Some researchers are seeking to answer
such questions through field experiments. Mariana Carrera of Case Western
Reserve University and Sofia Villas-Boas at the University of California,
Berkeley wondered why branded over-the-counter drugs still command up to 60% of
market share. They got permission from a national supermarket chain to change
the shelf tags for several branded and generic drugs at six locations. They
posted new labels beneath the items’ price tags, some pointing out that the
branded and generic drugs contain the same active ingredient or are
therapeutically equivalent. Other labels showed the percentage saved for buying
the generic drug. The third set of tags indicated how many other customers
purchased the generic.
From the four-week trial, the researchers
conclude that customers are already aware that generic substitutes exist and
save them money, so posting that information didn’t change their buying habits.
However, learning that other customers bought the generic drugs did shift
purchase decisions—when the percentage of shoppers buying the generic exceeded
50%.
The Nielsen data provide another way to
approach the same question. Gentzkow; Dubé;
Jesse M. Shapiro, professor of
economics at Chicago Booth; and Bart J. Bronnenberg of Tilburg University in the
Netherlands compare different kinds of shoppers, whom they classify as either
sophisticated or typical consumers. In addition to looking at how pharmacists
buy headache remedies, they look at how chefs buy pantry staples, hypothesizing
that sophisticated shoppers may find it easier to cut through the informational
clutter created by commercials and branding to focus on a product’s most
important attributes.
Using the Nielsen data, the researchers
are able to compare purchases of branded and private-label products at not just
six stores but thousands of locations. They construct an experiment, in a sense,
after purchases have been made, using a set of criteria to classify those who
are sophisticated, well-educated consumers versus those who are not. Then they
use the data to compare the buying habits of those groups of people. The data
allows them to compare shopping patterns at the same chain in the same week in
the same city, just as Carrera and Villas-Boas did in their field experiment,
but with far more stores.
The researchers also survey consumers and,
through Nielsen, pose their own questions to tens of thousands of households.
(This is not part of the Kilts-Nielsen datasets, but a separate initiative
arranged by the researchers through Nielsen.) They ask consumers to name the
active ingredient—such as aspirin, acetaminophen, or ibuprofen—in a list of
branded headache medicines. Overall, college-educated respondents name the right
ingredient 61% of the time. The survey respondents’ career choices matter far
more than their socioeconomic status in determining whether they can be
considered typical shoppers or sophisticated ones. For example lawyers, who have
had a lot of schooling, don’t necessarily know much about the ingredients in
headache medicine. But for those who majored in science or health, the correct
response rate rises to 73%. The figures continue to climb for registered nurses,
at 85%; for pharmacists, 89%; and for physicians, 90%.
Armed with this survey information, the
Booth-led research team looks at how doctors and pharmacists shop for headache
remedies, a product category in which a branded drug can cost three times as
much as the generic offering. As the respondents’ knowledge level increases, so
does their willingness to buy generic headache medicines. Nearly 90% of
pharmacists’ purchases of headache remedies are private-label products, compared
with 71% for the total population.
A similar pattern emerges among chefs
shopping for pantry staples such as salt, sugar, and baking powder. Chefs are 13
percentage points more likely than the average customer to buy the
less-expensive, private-label items, to opt for the store brand of flour, say,
rather than the branded flour. If everyone in the United States shopped like a
chef, the researchers estimate, spending on branded pantry staples would drop by
24%, leading to a fall of $20 million per year in the total amount spent on
pantry staples.
Overall, shopping with more knowledge
could cause a $1.1 billion drop in annual spending, as customers save money
through buying private-label goods. Some of the first to shift their preferences
might be Gentzkow’s MBA students, who told him they often bought branded Tylenol
and Advil before learning about the new research.
Brand
preferences are hard to shake
The revelation about pharmacists grew out
of an earlier question. Forgetting generics, when it comes to picking one brand
over another—an area where marketers are constantly trying to sway our
emotions—what if our preferences are actually stubborn, and tied to where we
grew up?
That’s the question Gentzkow set out to
answer with Dubé and Bronnenberg, building on previously published research Dubé
and Bronnenberg completed with
Sanjay K. Dhar, James H. Lorie
Professor of Marketing at Chicago Booth, which analyzed Nielsen data provided
by a large consumer-products company covering 30 categories of goods, including
soft drinks and ground coffee.
