360 Analytics - A Data-Driven Business Model for Retailers. Global Consulting Practice. White Paper

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1 Global Cosultig Practice White Paper 360 Aalytics - A Data-Drive Busiess Model for Retailers How retail compaies ca leverage the opportuities of digital chage

2 About the Authors Dr. Clemes Bachma Clemes Bachma is Lead Cosultat for the Retail Cetral Europe divisio at Tata Cosultacy Services (TCS). He helps customers from the retail sector idetify potetial opportuities for optimizatio ad develop solutios. Before joiig TCS i May 2013, Bachma worked for early 20 years i the retail ad cosumer busiess i Germay ad iteratioally. Alog with providig idustry expertise, his previous resposibilities icluded maagig digital trasformatios for PwC Maagemet Cosultig ad headig iteratioal cosultig projects. Dr. Kay Müller-Joes Kay Müller-Joes is head of the Global Cosultig Practice (GCP) for Germay at TCS ad is also resposible for the busiess ad IT cosultig divisio for Cetral Europe. Before joiig TCS, Müller-Joes worked as brach maager at Capgemii sd&m AG i Berli for five years, ad was previously the divisio head ad techical director at cosultig ad IT service provider, Logica Deutschlad GmbH & Co. KG. He has bee a member of the Global Techology Board ad also maaged the compay's Techology Competece Ceter (TCC).

3 Today, retail compaies ad maufacturers who are active as retailers are facig a double challege the chagig eeds of digital cosumers ad the rise of ew digital competitors. The shift has bee drive by four mai factors: mobility, social media, Big Data, ad cloud computig. These drivers are ot oly a challege but also disruptive for traditioal busiess models. How ca retailers cope with the chage, ad better still, use the ecessary trasformatio of processes ad systems as a opportuity? I this paper, we show how a holistic cosideratio of the four drivers of digital chage, ad the use of the 360 Aalytics approach ca create a ew, data-drive busiess model that delivers lastig market ad growth potetial.

4 Cotets About the authors 3 Itroductio: Digital Chage i Retail 4 The impact of digital chage o traditioal busiess models 4 Opportuities arisig from digital chage Aalytics - a data-drive busiess model for retailers 10 Overview: the 360 Aalytics approach i a retail compay 11 Key questios regardig the 360 Aalytics approach i retail compaies 11 Success factors for trasformatio to a data-drive busiess model 13 Coclusio 17 Refereces 17

5 Itroductio: Digital Chage i Retail The impact of digital chage o traditioal busiess models Retail compaies as well as maufacturers active as retailers, are challeged by the ew eeds of digital cosumers ad the growth of strog competitors emergig from the digital marketplace. Four mai factors mobility, social media, Big Data ad cloud computig drive the digital shift (see figure 1), which i tur raises the questio: How do these four factors affect the retail busiess ad its value chai, ad chage the eviromet i which retailers are operatig? Figure 1: The impact of digital chage o traditioal busiess models The results of a simple Google search of key terms reveal that these factors are already the subject of much discussio amog the geeral public. (Dr. Kay Müller-Joes' white paper, 1 'Digital Chage ad the Mobile Customer', presets deeper elaboratio of the relatioship betwee these drivers) The four drivers of digital chage have a 'disruptive' effect o a majority of compaies; that is, they challege traditioal successful busiess models. What makes thigs eve more complex is that these four drivers are mutually iterdepedet ad, i some cases, eforce each other. This makes a holistic [1]Dr. Kay Müller-Joes, TCS white paper 2012: Digital Chage ad the Mobile Customer (The Digital Shift ad Mobile Customers); dowload at 5

