysis, and sharing of data must
be conducted in a protected, permission-based
Humana provides health benefit plans and
related health services to more than 11 million
members nationwide. We use proprietary data-
mining and analytical capabilities to help
guide consumers through the health maze.
Like IFA, we ask our customers to share their
personal and medical histories with us (the
risky behaviors as well as the good habits) so
we can acquaint them with programs and pre-
ventive services geared to their health status.
Customer data come to us in many different
ways. For instance, we offer complimentary
health assessments in which plan members
can take an interactive online survey designed
to measure how well they’re taking care of
themselves. We then suggest ways they can re-
duce their health risks or treat their existing
conditions more effectively. We closely moni-
tor our claims information and use it to reach
out to people. In our Personal Nurse program,
for example, we’ll have a registered nurse fol-
low up with a member who has filed, say, a
diabetes-related claim. Through phone conver-
sations and e-mails, the RN can help the plan
member institute changes to improve his or
her quality of life. All our programs require
members to opt in if the data are going to be
used in any way that would single a person
out. Regardless of your industry, you have to
start with that.
One of the biggest problems in U.S. health
care today is obesity. So would it be useful for
our company to look at grocery-purchasing
patterns, as the insurance company in the case
study does? It might be. I could see the upside
of using a grocer’s loyalty card data to develop
a wellness-based incentive program for insur-
ance customers. (We would try to find a way to
build positives into it, however, so customers
would look at the interchange and say “That’s
in my best interest; thank you.”) But Humana
certainly wouldn’t enter into any kind of data-
transfer arrangement without ensuring that
our customers’ personal information and the
integrity of our relationship with them would
be properly protected. In health care, espe-
cially, this has to be the chief concern—above
and beyond any patterns that might be re-
vealed and the sort of competitive edge they
might provide. We use a range of industry stan-
dard security measures, including encryption
and firewalls, to protect our members’ privacy
and medical information.
Ethical behavior starts with the CEO, but it
clearly can’t be managed by just one person.
It’s important that everyone be reminded
often about the principles and values that
guide the organization. When business oppor-
tunities come along, they’ll be screened ac-
cording to those standards—and the decisions
will land right side up every time. I can’t tell
people how to run their meetings or who
should be at the table when the tougher, gray-
area decisions need to be made, but whoever
is there has to have those core principles and
values in mind.
The CEOs in the case study need to take the
“front page” test: If the headline on the front
page of the newspaper were reporting abuse of
customer data (yours included), how would
you react? If you wouldn’t want your personal
data used in a certain way, chances are your
customers wouldn’t, either.
Michael B. McCallister
is the president and CEO of Humana, a health benefits
company based in Louisville, Kentucky.
Reprint R0705A
Case only R0705X
Commentary only R0705Z
To order, call 800-988-0886
or 617-783-7500 or go to
When the tougher, gray-
area decisions need to be
made, each person has to
have the companys core
rinciples and values in
U.S. and Canada
617-783-7555 fax
To Order
Harvard Business Review
reprints and
subscriptions, call 800-988-0886 or
617-783-7500. Go to
For customized and quantity orders of
Harvard Business Review
article reprints,
call 617-783-7626, or e-mail
... As such, to be embedded smoothly in organisations and societies overall, re-skilling, training and several organisational and cultural changes are required (Iansiti & Lakhani, 2020;Tschang & Mezquita, 2021). Such disruptions coupled with a growing awareness of the 'dark side' of AI&A related to for example, inequalities, social exclusion, and fear that AI will take over the world (Davenport & Harris, 2007) call for reflections on the ethical application and governance (Mäntymäki et al., 2022a(Mäntymäki et al., , 2022b of AI&A so that actionable insights can be drawn for practitioners. ...
Artificial Intelligence and Analytics (AI&A) are used in various application areas ranging from online entertainment to healthcare. The goal of this Special Issue is to focus on how AI&A are used in practice to assist organisations to create economic value, support decision making, transforming them, and enhance employees’ skills such as communication, innovation, and decision-making. To derive actionable insights on the application of AI&A in practice, the five articles selected for this Special Issue feature both rigorous academic research and reflections and actionable lessons for professionals. This editorial contains a brief overview of the articles included in this special issue.
... Business analytics refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain better insight into their operations, and make better decisions, based on facts (Davenport & Harris, 2007). In the context of information systems, business analytics capability is defined as the business capabilities that support IT (El Sawy & Pavlou, 2012). ...