As the researchers had hopscotched through
the data from one US city to the next, they found broad, persistent, and
inexplicable differences in market share. Take the instant-coffee brands Folgers
and Maxwell House. Folgers dominates the West Coast, while Maxwell House leads
the Atlantic Seaboard. When it comes to beers, Budweiser ranks highest in most
US cities, but second in Chicago, behind Miller. Similar patterns hold in dozens
of categories. “We thought these products would have had the same market
share—because they’re the same thing,” Dubé says.
Baffled by the results, Dhar, Dubé, and
Bronnenberg eventually combed companies’ archives, searching for the date
products launched in a particular city. By linking these dates to market share
data that depict what happens after the brands launch in that city, simply
looking at which brand launched first explains more than 80% of the geographic
variance in several categories. In other words, the data show that a brand’s
“early-mover advantage,” a notion Dubé had long been skeptical about, can
persist for generations.
Dubé, Bronnenberg, and Gentzkow extend
this research from the overall preferences of entire cities to the habits of
individual shoppers. In a survey commissioned through and administered by
Nielsen, they ask more than 38,000 households for each adult’s current home
location, country and state of birth, gender, birth date, education, and
employment status, among other pieces of information.
Then the researchers match the survey
results with the same group’s grocery purchases, as recorded by the Nielsen
consumer panel data, collected from shoppers themselves. The conclusion: 40% of
the geographic variation in product market share can be explained by where
customers were living when they formed their early shopping habits, and the
effects can take years to dissipate. If shoppers are made aware of these
unconscious preferences, they might become more rational in their purchases. For
example, they might be more willing to use a coupon to try a new brand.
It’s
better to be good than cheap
Coke and Pepsi dominate the soft-drink
aisles of grocery stores, while RC Cola often languishes on a dusty shelf. It’s
easy to see which brands and companies lead their respective product categories,
but it has long been difficult to explain why. Early-mover advantage has
something to do with it, but so too do the strategies that follow a product’s
launch. Researchers at Columbia and Princeton are using the Nielsen data to
investigate how firms establish their market share, looking at the prices and
sales of products from thousands of retailers to get a bigger picture of how
those companies do business.
The goal is to resolve a longstanding
debate in business-strategy circles about why some firms grow huge while others
don’t. Economists have floated plenty of theories. Larger firms could be more
efficient at holding down costs and selling their goods at cheaper prices, or
they might have better advertising or higher quality products. Some might have
established monopolies that allow them to charge big markups.
The three researchers—Columbia’s David E.
Weinstein, Columbia PhD student Colin Hottman, and Princeton’s Stephen
Redding—are combing through sales data from hundreds of thousands of bar codes
in categories from food to electronics to appliances. The number of individual
items they’re studying is comparable to the inventory of a Kmart or Walmart
megastore. “I had to buy a server to handle it,” says Weinstein.
The researchers’ approach to the question
of why some firms grow dominant hinges on a method of measuring how consumers
perceive product quality. The basic idea is that if two items sell for the same
price, their brands’ market shares should reflect the items’ perceived quality.
The brand of the higher-quality item would have a higher market share, and that
of the lower-quality item would capture less share. If the products being
compared sell for different prices, the researchers adjust their measurements
accordingly. If one item is 10% cheaper than a similar item, the researchers
predict its brand will have a slightly larger market share than its
more-expensive competition.
After that weighting is complete, what’s
left over is the difference in market share that can’t be explained by the
formula, and that difference shows how the quality of a product impacts its
sales. Quality, by this definition, is in the eye of the consumer.
For example, Coke and RC Cola are similar
products, but Coke is more successful because people prefer it. Why they prefer
it, whether they’re influenced by advertising or other factors, is a secondary
matter for the researchers. All they consider, besides price, is the perceived
quality.
The early results are upending a long-held
belief that the most successful companies are the ones that focus on reining in
expenses or establishing economies of scale. “The key determinant of firm
success is quality, much more than cost,” Weinstein says. “The most successful
firms produce goods that consumers think are high quality. It’s not just about
lower prices.”
In the past, researchers could observe
sales at the overall company level, but there was very little information about
each product a firm was producing. Now economists can understand how each
company operates at a minute level of detail. “It’s a bit like going from
studying atoms to suddenly being able to study subatomic particles, and seeing
what you can learn from observing electrons or protons,” says Weinstein.
Weinstein has published other papers based on Nielsen
data, including a widely noted study that uses sales of 10 million–20 million
products and shows that overall grocery prices are lower in larger cities. The
work validated the theories about international trade and economic geography
that earned Princeton professor and
New York Times
columnist Paul Krugman the Nobel Memorial Prize in Economic Sciences.