6 view ievitable. Accordigly, compaies must realig themselves ad iitiate a trasformatio of may of the busiess processes ad systems to which they have become accustomed. The sole exceptios are the 'pure players,' busiesses that operate olie oly like Amazo, Google ad ebay. This sectio takes a closer look at some of the challeges highlighted i figure I, focusig o examples from the retail idustry. These examples illustrate the irreversible oward march of the ogoig digital chage. High customer expectatios resultig from icreasigly better services offered by 'pure players': Free shippig, easy returs, same-day delivery ad full assortmets are some of the services offered by pure players that result i higher customer expectatios. The time widow for a successful market lauch is becomig icreasigly shorter. Also, customers expect the quality of services ad product rages offered i the ew market to closely resemble those available i the home markets. However, simply scalig up or tweakig a previously successful home market retail cocept, with its existig locatio ad merchadisig structures, is ot a optio i most situatios. As a result, ew rules are beig created at tremedous speed to coquer emergig markets. Icreasig price trasparecy ad pressure o margis, especially for braded goods: Competitive ad ew forms of offerig are oly a mouse-click away private auctios, fixed-price offers of used products, temporary promotios offered by producers ad the perks offered by customer clubs are some examples. Prices ad services ca be compared quickly ad easily. The resultig price pressure is itesified by simple itelliget tools such as price robots that iitiate direct price comparisos across all players, both olie ad i stores, ad other tools that ca itegrate data from barcodes read via smart phoes. Ope access to iformatio for digital cosumers, aytime, aywhere: Today, almost every purchasig decisio starts with olie research, as cosumers use recommedatios, tests, ad additioal product iformatio to support their decisio. This has two cosequeces: first, digital cosumers value a cosistet offerig of their favorite brads ad retailers across all chaels; ad secod, they geerally see themselves as experts whe dealig with sales staff osite. Icreasigly better experieces ad the outstadig ease of access to locatio-specific iformatio i the service sector (such as taxis or car sharig) will cotiue to boost cosumers' expectatios ad force retailers to offer more locatio-based, persoalized products ad services. Rapid spread of social media ad improved web access via smartphoes ad apps: This tred has both created ew markets ad further lowered barriers to etry i may segmets. As brick-ad-mortar retail stores struggle to lure customers, the cocept of specialty retail has grow expoetially. I almost every merchadise category, iteratioal ad local specialists operate o the web, ad i may cases, with broader product rages, more comprehesive services, ad better pricig as compared to their brick-ad-mortar rivals. 6

7 Opportuities arisig from digital chage It is importat to ot overlook the fact that the four mai drivers of digital chage also offer huge opportuities for retailers, give their close relatioships with digital cosumers as log as the ecessary trasformative steps are take towards buildig a 'data-drive busiess model'. Oe key feature of a data-drive busiess model is that it makes systematic use, across all departmets, of all data that help to achieve busiess targets. The crucial factor is ot to be put off by the complexity this udertakig seems to pose to the orgaizatio at first sight. It is importat to 'start the jourey' with a ope spirit, testig ew solutios early o, gatherig precious experiece ad learig quickly by trial ad error. Figure 2 highlights some of the key opportuities retail compaies ca leverage: Figure 2: Opportuities arisig from digital chage 7

