Macroeconomic trends, changing consumer behavior and increased data volumes – these trends are forcing retailers to devise mechanisms or seek out partners who can quickly transform raw data into bankable insights.

1. • Cognizant Reports
How Advanced Analytics Will Inform
and Transform U.S. Retail
Executive Summary sales increasing at an estimated 4.4% CAGR,
and online retail sales expected to grow at an
During the “Great Recession,” many retailers were
estimated 10% CAGR for the next five years,
forced to cut costs to stay afloat. This cost-cutting
retailers will have a rich pool of available data
gave some retailers a head start toward profit-
from Web and store interactions to apply to
ability when the recovery began in 2009. Two
more timely and precise decision-making.
years later, conditions are now ripe to embrace
new technologies and processes to capitalize on The good news — in addition to the availabil-
building market momentum. One critical area for ity of more data — is the evolution of analytics,
investment is advanced analytics. which has transitioned from standard reports to
real-time data feeds that can optimize planning
Three forces are at work, reinforcing the need for
and fine-tune business strategies on the fly.
retailers to more quickly transform the torrent of
Among the emerging approaches are operation-
raw data generated from the Web (including social
al analytics, text analytics, sentiment analysis
media) and their brick-and-mortar presence into
and visual analytics. According to a study by
bankable insights:
Thomas Davenport, an analytics guru and dis-
1. Macroeconomic indicators: Indicators such tinguished professor of management and infor-
as personal disposable income, consumption mation technology at Babson College,2 retailers
expenditure and consumer confidence all point must look at applying analytics not only to
to a modest recovery for retailers (although maintain ruthless cost-cutting strategies but also
the recent spike in oil prices could undermine to increase revenue across key geographies and
these projections). Quicker conversion of raw market segments. The emergence of analytics
data into foresight will provide a first-mover services delivered via a cloud infrastructure
advantage that is critical to retailers seeking can enable retailers to leverage lower-cost, pay-
to establish or maintain segment leadership. per-use models and skilled analytical resources,
regardless of physical location.
2. “Spend shifters:” The emerging class of
“spend shifters” — or people who have down- Macroeconomic Indicators
shifted their purchasing habits by buying less,
The retail industry represents a key sector of the
choosing less expensive brands and saving
U.S. economy, with a total value of $4 trillion in
more1 — has made it imperative for retailers to
20103 and an estimated 27% of the U.S. Gross
correctly target their customers.
Domestic Product (GDP) emanating from retail
3. The ever-increasing volume of data accessi- consumption.4 The retail industry’s share of
ble from multiple sources: With offline retail employment is roughly 12%. Between 2001 and
cognizant reports | july 2011

2. Forces Driving Analytics
Force Application
Implication Impact of Analytics
Disposable personal income The retail industry is set for Apply analytics to identify
and consumer spending are better times, but consumers and serve the right consumer,
Macroeconomic bouncing back; consumer are more value-conscious. profitably.
confidence is growing.
Consumers are becoming more Retailers need to continuously Apply analytics to bolster both
tech-savvy and want transpar- listen, respond and innovate to the top and bottom lines.
Spend Shifting ency from retailers. cater to more knowledgeable
consumers.
Data is available from multiple There are increased Leverage analytics to make
Omnipresent sources, including mobile and opportunities to analyze data. better decisions.
Data social media.
Source: Cognizant Research Center
Figure 1
2010, U.S. retail revenues grew at CAGR of 4.4% to March 2011) is clearly a deterrent to retail per-
(see Figure 2). Disposable personal income and formance. Sky-high gasoline prices are causing a
consumer spending are bouncing back after a dip ripple effect across global supply chains, driving
in 2009, indicating a revival in consumer demand up structural costs. As a result, retailers are
(see Figure 3). The long-awaited but still uncertain passing on the extra cost to consumers.
increase in employment and income, as well as
improvement in consumer demand, could help The employment cost index5 is also far above its
further fuel the recovery of retail sales in 2011. pre-recession levels (see Figure 5). This is adding
further to retailers’ total overhead.
The consumer confidence index (see Figure 4)
also shows an upward trend. However, research Overall, key macroeconomic indicators suggest
suggests that consumers are thriftier than ever that the U.S. retail industry is set for gradual
before. As a result of limited consumer credit improvement. The good news: The slow but steady
following the recession, consumers are increas- upturn is likely to generate a treasure trove of
ingly opting for discounted products. This is data for retailers. The not-so-good news: Retailer
further evidence that retailers need to understand capacity to effectively process, manage and apply
shopper sentiments, product preferences and analytics to fast-growing volumes of data will be
customer segments better than before. tested. One key reason for this is that during the
recession, many retailers cut costs to the bare
Retailers face other macro-economic headwinds. minimum by reducing their workforce. Headcount
The 34% spike in gasoline prices (from May 2010 reductions left many retailers suffering serious
Growth in Retail for Supermarkets, General Merchandise Stores
$1,500 $1,313 $1,316 $1,387
$1,205 $1,270
Revenues ($ billions)
$1,070 $1,139
$939 $966 $1,009
$1,000
CAGR = 4.4%
$500
$0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Source: U.S. Census Bureau
Figure 2
cognizant reports 2