Weinstein hopes to use data from
individual bar codes of products sold at large numbers of stores to develop
more-accurate measures of US economic inflation, which affects cost-of-living
adjustments for workers and Social Security recipients, as well as Federal
Reserve policies linked to mortgage and other interest rates. When calculating
the Consumer Price Index, the Bureau of Labor Statistics (BLS) tries to create a
representative basket of goods. The main difficulty with this approach is that
the agency measures only prices, not quantities. As prices of items rise and
fall relative to one another, consumers make substitutions, and it isn’t always
clear whether the BLS is accurately reflecting these choices. Using the scanner
data could suggest refinements to the index based on what people actually buy in
a given month.
In a similar vein, Francisco Palomino and
Robert Dittmar, both at the University of Michigan’s Ross School of Business,
and Ozge Sahin at Johns Hopkins Carey Business School are looking at companies’
overall success by using the Nielsen data to measure “innovation risk” in
approximately 10 industries. By noting when new bar codes appear in the Nielsen
sales results, the researchers can see when a manufacturer introduces a new
product. If that product has poor sales, a company may be wasting resources that
could be better allocated elsewhere, Palomino says. The researchers’ next step
will be to track whether this innovation risk correlates to companies’ stock
prices.
Subsidies build brand loyalty
Because they track buying patterns over
time, the Nielsen data allow researchers a close-up view of how household
purchases respond to changes in income or government benefits. Booth’s Dhar,
with Romana Khan of Özyeğin University in Turkey, and Ting Zhu of the University
of British Columbia’s Sauder School of Business, is examining the purchasing
habits of families that enroll in WIC, a US federal nutrition program for women
and young children.
The WIC program—formally called the
Special Supplemental Nutrition Program for Women, Infants, and Children—promotes
nutrition and healthy eating habits for low-income pregnant women, new mothers,
and their children. The program was established in 1972, after doctors noted
that pregnant women were coming to their offices with health problems caused by
a lack of food. Administered by the US Department of Agriculture, WIC served
about 9 million participants each month in 2012, including 53% of the country’s
infants.
In most cases, participants receive
monthly vouchers, or electronic benefits cards to purchase foods from a
restricted list designed to supplement their diets with specific nutrients.
Their choices—which are determined by WIC offices in each state—generally
include specific brands of cereal, whole-wheat bread, fruit, vegetable juice,
eggs, milk, cheese, peanut butter, and canned beans. Food-package costs average
$45 a month, for an annual taxpayer cost of $5 billion. The WIC program
generally reimburses each retailer the full price of a participants’ purchase.
While WIC is generally a short-term,
two-year subsidy to improve the health of mothers and children, the researchers
wonder whether its impact on behavior may persist beyond participation in the
program, creating new habits and brand preferences. Once households lose their
WIC benefits, their choice of what to buy is no longer limited to a list of
WIC-approved items and brands. But does WIC get participants hooked on certain
products?
To find out, the researchers use the
Nielsen consumer panel dataset, in which participants self-report their
purchases. That dataset also identifies individuals who have received WIC
benefits, which lets the researchers track the buying habits of people before,
during, and after they participate in the WIC program. The researchers start by
looking specifically at breakfast cereals, a category that appears to benefit
from WIC, as the program accounts for 7% of the $9 billion cereal category, the
researchers estimate.
Dhar, Khan, and Zhu are in early stages of
their work, but what they have found confirms what previous, less data-intensive
surveys have shown: the WIC program may have a lasting effect on consumer
choice, changing behavior in ways that outlast participation in the program.
After WIC, households spent more on cereal annually, a roughly 15% increase
relative to what they spent before. Many households who bought WIC-approved
cereals continued to buy the same cereal brands even after leaving the WIC
program.
Involvement in WIC has a particularly strong effect on
participants whose income is in the bottom quintile of national income. The
researchers find that those participants—who generally have poor nutrition,
suffer high rates of obesity, and have bad health—are most likely to change
their purchasing behavior during and after receiving WIC benefits.
But the data suggest the program also has
a big impact on food manufacturers, showing why firms lobby intensively to have
their products included on each state’s list of WIC-approved foods. General
Mills once revealed that in 1998, WIC accounted for 3% of the company’s annual
sales, say the researchers, though companies generally don’t release their sales
related to the WIC program. This early research suggests that WIC, beyond being
a short-term subsidy for food companies, also affords these companies an
opportunity to build brand loyalty and expand a market.
