Big Data in Payments Unparalleled Opportunity for Strategic Excellence

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1 A Poit of View Big Data i Paymets Uparalleled Opportuity for Strategic Excellece Small data is goe. Data is just goig to get bigger ad bigger ad bigger, ad people just have to thik differetly about how they maage it - Scott Zucker, Vice Presidet of Busiess services, Family Dollar Stores INC Uited States Orgaizatios are curretly deluged with data from various idepedet sources. Ad, this is just the begiig. Data volume is predicted to grow expoetially. With expoetial growth i trasactio volumes, the data associated with these trasactios ca prove to be a hidde treasure for aalysts to ucover ivaluable customer isights. It is thus a gateway to a massive trasformatio i the world of bakig ad fiacial services. Itroductio Big data is a boo to paymets processers ad global etworks as it gives them access to crucial customer data which, whe properly aalyzed ad customized, ca ope up ew growth aveues. Aalytics ca be performed o Big Data to track spedig patters. This ca assist i creatig customized product ad service lies. There are orgaizatios dedicated to aggregatig, cleasig ad aalyzig data ad sellig the same to iterested parties for customized usage. Big Data is chagig the rules i the world of global iteroperable paymet etworks. A paradigm shift i strategy formatio related to data sciece ad advaced aalytics is a compellig reality ow perhaps the biggest ever, pavig the way for remarkable trasformatio joureys i fiacial services eterprises. With the cost of retrieval, processig ad storage of data gradually reducig, eterprise thought leaders ca adopt a radically differet approach to achieve ever before customer isight, cosidered util ow virtually impossible ad prohibitively expesive. The secret of tappig ew geeratio techologies such as social media etworks, mobility, ad cloud for busiess beefit lies i the successful adoptio of Big Data techologies. Big Data strategy is thus goig to be the sigle most sigificat differetiator for orgaizatios i the years to come, ad whe implemeted, will eable them to move away from disjoied illusio to a itegrated ad expaded horizo. What is Big Data? Is there a busiess case to deploy Big Data i the world of paymets? Big Data is used to describe a eviromet characterized by data explosio. Such data, available i structured ad ustructured format, ca be haressed to derive isights that drive uderstadig of customers, differetiatio i value propositio, eed-based iovatio ad top ad bottom lie growth. Experts attribute the followig characteristic features to Big Data: Volume The amout of data to be hadled is huge, ad is derived from multiple sources be it structured trasactioal data or ustructured data - icludig sources such as social media. Earlier data storage was the mai cause for cocer; but with decreasig storage costs, cocer has ow shifted to determiig the relevace ad derivig value from data. Velocity Refers to the speed of data geeratio, availability ad processig ability. Variety Data sources ca be as diverse as social media ad etworkig websites, CRM software, trasactioal processig systems ad other applicatios that form a part of the techology ifrastructure of a orgaizatio. Itegrity Data comig from multiple sources becomes usable oly whe it is cleaed, validated for itegrity ad correlated to idetify relatioships, hierarchies ad likages. The complexity of this exercise stems from the eed to determie the required mappig to commo

2 defiitios ad brig structured ad ustructured data uder a commo framework. Data icosistecies due to source evirometal variability add to the complexity. Agaist this backdrop, Big Data ca be the gateway to hidde patters ad correlatios that are ot otherwise easily discerible. For istace: A merchat could aalyze data o visitor browsig patters, logi couts, past purchase behavior, ad resposes to promotio campaigs. This ca help zero i o issues that eed focus A vedor ca decide o the eed for ad the quatum of discout to be offered to improve loyalty ad cotrol attritio. Product budlig strategies ca be decided usig appropriate aalytics o the cleased Big Data. The busiess case becomes stroger whe: Complex readig algorithms are deployed to decode semi-structured data followed by a best-i-class strategy to create actioable busiess processes. High performace aalytics are used by implemetig the right techology solutios supported by a robust Big Data eco-system. Adoptig a pragmatic approach of applyig relevat advaced mathematical models ca create may iche areas i Big Data aalytics which, whe itegrated efficietly, ca produce powerful actioable isights. How does it impact the Paymet Card Idustry? Global paymet etworks i the B2B segmet will use Big Data to better uderstad their customers, facilitate card issue ad merchat acquisitio i the cross border iteroperable eviromet, ad expad the etwork of their strategic parters. The idustry is expected to shift gears by movig from simple demographics ad trasactio drive aalytics to icorporatig massive data volumes i aalysis. Big Data will help leverage previously utapped attributes such as time spet, locatio, itet ad merchadise / service prefereces vis-à-vis peer groups. Thus, it will ow be possible to gai valuable isights ito specific areas such as web browsig behavior, the itetio behid ad istaces of abadoig shoppig trasactios midway, impact of olie security, social etwork behavior ad so o. Disruptive iovatios are challegig the traditioal paymet idustry practices. New geeratio players with ew busiess models are threateig to take over market share from traditioal baks ad paymet etworks. Market aalysts aticipate that ew geeratio chaels such as mobile ad social media will fuel paymet trasactios growth. Fiacial istitutios are uable to move forward fearig caibalisatio of reveue due to cosumers shiftig from existig chaels to ew geeratio chaels. Big Data will eable orgaizatios to predict growth by factorig i the caibalizatio effect. Fiacial istitutios will thus be able to differetiate basic customer experiece from premium services ad maage the retur o ivestmet as well as customer attritio. It is essetial to embed aalytics ito a Big Data solutio as it is required to derive value from the data. The success of a Big Data iitiative also depeds o how well ew sources are itegrated with traditioal data for further aalysis to derive previously udiscovered customer isights. A optimal itegratio of structured ad ustructured data leads to powerful syergies. The importace of Big Data i paymets is reiterated by the World Wide Web data i a e-commerce eviromet that shows how ew sources of Iteret data about customers ca help gai a 360 degree view of customers. Figure 1 depicts the differece betwee the scope of Big Data ad traditioal data: 2