3. Disposable Income and Consumer Spending Trends
7% 7%
6% 6%
6% 6% 5%
5% 5% 5%
5% 5% 5%
5% 4%
4% 4%
4% 4%
3% 3%
2%
1%
0%
-1%
-2%
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
YoY change in consumption expenditure YoY change in disposable personal income
Source: U.S. Census Bureau
Figure 3
brain drain; in some cases, they eliminated Spend Shifters
specialty positions that were considered a luxury
The Great Recession was preceded by a con-
— business intelligence and analytics among
sumption binge. Consumers spent wildly on
them.
many luxury items. But today, post-recession
The projected uptick in business provides an consumers are radically reducing their rate of
opportunity to maximize value from available consumption and conspicuous display of wealth.
human resources, while extending capabili- Consumer spending is no longer outdistancing
ties with third-party experts. A trusted partner personal income. This is both a voluntary decision,
specializing in retail analytics can help rapidly as well as a result of increased consumer savings
transform data into actionable insights by tapping and reduced borrowing, due primarily to tighter
emerging techniques, such as operational, text, credit policies.
sentiment and visual analytics. If these analytics
Two attributes of spend shifters is that they are
are delivered as a service, the retailer will then
highly tech-savvy, and they want retailers to be as
pay only for insights that are actually used, elimi-
transparent as possible. They crave information
nating the need for additional capital expendi-
about their retailers (product offerings, services,
tures, since no additional investment in hardware
pricing, etc.), and they usually find it. Therefore,
or software is required.
retailers that wish to serve this consumer
Consumer Confidence Index
4%
150
125
Index 1st Quarter 1977-2011
100
75
50
25
0
1975 1980 1985 1990 1995 2000 2005 2010 2015
Shaded areas indicate U.S. recessions
Source: U.S. Census Bureau
Figure 4
cognizant reports 3