As Dhar, Khan, and Zhu dig further, they
are asking related questions. Do WIC participants get better nutrition,
achieving the program’s goals? Does a subsidy for nutritious foods result in
increased spending on nonnutritious foods? Do the healthier habits WIC supports
persist, and for how long? That work is ongoing.
Government vouchers lead to higher prices
Data about WIC participants may yield
other insights. For example, are WIC program subsidies distorting the free
market, affecting the prices for many consumers, even those not participating in
the program?
For decades, shoppers who enrolled in the
WIC program had to submit a paper voucher to get their groceries. The government
reimbursed stores on a per-item basis, at an agreed-upon price or price range.
That meant stores had an incentive to charge as high in that range as possible
because the government had agreed to essentially pay that price. Stores weren’t
supposed to raise prices just to bilk the government, but there were few
checks.
Similar cost-inflation pressures have
existed in government health-insurance programs such as Medicaid, where doctors
order extra procedures that don’t cost the patient anything at the time of
treatment. Doctors earn more, and taxpayers pick up the tab. Since WIC
participants are unaffected by the cost of items, they don’t care if a store
raises prices.
Katherine Meckel, an economics PhD student
at Columbia University, is studying how stores responded when the government
cracked down on how much retailers could charge for WIC items. She’s looking
specifically at Texas, where the program’s regulations became stricter between
2004 and 2009. In that state, stores are required to accept electronic benefits
cards instead of vouchers. The electronic cards track purchases more precisely,
so it’s hard for retailers to substitute items or record inaccurate prices.
Stores also have to agree in advance on what they would charge for certain
brands of bread, milk, and other items on the WIC list. All states have to
implement this new system by 2020, and so far six have done so. Texas was an
early adopter.
Meckel is analyzing purchase data from the
6,000 Texas households participating in the Nielsen consumer panel dataset,
which contains purchases made by the households at various retail stores. She’s
matching that information with government data to identify 2,000 stores that
accept WIC benefits. The dataset covers about 300,000 purchases made by roughly
6,000 households.
With the dataset, Meckel can observe
whether a panelist is part of the WIC program, and she tracks product prices
before and after the tighter surveillance of WIC took effect. While her results
are preliminary, one early discovery she finds surprising: many consumers who
were not WIC participants were paying higher prices because they shopped at
stores that participated in the WIC program. An individual store set the price
for an item and got reimbursed by the government. The store had an incentive to
charge a high price to people shopping with WIC benefits in order to collect the
maximum reimbursement possible. But as a result, the store charged all of its
customers, even people shopping without WIC benefits, that same high price.
That said, she finds that the new
regulations may help contain costs but also drive retailers away from the
program, in part because they can be more easily punished for breaking rules. If
a small store runs out of a WIC-approved brand of bread, it will get fined if it
sells the customer a different loaf.
Indeed, many of the retailers dropping out
of the WIC program are small stores in impoverished areas, and many are in
cities. While tighter regulation saves the government money, it also makes it
harder for WIC participants to use the program. Meckel says officials might
consider relaxing rules for stores that have trouble complying because of their
size, in order to preserve access for low-
income
families.
A soda
tax could save billions
Other behavior patterns in the Nielsen
data could be used to promote better nutrition for a wider group of people. The
issue: soft drinks. Public-health experts have long argued a link between soda
and high levels of obesity, but the debate over how to discourage soda
consumption has raged for a decade.
One perennially unpopular idea is to tax
soft drinks, as consumers can buy these drinks tax-free in almost one third of
US states. In New York, a proposal that would have imposed a 1¢-per-ounce soda
tax failed to make it to the ballot in 2010. Soon after, in 2012, New York Mayor
Michael Bloomberg took a different approach and proposed making it illegal for
his city’s restaurants, movie theaters, and stadiums to sell sugary drinks
bigger than 16 ounces. The controversial ban was quickly tied up
in court.
A new study provides more support for the
notion of a soft-drink tax. University of Chicago economics PhD student Avigail
Kifer uses Nielsen data to analyze the soda purchasing habits in 40 US counties,
tracking millions of purchases. She matches those results with data from the US
federal government’s Centers for Disease Control and Prevention on each county’s
mean body-mass index, or BMI, a measure of body fat. She then compares the mean
BMI of each county to the soda-buying habits of its residents, as measured in
the Nielsen consumer panel dataset, which asks households to record every item
purchased. Through that data, she tracks consumption as well as the specific
price paid for each bottle of soda.