Full-text available
This study aims to analyze the important role of business analytics capability, information quality, and innovation capability in influencing organization agility and organization performance during the Covid-19 pandemic. Data was collected from 76 companies from various sectors in Indonesia. Structural Equation Model-Partial Least Square (SEM-PLS) analysis was conducted to analyze the relationship between variables and test a series of hypotheses. Importance-Performance Matrix Analysis (IPMA), a useful analysis approach in PLS-SEM, is used, which extends the results of the estimated path coefficient (importance) by adding a dimension that considers the average values of the latent variable scores (performance). The IPMA approach examines not only the performance of an item but also the importance of that item. The results show that business analytics capability has a significant effect on information quality and innovation capability which then affects organization agility. Organizational performance is influenced by organizational agility. IPMA results show that organizational agility has the highest level of impact on organizational performance. This study will assist companies in planning business analytics, improving information quality, increasing innovation capability, and ultimately increasing agility and performance during the Covid-19 pandemic. This study will add to existing knowledge about previous literature, especially in the Covid-19 pandemic situation.
... Analytics has been put into many uses not limited to fraud detection (Gee, 2014); optimizing marketing campaigns; sports results prediction (Gerrard, 2014); political opinion and outcomes forecasting (Tumasjan et al., 2010;Boon, 2012;Lundberg and Payne, 2014); targeting and product recommendations; stock market behaviour forecasting (Ferson et al., 2003); customer demand prediction (Scholz-Reiter et al., 2014); and anticipating operational maintenance schedules. Despite having all these beneficial use cases, there has been a global surge of unethical analytical activities in business practise (Bonetto, 2015;Harris and Davenport, 2007). ...
Full-text available
The proliferation of data from social media platforms and a myriad of ethical issues combined have pushed the application of legislation beyond its limits. Legislation has not been able to codify ethical issues like privacy, transparency, data ownership and control or error from algorithm bias into laws. This failure compelled some IT professionals to turn into white collar criminals, misusing the enormous amount of power associated with big data. Some of this relates to the unconventional use of predictive analytics to help unscrupulous politicians in their quest for political power, building algorithms to sway voters. Rather than user-centric, practices have tended to emphasize organisational-centric ethical frameworks and regulations. Personal and public ethical perspectives have to a larger extent been ignored as insignificant. This dissertation explores and understands the dynamics of personal ethics associated with social media users. It deploys the Ethical Position Questionnaire and Sentiment Analysis to measure the ethical positions and opinions of social media users with respect to the use of predictive analytics on social media platforms. Results show that, longer spans of time spent on social media platforms per day is associated with low idealism while longer periods of owning a social media platform account is associated with high relativism. Rather than negative, social media users expressed more positive sentiments towards ethical debates such as those recently sparked by the Facebook-Cambridge Analytica scandal hence could be more relativistic. This dissertation emphasises that the possibility of harvesting consumer data and related analytics activities should be driven by user-centric ethical frameworks.
Big data has brought unprecedented opportunities and challenges, prompting global firms to grow big data analytics (BDA) investments, especially in a turbulent business environment. However, there is insufficient empirical evidence in scholarly research on whether and how using BDA functions of various types creates business value. The current study divides BDA into inside-out and outside-in types and explores whether and how firms can create value by using functions of these two types of BDA. Then, the knowledge-based view (KBV) is applied as a theoretical foundation to investigate the independent and combined impacts of inside-out and outside-in BDA usage on firms’ sales performance. Furthermore, we build a quantile regression model to analyze the heterogeneity of independent and combined impacts among firms with different performance levels. The empirical study is based on a unique dataset collected on one of the largest electronic platforms (e-platforms) in China from 785 firms in 35 weeks. The results of the benchmark model based on two-way fixed effects show that both inside-out and outside-in BDA usage, as well as their interactions, are positively related to the sales performance of firms on e-platforms. The heterogeneity analysis indicates that inside-out (outside-in) BDA functions have a greater degree of impact on firms with lower (higher) sales performance. Through the theoretical and empirical analysis of the complex performance impacts of BDA usage, this study enriches the understanding of value creation in using multiple BDA functions and extends the theoretical account of KBV in the field of BDA.