8 For retailers, may opportuities arise directly from Big Data as a driver of digital chage. This is critically importat for the trasformatio to a data-drive busiess model; therefore this sectio takes a more detailed look at what Big Data meas for retailers. Fast aalysis of icreasig volumes of exteral ad ustructured data. Data ca differ fudametally i terms of its origi (iteral or exteral) ad structure (structured, partially structured, ad ustructured). Partially structured ad ustructured data geerated outside the busiess is quickly becomig a icreasigly large portio of the total volume, a tred that will likely cotiue. This is a cosequece of the icreasig umber of web-based iteractios betwee digital cosumers ad retailers, ad o social platforms such as Facebook, Xig, or LikedI. I additio, digital cosumers, retailers ad maufacturers are commuicatig more ofte with each other ad via various chaels. New reports o this growig volume of data appear almost daily. I 2010, Eric Schmidt, head of Google, observed that the volume of iformatio geerated worldwide every two days was equal to that geerated throughout all of huma history util The figure he cited at the time for two days' data 2 volume was five exabytes five billio gigabytes. I 2011, the secod Digital Uiverse study published by media ad research firm, IDC, forecast that the global volume of data would double every two years for the foreseeable future. The figure for Wester Europe aloe i 2013 will be more tha 1,000 exabytes. 3 4 I our excitemet over these extreme growth rates, we must ot lose sight of the fact that mere data aloe does ot offer ay ew isights or beefits. Every compay first has to discer which sources of data are available ad usable for its specific eeds. The structure ad cotet of may data stocks have to be cleaed up before they ca be aalyzed. The most critical factor here is to defie specific algorithms that guide the actios to be take, ad aswer questios o how to improve offerigs to beefit both the customer ad the retailer. The techological coditios for achievig this are gettig better by the day, with more robust processig power ad faster processig programs. These algorithms the make it possible to store high volumes of data effectively, aalyze it promptly, ad visualize it i the form of simple dashboards that allow eve o aalytical users to uderstad the required actios. To esure data protectio ad privacy, the aalysis ca be made based o aoymous data. With regard to persoalizig features ad offers, the compay must make sure that ay persoal recommedatio is made oly with the customer's explicit prior coset. Ideally, a customer's behavior ca be used to automatically geerate persoal product suggestios i real time. The particular beefit of usig ew techologies to aalyze data lies i the fact that exteral ad iteral, structured ad ustructured data ca be grouped together ad aalyzed joitly. This makes it possible to estimate ad eve predict the aswers to importat questios such as the busiess potetial of a certai locatio or a ew product with greater accuracy. [2] Eric Schmidt, ow Executive Chairma, the CEO of Google, speakig o August 4, 2010, at the Techoomy coferece; cf. [3] IDC, i its 2011 Digital Uiverse study; cf. [4] Digital-Uiverse-Studie vo IDC. Das Datevolume i Westeuropa wächst jährlich um 30 Prozet (IDC Digital Uiverse Study. Data Volume i Wester Europe Growig by 30 Percet Aually); cf. (also i Germa) February 22,

9 Likig traditioal retail key idicators with olie key idicators, kow as digital aalytics, for soud multichael decisios. The ogoig optimizatio of key idicators of olie success is udisputedly a importat goal. However, there are two problems associated with cosiderig these idicators such as purchase coversio rates i isolatio. First, doig so sometimes coflicts with 5 the compay's overarchig goal of creatig a 'itegrated multi-chael customer experiece'. Secod, this approach disregards the fact that differet shoppig software systems require differet ad sometimes widely diverget methods of iterpretatio. O the other had, a oe-sided focus o the overall average ca hide the ofte cosiderable effects of idividual products o the purchase coversio rate. All retailers, regardless of whether they operate via catalogs, olie or i stores, must focus ot oly o the ergoomics withi their web offerigs as part of the ext re-lauch, but also defie the fudametals of a cross-chael aalytics strategy. This will esure that the right data is collected ow itself to meet possible future eeds related to aalyzig ad assessig the performace of all relevat chaels. This kid of itegrated cosideratio of customer prefereces is eeded because trasactios do ot always take place olie, although they may be iitiated there. Websites ad apps icreasigly play a importat part i ifluecig purchasig behavior at the physical poit of sale (POS). Without systematic cross-chael data collectio, it is ot possible to make accurate ad strategic multi-chael decisios. After all, customers buy brads, ot chaels a fact that has bee prove by extesive market 6,7 research ad exploited strogly by successful multichael retailers. IT ad marketig are icreasigly realizig complemetary beefits. New commuicatio ad purchasig chaels create may poits of cotact with the customer. The oly way to aalyze ad iterpret these 'customer touch poits' meaigfully across the customer jourey is through Big Data aalysis which requires IT-supported aalytical expertise. The pressure to use IT as a tool is felt most keely i marketig. Accordig to Garter, just to accommodate the eed for data-drive measuremet ad visualizatio of results, CMOs i this field will have higher IT expeditures tha the actual IT departmet of the 8 orgaizatio by as early as Usig Big Data aalysis to measure the effectiveess ad efficiecy of marketig activities helps drivig dow costs, icreases the relevace of the products ad services offered, ad eables focus o more cocrete target groups (through micro-segmetatio ad real-time segmetatio). Data aalysts eed experiece ad expertise draw from marketig, merchadisig, ad other areas with close customer cotact, to establish hypotheses about customer behavior ad to use these hypotheses to desig [5] Ail Rajpal, Kedar Mehta, Abhishek Pagaria, Deepali Malhotra, TCS white paper: TCS-FICCI Massmerize Adaptig to the Multi-chael Customer. A Roadmap for Itegrated Multi-chael Retailig; dowload at Coferece.aspx [6] PwC Global multichael cosumer survey 2012: Demystifyig the olie shopper 10 myths of multichael retailig; dowload at also, E-Commerce Ceter Cologe (ECC Cologe), 2013, fact sheet titled Das Cross-Chael-Verhalte der Kosumete (Cross-Chael Cosumer Behavior) as well as various other publicatios available at [7] Iterview with Reier Heckel (former CEO of redcoo GmbH): Havig a olie shop is dam well the obligatio of ay retailer, available at [8] I Jauary 2012, Garter aalyst Laure McLella predicted i a webiar that there would be a fudametal shift i the buyig ceter over the ext few years, whe marketig maagers make marketig-related IT ivestmets based o their ow budget; see or 9