Kifer finds that residents of the high-BMI
counties in her sample were, on average, 16 pounds heavier than residents in the
low-BMI counties. The high-BMI-county residents also exhibited stronger
preferences for soda, both diet and regular. Soda cost less in those counties,
and the residents drank more of it. This relationship, says Kifer, “further
confirms the role of sugar-sweetened carbonated beverages in the obesity
epidemic.” People in high-BMI counties consumed modestly smaller quantities of
other sugary drinks, and a roughly equal number of low-calorie drinks.
The demand for both diet and regular soft
drinks turns out to be fairly elastic—when the price goes up, people buy less of
them. Moreover when soda prices go up, consumers don’t often substitute sugary
drinks such as V8 Splash, lemonade, or orange juice, in part because they’re
usually more expensive. Many switch to water.
Kifer finds that 3.5% of the weight
difference between residents in high- and low-BMI counties may be attributable
to differences in how much regular soda they drink. And she concludes that an
excise tax of 3¢ per ounce would, by encouraging people to buy something other
than soft drinks, eliminate the soda-specific weight gain. Such a tax would lead
to a 5.8% drop in a county’s mean BMI over a 20-year period.
That could save a lot of money currently
being put towards health care. An estimated $190 billion has been spent treating
obesity-related diseases, according to research by Cornell’s John Cawley and
Lehigh University’s Chad Meyerhoefer. One study, commissioned last year by the
Trust for America’s Health and the Robert Wood Johnson Foundation, and carried
out by the National Heart Forum, predicts that a 5% decline in mean BMI by 2030
will allow the state of California to save $81.7 billion and spare 796,430
Californians from developing type 2 diabetes. A drop of 5.8% in BMI, which Kifer
predicts, could save a good chunk of change.
Given
information, people eat healthier
If a consumer drinks a 30-ounce sugary
soda, she may do so knowing full well that it’s unhealthy. But in many cases it
can be hard to identify healthy foods. Nutritional data presented on a product
label can be difficult to decipher, and nutritional claims made in commercials
or on a product’s packaging can be misleading. A person buying a food because it
claims to be low-calorie or low-fat may be unknowingly opting for a less-healthy
option within a product category. Low-fat peanut butter, for example, may be
higher in sodium than regular peanut butter.
To make it easier to identify healthy
foods, in 2008 a group of nutritional and medical experts, some affiliated with
the Yale University School of Medicine, created a new nutritional scoring
system, NuVal. NuVal employees go to grocery stores and scan the labels of tens
of thousands of products to record their nutritional information. Then NuVal’s
algorithm spits out scores that range from one to 100 and are easy to compare.
Keebler Townhouse Bistro Multigrain Crackers, for example, get a NuVal score of
three. Fresh broccoli florets get a perfect score of 100.
Several large chains, slightly more than
two dozen so far, are posting NuVal scores on shelf price tags. The people
behind NuVal hope that the scores, which measure relative nutrition rather than
calorie counts, will help people, particularly those who might otherwise get
swept up in fad-based diets.
The question is whether easy-to-read
nutritional information will change shoppers’ purchasing habits. After all,
everyone should already know that broccoli is healthy, and yet many choose to
eat something else. But Dubé;
Günter J. Hitsch, professor of
marketing at Chicago Booth; and Booth PhD student Jin Zhang have early evidence
that the scores may work as intended.
The researchers stress that their work is
in its early stages, but they have started by looking at data for purchases of
refrigerated yogurt. They use two datasets. The one purchased from NuVal shows
that the mean score for refrigerated yogurt products is 48, and the standard
deviation is 28. There’s a 91-point difference between the least- and
most-healthy yogurt, a large enough difference to get a shopper’s attention. To
find out what consumers do with this knowledge, the researchers use their second
dataset, the Nielsen consumer panel, observing purchases before NuVal scores
became available as well as after. They observe the behavior of households that
shop in stores posting NuVal scores, and that of households that shop
elsewhere.
The researchers consider their conclusions
preliminary, in part because NuVal data were only introduced in January 2009,
and in part because they are refining how they define a control group. But Zhang
says the researchers can claim, conservatively, some evidence that a consumer
presented with NuVal yogurt scores will pick a healthier option on the shelf.
Once that is further confirmed, they have more questions. Will a consumer who’s
seen NuVal scores remember those when they go to other stores that don’t display
the scores? Will they remember and act on what they’ve learned? For now, that
remains hidden in the data.