4. Employment Cost Index
3.0%
2.5%
Percent Change (%)
2.0%
1.5%
1.0%
0.5%
0%
March ‘09 June ‘09 Sept. ‘09 Dec. ‘09 March ‘10 June ’10 Sept.’10 Dec. ‘10 March ‘11
Source: U.S. Bureau of Labor Statistics
Figure 5
segment will have to continuously listen, respond ing. There were five billion mobile users globally
and innovate. The growing use of social media in 2010, generating data about their location,
gives retailers an additional stream of unstruc- the Web sites they visit and the products and
tured information on this segment’s beliefs and services they buy through their mobile phones.
behaviors, presenting a great opportunity to Approximately 30 billion pieces of content are
convert raw data into bankable knowledge. shared on Facebook every month. The project-
ed growth in global data generated annually is
Omnipresent Data expected to rise 40%, from 2011 to 2015.6
Shopping is increasingly becoming multichan-
nel, with customers completing transactions At a time when many retailers are offering an
through smartphones, personal computers and equivalent range of products, using similar pro-
physical stores, as well as researching products motional campaigns and suppliers and targeting
through social networks. All these devices gen- the same customers, a key point of differentia-
erate growing volumes of consumer data, which tion will be the quality of their decision-making.
retailers can utilize for more effective target- This makes it incumbent on retailers to not only
integrate data from various sources, but to also
use analytics to enhance business capability. This
Benefits of Analytics will help inform strategy and lead to long-term
benefits (see Figure 6).
Organization Benefits Attributed to
Analytics One of the most important activities for any
business is optimizing business processes, and a
Improved direct mail performance very effective way to do this is to use analytics
Cabela’s7
by 60%.
across various functions within the organization.
Increased response rate for coupons Customer, employee and supplier data is plentiful
Sam’s Club8
from 2% to 20%. and to some degree is the “oxygen” for analytics.
Analytics capability viewed as a nine- Still, survey after survey reveals that organiza-
CVS
figure profit center. tions are not effectively using all the data they
Hudson’s Bay Achieved a 2:1 return on its database have. According to the study “CFO Insights:
Corp. management and analytical efforts. Delivering High Performance,” 60% of workers
Increased inventory turns by 10%.
feel overwhelmed by the amount of information
JCPenney
they receive, and 43% of managers believe that
Source: Thomas H. Davenport and Jeanne G. Harris, too much information is a hindrance to better
Competing on Analytics: The New Science of Winning, decision-making.9 By using analytics to harness
Harvard Business School Press, 2007; Thomas H. the plethora of information in organizations,
Davenport, “Realizing the Potential of Retail Analytics,”
management has the ability to drive better and
Babson Executive Education, Working Knowledge
Research Center, 2009. more informed decision-making.
Figure 6
cognizant reports 4

5. Application of Analytics a class of techniques called cluster analysis
and decision trees can be used. Within cluster
The practice of analytics has evolved from simple
analysis, both hierarchical clustering and
reporting to predictive modeling and optimiza-
k-means clustering11 can be applied. In decision
tion (see Figure 7).
trees, CART (Classification and Regression
Retailers’ use of analytics has gradually spread to Trees) and CHAID (Chi Square Automatic Inter-
several key areas of business: action Detection) can be utilized. These clas-
sification techniques can profile consumers
• Identifying the most profitable customers.
using demographic and behavioral predictors.
Correctly identifying the most profitable
Cross-shopping behavior can also be analyzed.
customers can maximize a retailer’s return
Retailers can test hypotheses, such as whether
on investment for various in-store activities.
they should differentiate their products if their
Retailer offerings should align with the
strategic positioning is “low price.”
behavior of the most profitable customer
segments. For instance, the most profitable • Assortment planning and optimization. This
customers should be able to easily find desired helps retailers discover various style and color
products. If retailers ensure the availabil- combinations, as well as the quantity they
ity of the right items at the right locations in should buy. The retailer’s capacity require-
the right quantities for the most profitable ments can then be defined and addressed.
customers, margins will increase. For instance, Consumer choice models can become the
when analytics helped Best Buy identify that platform for assortment planning. These
7% of its customers accounted for 43% of its models have been further classified into utility-
sales, it reorganized its stores to address the based choice models and exogenous demand
needs of these high-value customers.10 models. In 2004, Walmart noticed a rush to
purchase flashlights and batteries before a
• Understanding customer behavior. Though
hurricane struck. The forecast of a hurricane
each customer is considered unique, it is
also resulted in an increased sale of Pop-Tarts,
still possible to classify groups of customers
a sugary breakfast food.12 This insight was
who exhibit similar behaviors. If retailers can
obtained through exogenous demand models.
understand this behavior, they can target
specific customer types more effectively. This • Accurate prediction of what products should
classification will also help them with cross-sell be sold together. Market basket analysis
and up-sell opportunities, as well as targeted provides retailers with insights into the com-
marketing. To accomplish the required bination of products to be stacked together to
customer classification and segmentation, increase individual transactions.
The Evolution of Analytics
Optimization What is the best that can happen?
Future
u
Future
Predictive Modeling What will happen next
next?
(with analytics
and business
s
business Forecasting/Extrapolation What if these trends continue?
intelligence)
e
intelligence)
nce
ige
Statistical Analysis
n tell Why is this happening?
el of I
KPIs/Alerts e Lev What actions are needed?
siv
res
P rog
Current
Current
e Query/Drill Down Where exactly is the problem?
state
a
state
orts
Ad Hoc Reports How many, how often, where?
Re
Standard Reports What happened?
Degree of Intelligence
Note: Adapted from Competing on Analytics: The New Science of Winning, Thomas H. Davenport
Source: Department of the Treasury, Financial Management Service, Debt Management Services
Figure 7
cognizant reports 5