10 algorithms that offer guidace for actios. For marketig executives, the ew areas of focus backed by data ca provide ehaced credibility eve i areas that traditioally used to rely o data-drive decisio makig ad commuicatio; solid figures are ultimately more persuasive tha the ofte cited 'gut feelig. A customer-cetric approach to IT will gai acceptace i the retail eterprise of the digital future oly if it eriches the compay's existig IT competecies ad abilities or creates ew oes. IT-orieted team members must be both ope to last-miute requiremets ad able to maitai a strog culture of trial ad error. However, this combiatio of abilities has limited compatibility with the characteristics ecessary for a traditioal IT departmet to cotrol busiess-critical backboe systems: zero tolerace for errors, absolute stability, ad predictable but slow scalability cycles. It is importat to ote that a lack of iteral willigess to build ew skills ad abilities i IT will ievitably ecessitate the use of several exteral IT-service providers, ad will lead to may silo-its which are moitored oly by 'their ow' departmets ad which operate outside the cetral corporate IT orgaizatio. Brigig IT ad marketig closer together ca oly succeed through a shared maagemet perspective i both areas: through pilot projects ivolvig joit teams, clearly measurable targets, ad icreasig trust i the pooled competecies of both parties. I additio to creatig such 'social glue' betwee both departmets, the use of iovative approaches i IT, such as agile developmet, ca help achieve ehaced flexibility ad shorter time-to-market. Big Data aalysis solves problems i all departmets. While it is clear that Big Data aalysis ca help ehace the performace of those fuctios with high levels of customer cotact (marketig, sales, ad customer service), it ca beefit other fuctios as well. For istace, there is a obvious beefit for the purchasig departmet. Here, meaigful ad easy-to-hadle product-rage aalyses ca provide clear iformatio o future sales figures ad prefereces across all chaels, to be used i talks with suppliers. Alog with these issues that clearly affect reveue, may other departmets also use Big Data ad aalysis to focus o other goals. Reducig risk ad optimizig processes are mostly relevat for R&D ad the supply chai team respectively. e.g. through better credit scorig or improved merchadise availability or fewer bottleecks i logistics. A worldwide study by TCS revealed the surprisig fidig that it is ot just the traditioal customercetric departmets, but other departmets as well, that ca haress Big Data for their ow beefit. Nearly 1,300 executives from across idustries ad departmets were quizzed, icludig o the beefits 9 they expected from Big Data projects. The result: eve more tha marketig, sales, ad customer service which together accout for early a third of Big Data budgets supply chai ad fiace departmets expect to register high ROI from their ogoig Big Data projects. Logistics professioals wat to see improved moitorig of their trasportatio uits, alog with clear statemets o potetial savigs ad the 'right' stock levels. Fiace departmets hope to idetify fiacial risks earlier ad achieve greater overall budgetig accuracy. [9] TCS, 2013: The Emergig Big Returs o Big Data. A TCS 2013 Global Tred Study, p. 9; dowload at 10