How to get a good deal on champagne
The Nielsen datasets are still new to
Booth. “We’ve been in development mode for about three years, getting things put
together and cleaned, organized, and validated,” says
Arthur Middlebrooks, executive
director of the Kilts Center and clinical professor of marketing at Chicago
Booth. The scanner data are being made available to non-Booth researchers this
fall, while the media data will still be available only to Booth researchers.
The center’s goal is to release an updated dataset to researchers annually. In
January 2014, Middlebrooks says, the Kilts Center hopes to release Nielsen data
collected from and about consumers in 2012.
Dhar predicts a wave of new research will
be published in the next year, as some of the earliest projects launched when
the data became available near completion. Of the more than 70 research projects
under way, one could suggest the best time to buy certain products. Juliette
Caminade and Denrick Bayot, University of Chicago PhD students in economics, are
using the Nielsen retail scanner data to examine the prices of goods for which
demand shoots up during particular holidays. They focus on sparkling wine, as
25% of all champagne sales occur during the last two weeks of December.
You might expect retailers to take
advantage of the demand to raise prices, Caminade says. But if you buy during
off-peak times, trying to outwit the stores, it turns out you’re actually
wasting money. Prices of sparkling wine fall about 10%—across all brands and
products—when consumers want it most, according to the data. The researchers are
primarily interested in why this effect occurs, so they’re looking at Nielsen’s
consumer panel information to tease out whether the demographics of people who
buy sparkling wine during the rest of the year are different from those who only
buy it during the holidays. Preliminary results show that year-round champagne
purchasers are more affluent, making them less price-sensitive than the holiday
drinkers. But the questions continue. Next time you pop a cork, you may
contribute to the answer.
Have a
question?
Nielsen’s retail scanner and consumer
panel datasets are available through Chicago Booth’s James M. Kilts Center for
Marketing to approved academic researchers, which includes US-based tenured and
tenured-track faculty, plus PhD students under their advisement.
To obtain a subscription to the data,
interested researchers need to first subscribe and register. Subscriptions are
available on individual and institutional bases. The Kilts Center does not make
a profit by providing Nielsen data to academic researchers, but it does charge
modest subscription fees to recover some of the costs of processing, storing,
cleaning, and distributing the data.
Around the world, billions of sales transactions every month, down to a can of Coca-Cola from a local store, are recorded in some way by Nielsen, the measurement and information firm that has been gathering data from retailers and consumers for 90 years. For most of its history, Nielsen shared those data primarily with its retail customers and manufacturing customers under strict agreements that protected customer confidentiality. Academic researchers gained access to some data by negotiating directly and often at length with Nielsen, or by partnering with a corporation and promising the data and results would be for internal use only. “We typically provided snippets of aggregate-level data [to academics],” says Frank Piotrowski, Nielsen’s senior vice president of measurement science and a 30-year company veteran. The situation was difficult for researchers—and for Nielsen, which wasn’t set up to easily release data to academics.
But in 2009, Nielsen executives were seriously thinking about how they could share data with researchers more systematically. The chairman of Nielsen’s Supervisory Board, James M. Kilts, who earned an MBA from Booth in 1974 and founded Booth’s James M. Kilts Center for Marketing in 1999, said he was interested in forging a partnership with the school. Working with Peter E. Rossi, cofounder of the Kilts Center and a former marketing professor at Chicago Booth, in 2010, Booth and Nielsen established a data warehouse under the umbrella of the Kilts Center as a central clearinghouse for the Nielsen information.Now Nielsen is sharing three datasets through Booth, with a staggering amount of information. One dataset covers purchases by 40,000–60,000 households in the United States. Another contains sales results from 35,000 stores—grocery stores, drugstores, discount chains, and similar outlets—for the years 2006 through 2011. Those records span up to 3 million bar codes, and the data represent about 33% of the volume at mass merchandisers and about 55% of US retail volume from grocery stores and drugstores.The information now available is a gold mine for researchers, marketers primarily, but also economists who see the potential to explore longstanding questions about consumer behavior. For years marketers focused their research on the coffee market, largely because the only good survey data available were household shopping diaries collected by local newspapers. Those diaries only covered a handful of product categories, including coffee. The data now available make coffee data look like a drop in a cup.And the data reveal a lot about a lot of things, not necessarily about consumers themselves but about corporate actions and public policy. Some of the findings could have major implications for retailers, manufacturers, and society at large. The insights could help shoppers find the best time to buy certain products, but they could also provide ammunition for supporters of soft-drink taxes, or critics of government benefit programs. “We’ve still barely scratched the surface,” says Jean-Pierre Dubé, Sigmund E. Edelstone Professor of Marketing at Chicago Booth.
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