6. • Pricing optimization. Pricing can be a game • Operational analytics: This is a method in
changer for any retailer. Macy’s was able to which automated decisions are made almost
analyze pricing in 95% less time using price immediately, as they are typically embedded
optimization. For pricing each item on a weekly within operational business processes. With
basis, Macy’s examines the last three years’ better real-time availability of shopping cart
worth of data and analyzes which of these information, as well as automated rule en-
items were sold in exceptional quantities, even gines or rapid scoring of purchase behavior,
at a higher price, and which items were not it becomes possible to offer promotions and
sold at a higher price. Based on this, merchan- maintain stock in real time. This real-time in-
dizers place an optimal price on each item. formation can be obtained by using RFID tags
Pricing optimization is credited with saving attached to each product. As soon as the item
Macy’s 70% in hardware costs, according to is put into a shopping cart, a message is dis-
Brian Leinbach, Macy’s senior vice present, patched to a supervisor to refill the shelf space.
systems development applications.13 Analyzing this information over time will drive
better inventory management.
Staples is another retailer that puts pricing
optimization to effective use. Staples runs • Text analytics: Loads of data are generated
approximately 1,500 multichannel campaigns through social media. Retailers need to ana-
and uses analytics to discover up-selling and lyze this data to proactively see consumer
cross-selling opportunities. In this context, Jim trends and respond appropriately to discus-
Foreman, the company’s director of circulation sions that can affect retailer performance
and analytics, recently told SASCOM Magazine: and reputation. This technique can also be
“We did a financial analysis of the implementa- used to understand in totality what custom-
tion, and we found that we were getting a rate ers think about a particular product or service.
of return of 137%. That’s about as much of a Qualitative analysis of consumer behavior on
slam dunk as you are going to see.” 14 Web pages, blogs, and social media can help
determine more granular customer attitudes
• Procurement and spend analytics. This
toward particular products. Text analysis of
optimizes the organization’s supply-side per-
the information shared by customers on so-
formance by integrating data emanating
cial networking sites and blogs is conducted
from the enterprise value chain. In this
to learn customer dispositions on a wide range
scenario, data from the supplier’s supplier is
of product and company attributes. This could
integrated all the way to the customer. The
be very helpful in getting the strategy of the
retailer, therefore, would know, with precision,
retailer on the right track.
total product costs. This helps in identify-
ing cost savings across geographies, product • Visual analytics: This is used to summarize
categories, business units and procurement patterns and activities in video images and to
organizations. Suppliers that are inconsistent create alerts for particularly undesirable (or
can also be identified. Data collection time desirable) behavior in which human viewing
can be reduced to allow managers to focus on would be required. This can be used in both in-
decision making. store and online settings.
Walmart uses an inventory management
Harnessing New Opportunities:
system (called Retail Link) that enables its
suppliers to see exactly how many of its
Analytics as a Service
products are on every shelf of every store at While the aforementioned methods are moving
any given moment. This helps Walmart manage into the mainstream, big retailers with multiple
its stocks better. Published case studies reveal business units tend to have various databases
that the retailer’s sales increased by more than supporting a wide range of market initiatives.
40% per SKU as a result of using Retail Link.15 Collating and analyzing all necessary data
elements from disparate sources, however, can be
The Future of Retail Analytics a challenge without expert assistance. Working
As increasing amounts of data become available with a trusted third-party can bring sanity to an
and analytics grow more sophisticated, the otherwise chaotic process beset by resource con-
following types of applications should be straints.
considered by retailers:
cognizant reports 6