3 Traditioal Data Big Data Uiverse Semi Structured 360 degree customer View Tra De sa mo cti gr oa ap hic l Be (e hav h ior So cia ace al d d ata ld ata ) Structured 180 degree customer View Tra De sa mo cti gr oa ap hic l Customer thoughts, itetios, iactio Comprehesive customer populatio data Relatioship drive Sample Surveyed Relatioship drive Sample Surveyed Ustructured Figure 1: Scope of Traditioal Data ad Big Data Relevace of Big Data i Paymet Strategies Big Data has a large role to play i the paymet strategies of fiacial istitutios. Some of them are eumerated here: Big Data techologies ca process volumious data sets by drillig dow to a graular level. As a result, it allows a orgaizatio to explore all their data for isights, rather tha specific data that may appear relevat. It follows that data pertaiig to years of trasactios, paymets or iteractios ca be aalyzed to discover patters that ca be leveraged. Some examples of patters iclude Specific chaels used to source specific merchadise (for example, books ordered through olie stores, electroic gadgets accessories purchased through olie retail stores, ad so o.) High etworth customer segmets trasactig oly through reputed big olie stores for high value retail trasactios but ever trasactig with isecure e-commerce stores (such as websites with 3D secure autheticatio) IP addresses of customers explorig ticket purchases i railway, bus ad airplaes i sequetial order derivig valuable isights o customers optig to fly after explorig railway ad bus routes ad the uderlyig reasos for optig for a particular mode of travel No-traditioal sources of paymets data such as courier ad trasport compaies (deliverig teleshopped merchadise based o cash o delivery) Level 2 ad level 3 merchadise data from selected idustries (such as railways, airlie, trasport, travel ad so o) ca be icluded for aalysis ad strategic decisio makig The social media-mobility syergy has resulted i radical life style chages for may. Customers are icreasigly collaboratig olie, voicig their views, seekig opiios, criticizig actios, challegig rules, idicatig their preferece by votig for their favorites ad sharig their persoal experieces. Paymet services caot be outside the realm of this paradigm shift. 3

4 Big Data aalytics ca help derive isights from behavioral data of customers at the checkout couters of e-commerce retail stores. Some examples of trasactioal ad behavioral data that does ot reach the check out poit i a e-commerce eviromet but offers valuable isights are: Customers visitig a e-commerce store multiple times ad egagig i price comparisos but ot optig to buy Customers registerig for product demos or walkthrough videos for to-be released products. Customers tryig to dowload photos or digital cotet of merchadise Customers optig to choose cash or other alterate mode of paymets istead of credit or debit card ad the uderlyig traits of such customers based o other data sources Customers voicig their views ad opiios about the merchadise ad the mode of paymet o social etworks ad the peer group resposes for such reviews ad opiios Customers' itetio ad popularity of selected merchadise or service. Customers preferred merchadise sourcig chael ad actual chael through which retail purchase is doe ad the reaso for deviatio, if ay. Customers forwardig a product lik to social etwork groups, postig a commet o mode of paymet, reasos for ot buyig it or optig for alterate mode of paymet agaist paymet cards. Customer data ca be aalyzed at speeds ot previously possible or if ecessary, i real time. For example, speed of aalysis ca idetify customer behavior i real time ad eable takig actio at the poit of iteractio, i time to ifluece a customer decisio. Customer isight ca be improved with Big Data capabilities by optimizig offers ad cross sell. Big Data helps create preferetial segmetatio withi a idetified customer segmet. I the curret sceario, valuable customers are ofte treated the same as ay other customer i their segmet. This is because the iformatio that feeds segmetatio is limited sice a very small subset of the overall iformatio ca be harvested to get a view of the customer as a idividual. Moreover, the curret eviromet is witessig a delay i assessig the impact of a chage i customer iformatio. Big Data ad its aalytics help to better uderstad the eeds ad behavior of the customer as a idividual, rather tha just a member of a segmet. Armed with better isight about idividual customer eeds, attitudes ad behavior, offers ca be more targeted ad persoalized, ad are more likely to be accepted. Paymet solutios iitiative i social media etwork has just begu. A best-i-class Big Data strategy will lead to effective implemetatio of social media solutios with the right scope ad scalability to meet medium term priorities. For example, peer to peer moey trasfer is ow available as a service for Twitter accout holders as part of Twitter usage. Some baks are explorig pilot paymet applicatios itegrated with their Facebook page. Rapid progress i ew geeratio techologies has created a atmosphere that actively ecourages iovatio. Although they attempt to address a curret market eed or provide differetiatio, some iovatios do ot justify the ivestmet as they do ot result i growth i busiess or comply with regulatios. As a result, these iovatios are either scalable or secure. Adoptio of a Big Data strategy ca ispire meaigful iovatio ad lead to icreased reveue. New geeratio paymet istitutios are cosciously movig away from iteroperability to epic proprietary ecosystems without factorig the merits of hard-leared best practices from the global paymets idustry. Big Data ca help garer isights o right busiess models, reveue potetial ad safe practices. Eablig Big Data the advatages There is a coscious effort to move away from iteroperability to epic proprietary ecosystems durig iovatio without factorig i the merits of hard-leared best practices from the global paymet idustry. Disruptive iovatio ca scale up ad brig value oly whe it is aliged to the larger paymet idustry ifrastructure. I order for fiacial istitutios to choose the right iovatio for growth, a Big Data strategy is importat as it provides the required isight for decisio makig. By icorporatig Big Data ito their paymet strategy, fiacial istitutios ca re-strategize paymet joureys ad implemet best-i-class paymet programs ad services. 4