Customer analytics plays a vital role in generating insights from big data to improve service innovation, product development, personalization, and managerial decision-making; yet, no academic study has investigated customer analytics capability through which it is possible to achieve sustainable business growth. To close this gap, this chapter explores the constructs of the customer analytics capability by drawing on a systematic review of the literature in the big data spectrum. The chapter's interpretive framework portrays a definitional aspect of customer analytics, the importance of customer analytics, and customer analytics capability constructs. The study proposes a customer analytics capability model, which consists of four principal constructs and some important sub-constructs. The chapter briefly discusses the challenges and future research direction for developing the customer analytics capability model in the data rich competitive business environment.
As a result of data being collected daily at high speeds, it becomes extremely important for organizations to analyze it in real-time to be a decisive factor in successful decision-making. Therefore, real-time decision based on real-time generated data is seen by organizations as a decisive factor for successful decision-making. However, organizations have difficulties making a real-time analysis of this data due to its complexity, quantity, and diversity. Studying the problems inherent in processing these large amounts of data is insufficient. The way this data is visualized and presented to the end-user is crucial. Thus, this paper focuses on exploring Augmented Reality capabilities to optimize the traditional data visualization methods to support real-time decision-making. This technology offers a wide range of data visualization possibilities that can become quite attractive to the human being. A series of results inherent to this paper can be identified, namely analyzing the challenges and limitations of implementing augmented virtual reality systems for data visualization, the development of 1 augmented reality data visualization module in a real-world data science platform, based on a cross-analysis of 4 dashboards, the analysis of its impact relative to a traditional two-dimensional data visualization module through a questionnaire that serves as the first proof of concept.
Full-text available
The main purpose of this study is to examine the impact of the big data management capabilities on the performance of manufacturing firms in the Asian Economy during coronavirus disease 2019 (COVID-19). In addition to this, this study is also planned to examine the mediating role of organizational agility in the relationship between the big data management capabilities and the performance of Chinese manufacturing firms during COVID-19. Last, this study has examined the moderating role of information technology capability in the relationship between the big data management capabilities and performance of Chinese manufacturing firms during COVID-19. This study adopted the quantitative method of research with a cross-sectional technique. This study employed a questionnaire to gather the data as a research instrument. This study has used the purposive sampling method by keeping in mind the context of this study. Employees of the Chinese SMEs that were at least 10 years old were the population of this study. The research model was being analyzed by employing the “partial least squares” technique through statistical software the Smart PLS version 3. The results are in line with the proposed hypothesis. This study contributed to the literature by suggesting characteristics that promote or prevent the organization from successfully implementing big data and pointed out that showing resistance in information management system implementation may have different effects on the organization. Besides, the study also discussed the relationship between such information systems and the organization. Findings of these two factors provide insights for the practitioners and researchers in assessing the success or failure of organizations for using big data.
E-mail has become the most popular communication tool in the professional environment. Electronic communications, because of their specific nature, raise a number of ethical issues: e-mail communications are distance, asynchronous, text-based, and interactive computer-mediated communications and allow for storage, retrieval, broadcast and manipulation of messages. These specificities give rise to misunderstanding, misconduct in the absence of the interlocutors, information and mail overload, as well as privacy infringement and misuse of shared computing resources. Inexperience explains some users’ unethical behavior. Other forms of unethical behavior find their roots in corporate culture, internal competition and management styles. E-businesses, as early adopters of information and communication technologies, are being particularly exposed to such behaviors, since they rely heavily on electronic communications. They should therefore assess their internal situation and develop and enforce e-mail policies accordingly.
Customer analytics is one of the most dominant strategic weapons in today's competitive retail environment. In spite of its strategic importance, there is scant attention to investigating customer analytics capabilities in the retail context. Drawing on a systematic literature review and thematic analysis, this study proposes a multidimensional customer analytics capability model by identifying relevant dimensions and sub-dimensions in retail settings. The principal contribution of this study is that the model links a customer analytics perspective to a resource-based view (RBV)-capability of the retailers by proposing six customer analytics capability dimensions and twelve sub-dimensions in the spectrum of market orientation and technology orientation. The customer analytics capability dimensions depict three crucial themes of marketing, such as value creation (offering capability and personalization capability), value delivery (distribution capability and communication capability), and value management (data management capability and data protection capability). By incorporating this capability dimensions, practitioners will likely be able to engage customers and enhance customer equity.
ResearchGate has not been able to resolve any references for this publication.