7. While all these benefits can be obtained by which is emerging as an alternative way of
crunching numbers internally, the complexity of handling business activities that can be more
individual tasks that lead to successful outcomes easily standardized, virtualized and delivered via
requires the deployment of specialty teams with the cloud. While organizations obtain services in
interconnected and streamlined processes across a lower cost, pay-per-use
functions. Traditionally, these teams are formed mode with BPaaS, they also
by removing employees from existing depart- take advantage of expert
While all these benefits
ments and creating new functions, thus reducing advice from their service can be obtained by
the productivity of those departments and, in providers. crunching numbers
some cases, adding overhead. On the other hand,
if a specialist organization is given this respon- According to a 2011 CIO internally, the
sibility, then operational efficiencies would be survey by InformationWeek, complexity of individual
20% of companies won’t
expected to increase, since existing headcount
own their IT systems in five
tasks that lead to
would remain flat and focused on core activities.
years. In fact, 43% of the successful outcomes
Analytics service providers typically possess respondents surveyed are requires the deployment
demonstrated capability and the experience to either using or plan to use
streamline and jumpstart the process. With large some type of cloud service
of specialty teams with
pools of experts, analytics service providers often in the next 12 months. If this interconnected and
have an industry-wide view as a result of their survey is any indication, the streamlined processes
work serving a wider audience. An outsider’s view future of retailing is upon
of the data and analysis could also be a differenti- us, a future where systems
across functions.
ator because in-house experts can be biased and ownership is left to spe-
challenged to see the bigger picture vs. business cialists responsible for hardware and software
details. upkeep. With the right insights delivered as a
service, no retailer should be left behind.
This expertise can be provided through a model
known as business process as a service (BPaaS),
Footnotes
1
“The Power of the Post-Recession Consumer,” Strategy+Business, Feb. 22, 2011,
http://www.strategy-business.com/article/00054?gko=340d6
2
Thomas H. Davenport, “Realizing the Potential of Retail Analytics,” Babson Executive Education,
Working Knowledge Research Center, 2009.
3
“Estimated Annual Sales of U.S. Retail and Food Services Firms by Kind of Business, 1998 through
2009,” U.S. Census Bureau, http://www2.census.gov/retail/releases/current/arts/sales.pdf
4
“Flow of Funds Accounts of the United States,” Federal Reserve, June 9, 2011,
http://www.federalreserve.gov/releases/z1/current/z1.pdf
5
Employment Cost Index (ECI) measures the change in the cost of labor among occupations and
industries.
6
“Big Data: The Next Frontier for Innovation, Competition and Productivity,” McKinsey Global Institute,
May 2011, http://www.mckinsey.com/mgi/publications/big_data/
7
Deena M. Amato-McCoy, “A Data Filled Journey” Chain Store Age, May 31, 2009,
http://www.chainstoreage.com/article/adata-filled-journey
8
Andrew Martin, “Sam’s Club Personalizes Discounts for Buyers,” New York Times, May 30, 2010,
http://www.nytimes.com/2010/05/31/business/31loyalty.html?pagewanted=all
9
Michael Sutcliff and Michael Donnellan, CFO Insights: Delivering High Performance, Wiley, May 24,
2006.
10
Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning,
Harvard Business School Press, 2007.
cognizant reports 7

8. 11
K-means clustering is a segmentation technique where “n” observations are clustered in “k” clusters in
which each observation belongs to the cluster with the nearest mean.
12
Constance L. Hays, “What Walmart Knows About Customers’ Habits,” The New York Times, Nov. 14,
2004, http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html
13
Debbie Hauss, “SAS Global Forum: Macy’s Speeds Pricing, Cuts Hardware Costs with SAS Markdown
Optimization,” Retail Touchpoints, April 6, 2011, http://www.retailtouchpoints.com/retail-store-ops/822-
macys-speeds-pricing-cuts-hardware-costs-with-sas-markdown-optimization
14
“Staples Makes it Easy for Customers with SAS Marketing Automation,” SASCOM Magazine, 2011,
http://www.sas.com/success/staplesma.html
15
“Making Sense of Retail Link Data,” Accelerated Analytics, http://www.acceleratedanalytics.com/
retaillink.html
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cognizant reports 8

9. Marianne Kolbasuk McGee, “Outlook 2006: Confidence is Up, Barely,” InformationWeek, Jan. 2, 2006,
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Author
Sanjay Fuloria, Ph.D. and Senior Research Analyst
Cognizant Research Center
Subject Matter Expert
James Pise, Senior Engagement Manager
Cognizant Enterprise Analytics Practice
About Cognizant
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