• Cognizant ReportsHow Advanced Analytics Will Informand Transform U.S. Retail   Executive Summary                        ...
Forces Driving Analytics                           Force                                                                  ...
Disposable Income and Consumer Spending Trends                  7%                                                        ...
Employment Cost Index                                  3.0%                                  2.5%             Percent Chan...
Application of Analytics                                                           a class of techniques called cluster an...
•   Pricing optimization. Pricing can be a game         •   Operational analytics: This is a method in    changer for any ...
While all these benefits can be obtained by              which is emerging as an alternative way ofcrunching numbers inter...
11     K-means clustering is a segmentation technique where “n” observations are clustered in “k” clusters in      which e...
Marianne Kolbasuk McGee, “Outlook 2006: Confidence is Up, Barely,” InformationWeek, Jan. 2, 2006,http://www.informationwee...
Upcoming SlideShare
Loading in...5
×

How Advanced Analytics Will Inform and Transform U.S. Retail

3,788

Published on

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.

Published in: Technology
1 Comment
0 Likes
Statistics
Notes
  • Be the first to like this

No Downloads
Views
Total Views
3,788
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
1
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "How Advanced Analytics Will Inform and Transform U.S. Retail"

  1. 1. • Cognizant ReportsHow Advanced Analytics Will Informand 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. 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 CenterFigure 12010, 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 aconsumer spending are bouncing back after a dip ripple effect across global supply chains, drivingin 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 asimprovement in consumer demand, could help The employment cost index5 is also far above itsfurther 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 suggestsuggests that consumers are thriftier than ever that the U.S. retail industry is set for gradualbefore. As a result of limited consumer credit improvement. The good news: The slow but steadyfollowing the recession, consumers are increas- upturn is likely to generate a treasure trove ofingly opting for discounted products. This is data for retailers. The not-so-good news: Retailerfurther evidence that retailers need to understand capacity to effectively process, manage and applyshopper sentiments, product preferences and analytics to fast-growing volumes of data will becustomer segments better than before. tested. One key reason for this is that during the recession, many retailers cut costs to the bareRetailers face other macro-economic headwinds. minimum by reducing their workforce. HeadcountThe 34% spike in gasoline prices (from May 2010 reductions left many retailers suffering seriousGrowth 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 2010Source: U.S. Census BureauFigure 2 cognizant reports 2
  3. 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 Index4% 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. 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 ‘11Source: U.S. Bureau of Labor StatisticsFigure 5segment will have to continuously listen, respond ing. There were five billion mobile users globallyand 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 andtured 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 areconvert raw data into bankable knowledge. shared on Facebook every month. The project- ed growth in global data generated annually isOmnipresent Data expected to rise 40%, from 2011 to 2015.6Shopping is increasingly becoming multichan-nel, with customers completing transactions At a time when many retailers are offering anthrough smartphones, personal computers and equivalent range of products, using similar pro-physical stores, as well as researching products motional campaigns and suppliers and targetingthrough 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. ThisBenefits 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 thatSource: Thomas H. Davenport and Jeanne G. Harris, too much information is a hindrance to betterCompeting on Analytics: The New Science of Winning, decision-making.9 By using analytics to harnessHarvard 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 andBabson Executive Education, Working KnowledgeResearch Center, 2009. more informed decision-making.Figure 6 cognizant reports 4
  5. 5. Application of Analytics a class of techniques called cluster analysis and decision trees can be used. Within clusterThe practice of analytics has evolved from simple analysis, both hierarchical clustering andreporting to predictive modeling and optimiza- k-means clustering11 can be applied. In decisiontion (see Figure 7). trees, CART (Classification and RegressionRetailers’ 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. DavenportSource: Department of the Treasury, Financial Management Service, Debt Management ServicesFigure 7 cognizant reports 5
  6. 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 beThe Future of Retail Analytics a challenge without expert assistance. WorkingAs increasing amounts of data become available with a trusted third-party can bring sanity to anand 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. 