11 Big Data aligs departmets toward customer-cetric issues. The challege faced by most Big Data iitiatives is that may issues ca best be examied usig data from various sources withi the compay ecessitatig the establishig of overarchig teams of data specialists ad those specializig i a particular fuctioal domai. The TCS study showed, however, that may respodets are worried about sharig iformatio across 'orgaizatioal silos'. This poit eeds to be addressed early o through idepedet data specialists, measurable iterim results, ad clear, supportig commuicatio from the heads of corporate divisios. Actio is also eeded to couteract the risk of shadow IT systems ad orgaizatios, as metioed above. Otherwise, these issues ca iterfere with potetial compay-wide beefits arisig from itegrated Big Data aalytics solutios. 11

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13 360 Aalytics - a Data-drive Busiess Model for Retailers To esure that retail compaies leverage the opportuities offered by digital chage, each uit withi the compay eeds to aswer two questios: 1. What data do we eed to reach our goals? 2. What questios or aswers regardig this data should we icorporate ito our strategies ad measures? What we call a 'data-drive busiess model' is based o the covictio shared by all iterested parties that ultimately, every process i formulatig a strategy costitutes some measure of 'data-drive decisio makig'. These key questios geerally require iterdepartmetal sources of data ad the cooperatio of the idividual departmets i defiig hypotheses ad mappig actios through algorithms. Figure 3: 360 aalytics a data-drive busiess model for retailers 13

14 Overview: the 360 Aalytics approach i a retail compay A data-drive busiess model is based o the use of Big Data aalytics with a 360 perspective. Figure 3 illustrates the areas ad topics that stad to beefit the most from ew forms of data aalysis ideed, they must beefit from them if they wat to truly address the opportuities itroduced by digital chage. Key questios regardig the 360 Aalytics approach i retail compaies To esure its success, a umber of key questios eed to be aswered withi the scope of the 360 aalytics approach. From the retail perspective, the most importat of these are: Marketig Which campaigs, media, ad chaels have the greatest effect o visits, purchases, ad customer acquisitio? What criteria ca we use to determie the offers that should be made to visitors i ear real time? Which data ca be collected ad aalyzed with the customer's coset to offer recommedatios for purchase, which are appreciated by visitors? Chaels How should we adjust our processes to commuicate with customers across differet chaels ad provide them with a truly itegrated shoppig experiece? How ca we achieve greater persoalizatio i what we offer to stimulate additioal sales? Ca we provide offers to our potetial buyers while they visit our stores or shortly before, to positively impact their purchase behavior? How should our prices ad offerigs vary by locatio? Which data ca icrease accuracy of expected sales forecasts? Customer service How ca we create a truly cross-chael customer service experiece? Which iteral ad exteral data (isights) work better tha traditioal customer surveys to describe ad predict the behavior of visitors ad customers? How do we make sure our call-ceter employees are iformed i ear real time o the behavior of visitors ad customers? How ca we efficietly aalyze the causes of returs with regard to customers, products, ad occasios ad how ca we reduce future retur rates? Category Maagemet How should our rage ad mix of brads ad private label vary by locatios? How will the price elasticity factors associated with demad chage i the short term, takig writedows ito accout? 14