5 Cacelled Relatioship - Customer Never to retur Customers Cash oly purchaser Recet Purchaser 100 Frequet purchaser High value purchaser Frequet Visitor but do ot buy 10 0 Not buyig - Customer experiece reasos Iterested but busy Traditioal Demographic ad trasactio data Preferred Chael Purchaser Log term plaig customers Near term focused customers Big data uiverse Preferred Paymet Brad purchaser Preferred Paymet product purchaser Figure 2- The differece betwee the Traditioal Data ad Big Data uiverses Big data will eable the istitutios to idetify profitable customer segmets so that they ca focus their eergies ad ivestmet to achieve their eeds. Big data will eable istitutios to predict cosumer adoptio of ew programs ad techologies that will evetually decide their bottom-lie. Coclusio Istitutios eed to make rapid strides i Big Data techologies to create a differetiated value propositio. Global paymet orgaizatios eed to be aware of customer eeds so that they ca offer customized ad value based digital products ad services. The promise ad potetial of Big Data is true but the degree of success will deped o how well Big Data strategies are executed. A wellexecuted strategy will separate the leaders from the pack. About the author LK Ragaatha LK Ragaatha is a seior cosultat workig with the TCS GCP Bakig team i Idia, specializig i the Paymets Cards domai He focuses o thought leadership drive cosultig offerigs i the areas of EMV Compliace, mobile paymets ad paymet card fraud maagemet. He extesively collaborates with BFS project teams to drive cosultig drive deliverables i dowstream solutio programs. 5

6 About TCS Bakig ad Fiace Solutios Over the past four decades, TCS has partered with multiple cliets i the BFS domai ad has executed a umber of complex ad time-critical assigmets uder challegig busiess ad operatig eviromets. The BFS world sees TCS as a orgaizatio with strog foudatio ad superior uderstadig of the Fiacial Services market. This has paved the way for creatio of uparalleled vertical expertise. Our ed-to-ed offerigs, comprehesive product suite, scalable processes ad iovative frameworks have eabled sigificat strategic value creatio for our cliets by helpig them optimize their IT ivestmets, ehacig operatioal efficiecies, miimizig risk, ad acquire sustaied cost leadership. I the BFS idustry, TCS is raked at No. 2 i the FiTech 100 rakig, ad has bee amog the top 10 compaies for the last five years. With 12 out of top 20 global fiacial istitutios as customers, the clietele iclude big ad small baks, developmet istitutios, regulatory istitutios ad diversified ad specialty fiace istitutios. The BFS ISU works closely with Fiacial Services istitutios across the Thik, Build & Operate space to fulfill their strategic ad tactical objectives. The sub practices iclude Retail Bakig, Commercial/Corporate Bakig, Capital Markets (Ivestmet, Wealth Maagemet ad Securities), Market Ifrastructure, Cards (Credit, Debit ad Loyalty), Risk Maagemet ad Treasury. The TCS risk maagemet framework has bee rated as a leader by the fiacial research firm, Tower Group. Everest Research has also raked TCS as the leader i the bakig applicatio outsourcig space. Cotact For more iformatio about TCS cosultig services, cotact us at bfs.marketig@tcs.com About Tata Cosultacy Services Ltd (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 assurace services. This is delivered through its uique Global Network Delivery ModelTM, 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. 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 2013 Tata Cosultacy Services Limited TCS Desig Services I M I 07 I 13 For more iformatio, visit us at