7. While all these benefits can be obtained by which is emerging as an alternative way ofcrunching numbers internally, the complexity of handling business activities that can be moreindividual tasks that lead to successful outcomes easily standardized, virtualized and delivered viarequires the deployment of specialty teams with the cloud. While organizations obtain services ininterconnected and streamlined processes across a lower cost, pay-per-usefunctions. Traditionally, these teams are formed mode with BPaaS, they alsoby removing employees from existing depart- take advantage of expert While all these benefitsments and creating new functions, thus reducing advice from their service can be obtained bythe productivity of those departments and, in providers. crunching numberssome cases, adding overhead. On the other hand,if a specialist organization is given this respon- According to a 2011 CIO internally, thesibility, then operational efficiencies would be survey by InformationWeek, complexity of individual 20% of companies won’texpected to increase, since existing headcount own their IT systems in five tasks that lead towould remain flat and focused on core activities. years. In fact, 43% of the successful outcomesAnalytics service providers typically possess respondents surveyed are requires the deploymentdemonstrated capability and the experience to either using or plan to usestreamline and jumpstart the process. With large some type of cloud service of specialty teams withpools of experts, analytics service providers often in the next 12 months. If this interconnected andhave an industry-wide view as a result of their survey is any indication, the streamlined processeswork serving a wider audience. An outsider’s view future of retailing is uponof 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 softwaredetails. upkeep. With the right insights delivered as a service, no retailer should be left behind.This expertise can be provided through a modelknown as business process as a service (BPaaS),Footnotes1 “The Power of the Post-Recession Consumer,” Strategy+Business, Feb. 22, 2011, http://www.strategy-business.com/article/00054?gko=340d62 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.pdf4 “Flow of Funds Accounts of the United States,” Federal Reserve, June 9, 2011, http://www.federalreserve.gov/releases/z1/current/z1.pdf5 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-journey8 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=all9 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. 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.html13 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-optimization14 “Staples Makes it Easy for Customers with SAS Marketing Automation,” SASCOM Magazine, 2011, http://www.sas.com/success/staplesma.html15 “Making Sense of Retail Link Data,” Accelerated Analytics, http://www.acceleratedanalytics.com/ retaillink.htmlResourcesBarbara Farfan, “Retail Industry Information: Overview of Facts, Research, Data & Trivia 2011,”About.com, http://retailindustry.about.com/od/statisticsresearch/p/retailindustry.htmSreeradha D. Basu & Writankar Mukherjee, “Analytics Playing Critical Role in Retail Business,”The Economic Times, March 3, 2010, http://articles.economictimes.indiatimes.com/2010-03-03/news/27595077_1_analytics-retail-sector-retail-businessBarney Beal, “When Should You Outsource Customer Analytics,” SearchCRM.com, Jan. 12, 2010,http://searchcrm.techtarget.com/news/2240015848/When-should-you-outsource-customer-analytics“Get More for Your Non-Core Spend,” Everest Global, Inc., 2010, https://www1.vtrenz.net/imarkowner-files/ownerassets/749/Get_More_From_Your_Non-core_Spend_Greif_Case_Study.pdfDave Rich, Brian McCarthy and Jeanne Harris, “Getting Serious About Analytics,” Accenture, 2010,http://www.umsl.edu/~sauterv/DSS/Accenture_Getting_Serious_About_Analytics.pdfPedro Esquivias, Patricio Ramos and Robert Souza, “Business Model Adaptation in Retail,” The BostonConsulting Group, July 2010, http://www.bcg.com/documents/file56479.pdfBrad Loftus and Just Schurmann, “Staying Connected: Digital Advances in Multichannel Retailing,” TheBoston Consulting Group, Marketing in the Digital Economy, Nov. 23, 2010, http://publications.bcg.com/retail_branding_communications_staying_connectedDebbie Hauss, “Professor Tom Davenport Advises Retailers To Cultivate Potential Benefits of Analytics,”Retail Touchpoints, Feb. 19, 2009, http://www.retailtouchpoints.com/shopper-engagement/215-profes-sor-tom-davenport-advises-retailers-to-cultivate-potential-benefits-of-analytics“The Potential of Retail Analytics,” Teradata Magazine, December 2009, http://www.teradatamagazine.com/tdmo_assets/tdmo_pdfs/retail_analytics.pdfLarry Gordon, “Leading Practices in Market Basket Analysis,” The FactPoint Group, 2008,http://factpoint.com/pdf2/1.pdfBrad Loftus, John Mulliken and Jonathan Sharp, “The Multichannel Imperative,” The Boston ConsultingGroup, 2008, http://www.bcg.com/documents/file15308.pdfGeorge Stalk Jr. and Kevin Waddell, “Surviving the China Rip Tide,” The Boston Consulting Group, May2007, http://www.bcg.com/documents/file14992.pdf“Retailing 2015: New Frontiers,” PricewaterhouseCoopers/TNS Retail Foreward, 2007,http://www.pwc.com/en_US/us/retail-consumer/assets/retailing_2015.pdf“Size Optimization for Retailers,” SAS Institute, Inc., 2007, http://www.sas.com/resources/whitepaper/wp_3488.pdf cognizant reports 8
  9. 9. Marianne Kolbasuk McGee, “Outlook 2006: Confidence is Up, Barely,” InformationWeek, Jan. 2, 2006,http://www.informationweek.com/news/global-cio/showArticle.jhtml?articleID=175800049Tony Kontzer, “Big Bet on Customer Loyalty,” InformationWeek, Feb. 9, 2004, http://www.information-week.com/news/software/bi/showArticle.jhtml?articleID=17602075Bradford C. Johnson, “Retail: The Wal-Mart Effect,” McKinsey Quarterly, McKinsey & Co., February 2002,https://www.mckinseyquarterly.com/Retail_The_Wal-Mart_effect_1152Nina Abdelmessih, Michael Silverstein and Peter Stanger, “Winning the Online Consumer,” The BostonConsulting Group, 2001, http://www.bcg.com/documents/file13692.pdf“Allocation,” SAS Institute, Inc., http://www.sas.com/industry/retail/allocation/index.html“Space Planning and Optimization,” SAS Institute, Inc., http://www.sas.com/industry/retail/space-plan-ning/index.html“Assortment Planning and Optimization,” SAS Institute, Inc., http://www.sas.com/industry/retail/assort-ment-planning/index.html“Price Optimization,” SAS Institute, Inc., http://www.sas.com/industry/retail/price-optimization/index.htmlAuthorSanjay Fuloria, Ph.D. and Senior Research AnalystCognizant Research CenterSubject Matter ExpertJames Pise, Senior Engagement ManagerCognizant Enterprise Analytics PracticeAbout CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 111,000 employees as of March 31, 2011, Cognizant is a member of theNASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing andfastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. Haymarket House #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA 28-29 Haymarket Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London SW1Y 4SP UK Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7321 4888 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7321 4890 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com© Copyright 2011, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

×