15 How ca sales figures ad web aalytics be used to arrive at valuable coclusios about i-store placemet ad website desig that are most coducive to sales (for example, through 3D product images, recommedatios, rakigs)? Supply Chai Which data is eeded to reduce stock o had while at the same time lowerig the risk of gaps i availability (lost sales)? How ca we reduce the duratio ad costs of usig our fleet (for example, usig GPS ad route optimizatio)? Which iformatio ad aalysis ca we use to stregthe the buyig maagers' positio? Operatios/Fiace IT How do we determie the right mix of permaet ad o-permaet staff at the POS durig peak hours? How ca productivity be measured across chaels? Which key performace idicators (KPIs) ca we also use for fiacial reportig? How are the roles of IT ad the CIO chagig as a result of ew developmets such as everythig as a service (XaaS), social media listeig, a so-called 'two-speed' IT havig to cope with the eed for stability ad a culture of trial ad error at the same time, ad the eed of a close cooperatio with marketig? What competecies does the 'ew IT' eed? How do we esure adequate availability of 'data scietists?' Where ad how do we situate them withi the orgaizatio? How will we, as the IT team, act if stakeholder groups withi the compay wat to aalyze icreasig volumes of data icreasigly o short otice, icreasigly fast ad o a icreasigly idividual basis? Success factors for trasformatio to a data-drive busiess model Based o customer projects ad the results of the TCS Big Data study, we have laid out ad summarized a umber of pragmatic, 'hads-o' recommedatios for aalytics ad Big Data projects. It is clear that skill i chage maagemet ad program maagemet is eeded, i additio to the competecies already metioed.10 This implies the eed for exteral support to implemet a trasformatio strategy to trasitio to a data-drive busiess model. Nevertheless, most of the success factors set out below (i Figure 4) ca be implemeted idepedetly. [10] Dr. Kay Müller-Joes, i IT-Busiess, May 8, 2013: Die 7 Sorge ud der Erfolg vo Big-Data-Projekte (The 7 Cocers ad the Success of Big Data Projects); available at 15

16 Figure 4: Success factors for the trasformatio to a data-drive busiess model with 360 aalytics 1: Esure cosistet aligmet with busiess targets Uless Big Data projects are aliged with cocrete busiess targets, there is o beefit i pursuig them. Ideally, questios regardig the relevace of data should be idetified based o customers' purchasig criteria. Durig this step, the compay also must determie which types of data are eeded ad available for which decisios. The resultig questios should ot ecessarily be restricted to idividual departmets, but must ivolve iterdepartmetal cooperatio as well. 2: Top maagemet has to commit ad lead the way Comprehesive, lastig support from compay maagemet is crucial to success. This support must be firmly rooted i the project orgaizatio ad commuicated o a ogoig basis. This is the oly way to implemet the overall strategy, secure the ecessary resources, ad pave the way for rapid trasformatio. Commuicatio support from the top esures that people work across departmets, ad that departmet-specific data silos are broke dow or ot allowed to arise i the first place. 16

17 3: Pla ad procure resources carefully The availability of techical ad aalytical resources mostly as persoel must be secured for the log term, especially for the trasitio from pilot operatios to implemetatio. To prevet loss of time, support should also be called i from outside the orgaizatio, ad a culture of trial ad error should be established. 4: Documet ad preset iterim results Iterim results must be delivered early ad at regular itervals, ideally as a prove effect o a cocrete busiess target. This is the oly way to maitai a positive attitude amog steerig committee members, promoters, ad users. 5: Idetify relevat iformatio ad its beefits The iitial prerequisites for a pilot project must be clear to everyoe ivolved. Aswers to the followig questios are especially importat: what iformatio would help us, regardless of kow or ukow sources, to realize greater sales, avert risks, or optimize processes? Ad how, i detail, is it possible usig this iformatio? 6: Make the success of a project measurable How ca the success of the trasformatio be prove? First, there must be agreemet o a measurable outcome so that the project's results ca be compared. The key issue is quatifyig the project's goals ad specifyig exactly how ad whe to take comparative measuremets. 7: Ivolve iterested parties early o ad keep them iformed All the people ivolved i a certai activity must be itegrated ad all the affected processes must be cosidered. For example, POS staff must be iformed of the distributio of locatio-based coupos to specific customers with eough lead time to support the promotio o site. 8: Form a team of specialists The TCS study reveals that compaies that pool their data scietists i a separate uit beefit most from their Big Data projects. The climate of a 'ceter of excellece' promotes rapid implemetatio, developmet of competecies, ad kowledge trasfer. High levels of specializatio also make the job more attractive ad facilitate recruitmet. Ad last but ot least, orgaizatioal idepedece from specific departmets is also crucial whe it comes to successfully aswerig iterdepartmetal questios. 9: Provide results i a clear, decisio-orieted way Results eed to be visualized simply, uderstadably, ad appealigly for example, usig cockpits or dashboards. They should iclude both the most importat KPIs ad clear guidace o what actios to take, layig out ew isights i decisio-ready form for users i the specific departmets. 10: Prioritize strategic parters over ivestig i hardware It ofte makes sese to look at support from a strategic parter for example, to gai the ecessary IT ifrastructure to process ad iterpret large volumes of highly ustructured data at high speeds, without icurrig your ow ivestmet costs. 17

18 11: Ecourage the IT departmet to reivet itself With Big Data, IT departmets have to become icreasigly proactive ad take o ew tasks. They eed ew skills ad abilities ad a uderstadig of ew techologies ad systems. Beyod that, the IT departmet must become aware of its chagig role as coach to specific departmets, which brigs with it a ew set of requiremets for speed ad error tolerace. I other words, CIOs ad IT maagers face the challege of icreasigly aligig IT employees' target systems with exteral customer eeds. 12: Ehace trust ad customer loyalty through data protectio Data-protectio topics eed to be uderstood proactively ad viewed as a opportuity to build lastig trust ad cofidece amog customers for example, by documetig aoymizatio of data before storage ad aalysis, or through ew forms of customer 'opt-i maagemet'. Future or potetial customers should be able to authorize certai persoal data, subject to a time limit ad with clear thematic restrictios. That ehaces trust ad cofidece i the provider while creatig ew opportuities for the provider to exted eve more relevat offers to the customer. Coclusio Looked at critically, most of the questios listed i the data-drive busiess model are ot ew i priciple. After all, the retail busiess has already made extesive use of data aalysis i the past. What makes Big Data aalytics differet is that it offers sigificatly more extesive opportuities for arrivig at cosiderably more meaigful aswers to importat questios. The exteded aalysis ad iterpretatio optios ow available ca trasform Big Data eormous quatities of structured ad ustructured data ito solid kowledge for improved decisio-makig ad clear aswers. For the first time, it is possible to do this promptly, i ear real time. Easy-to-use aalytical tools with extesive visualizatio optios elarge the potetial audiece for data aalyses across all departmets, ulockig the busiess beefits of Big Data aalysis applicatios. The digital chage offers retailers tremedous potetial for better meetig customers' eeds. This opportuity ca be leveraged ad trasformed ito a lastig competitive advatage if the compay iitiates a trasformatio based o a 360 aalytics approach ad moves toward a data-drive busiess model. 18

19 Refereces Tata Cosultacy Services (TCS): Big Data Adoptio A Iterative Approach to Haress the Power of Big Data (2013), accessed Sep 18, 2013, Data Solutios %26amp; Services&type=White Paper Tata Cosultacy Services (TCS): Digitaler Wadel ud mobiler Kude (2012) (The Digital Shift ad Mobile Customers), accessed Sep 18, 2013, techcruch.com, Eric Schmidt, ow Executive Chairma, the CEO of Google, speakig o August 4, 2010, at the Techoomy coferece, accessed at Sep 18, 2013, cio.de, IDC-Studie zum Datewachstum. Doppeltes Datevolume alle zwei Jahre (07/2011), (IDC Study o Data Growth. Double the Data Volume Every Two Years), accessed Sep 18, 2013, IDC, Digital-Uiverse-Studie vo IDC. Das Datevolume i Westeuropa wächst jährlich um 30 Prozet" (02/2013) (IDC, Digital Uiverse Study. Data Volume i Wester Europe Growig by 30 Percet Aually), accessed Sep 18, 2013, Tata Cosultacy Services (TCS) / FICCI, Adaptig to the Multi-chael Customer. A Roadmap for Itegrated Multi-chael Retailig (2012), accessed Sep 18, PwC, Global multichael cosumer survey 2012: Demystifyig the olie shopper - 10 myths of multichael retailig, (2012); accessed Sep. 18, 2013, E- E-Commerce Ceter Cologe (ECC Cologe), Das Cross-Chael-Verhalte der Kosumete, (2013) (Cross-Chael Cosumer Behavior), accessed Sep 18, 2013, chaelparters.de, Iterview with Reier Heckel, former CEO of redcoo GmbH: Havig a olie shop is dam well the obligatio of ay retailer, (Jue 5, 2013), accessed Sep, 18, 2013, garter.com, Webiar aoucemet, By 2017 the CMO Will Sped More o IT Tha the CIO (01/2013), accessed Sep 18, 2013, Tata Cosultacy Services (TCS), The Emergig Big Returs o Big Data. A TCS 2013 Global Tred Study, (2013) accessed Sep. 18, 2013, dataceter-isider.de, Dr. Kay Müller-Joes, TCS, Die 7 Sorge ud der Erfolg vo Big-Data-Projekte (The 7 Cocers ad the Success of Big Data Projects); (2013 ), accessed Sep 18, 2013, 19

20 About TCS' Global Cosultig Practice TCS' Global Cosultig Practice (GCP) is a key compoet i how TCS delivers additioal value to cliets. Usig our collective idustry isight, techology expertise, ad cosultig kow-how, we parter with eterprises worldwide to deliver itegrated ed-to-ed IT eabled busiess trasformatio services. By tappig our worldwide pool of resources - osite, offshore ad ear-shore, our high caliber cosultats leverage solutio accelerators ad practice capabilities, balaced with our kowledge of local market demads, to eable eterprises to effectively meet their busiess goals. GCP spearheads TCS' cosultig capacity with cosultats located i North America, UK, Europe, Asia Pacific, Idia, Ibero-America ad Australia. Cotact For more iformatio about TCS' cosultig services, us at global.cosultig@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburer: About Tata Cosultacy Services (TCS) Tata Cosultacy Services is a IT services, cosultig ad busiess solutios orgaizatio that delivers real results to global busiess, esurig a level of certaity o other firm ca match. TCS offers a cosultig-led, itegrated portfolio of IT ad IT-eabled ifrastructure, egieerig ad TM assurace services. This is delivered through its uique Global Network Delivery Model, recogized as the bechmark of excellece i software developmet. A part of the Tata Group, Idia s largest idustrial coglomerate, TCS has a global footprit ad is listed o the Natioal Stock Exchage ad Bombay Stock Exchage i Idia. For more iformatio, visit us at IT Services Busiess Solutios Cosultig All cotet / iformatio preset here is the exclusive property of Tata Cosultacy Services Limited (TCS). The cotet / iformatio cotaied here is correct at the time of publishig. No material from here may be copied, modified, reproduced, republished, uploaded, trasmitted, posted or distributed i ay form without prior writte permissio from TCS. Uauthorized use of the cotet / iformatio appearig here may violate copyright, trademark ad other applicable laws, ad could result i crimial or civil pealties. Copyright 2014 Tata Cosultacy Services Limited TCS Desig Services I M I 01 I 14