Big Data How and How Big? How manufacturers and brands learn to handle data sensibly and generate customer insights

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1 May 2015 Bild: Rawpixel - Ftlia.cm Big Data Hw and Hw Big? Hw manufacturers and brands learn t handle data sensibly and generate custmer insights The everyday business f managers is characterized by dealing with cmplex challenges. As a decisin maker yu have t cnstantly register new trends in digital cmmerce and, if they are relevant, translate them t yur existing business strategy. Apart frm trends which are de fact relevant fr yur business, there is a large number f fast mving hypes withut a lng-term added value. Big data is a trend tpic that has t be assessed and evaluated individually. While the term recvers frm initial euphria and has slwly been integrated int the lexicn f glbal players, many market participants have nt yet identified and assessed this tpic fr themselves. First attempts t grasp this hype n an peratinal level ften fail because f the cmplexity f the tpic. Fr a viable apprach t this theme, we want t narrw the subject dwn with a fcus n fur crnerstnes: big data, e-cmmerce, CRM, and manufacturers/brands. Licensed under a Creative Cmmn License BY-NC 3.0 Seite 1/5

2 As a manufacturer r brand, why shuld I deal with big data? Suppsedly immediate need fr actin and majr prjects with multi-millin dllar budgets are still a vivid memry f thse wh started ut with the first CRM ten years ag. Waste f mney and dubius results were the utcme f these first initiatives. In the curse f the last few years, hwever, CRM has prven itself t be an essential instrument in rder t shape custmer relatins. Althugh, the challenges f big data are multitudinus, they can partly be cmpared t the intrductin f CRM f ld: There is nly a vague cncept f the relevance, the use, the csts and the implementatin f big data. It is crucial t deal with this tpic early, even befre it reaches a level f cmplexity that makes a cnfident intrductin as gd as impssible. In rder t apprach the tpic nw withut rash actins and get t knw the essentials can be regarded as an imprtant step t the evlutin r even the revlutin f business mdels. All prfit-riented businesses are in an incessant fight with the cmpetitin fr the custmers favr. Hence, a lng-term ptimizatin f custmer relatins is ne f the mst prminent aims t secure cmpetitiveness. Emplyed in the right manner, big data can supprt yu in making prgress n yur way t a better understanding f yur custmer requirements. In rder t tackle this tpic, it is necessary t take existing CRM cmpetences as a basis. What is big data actually? The meaning f big data is very brad. Fr sme, it is a new technlgy, fr thers it is a transfrmatin prject in a business r even the start f a new digital era. Fr a clear understanding it is crucial t advance n big data frm ne s wn specific vantage pint. Individual challenges must be perceived, analyzed and understd t derive the right actin ptins fr yur business. On the ne hand, big data is the extensin f existing technical pssibilities fr the analysis f data, n the ther hand it is a ttally new frm f analysis. Accrding t the definitin by Gartner, analyses with big data-technlgy can be identified by their three Vs: vlume, variety and velcity. Big data means the pssibility f a very fast analysis (velcity) f very large amunts f data (vlume) with different structures (variety). In practice, these three parameters are ften extended by the furth V: validity. Because cllecting data that may be relevant n a randm basis is nt prductive. Wrking with a big amunt f data is always preceded with the analysis that these are the right, the valid nes t achieve a certain gal. T secure this prepsitin, we g thrugh a structured prcess with ur custmers, alng three cre questin: Which data is relevant fr me? Why exactly these pieces? Hw d I get these data? The answers are always individual. It depends n what yu want t accmplish with this analysis. What can I accmplish with big data? Analyses f big data can equip yu with ttally new insights. Take a lk at yur business cntext: Which fields d yu as a manufacturer want t fcus n with yur analysis? The mst cmmn cre fields are custmer and prduct. The custmer has different requirements alng the custmer jurney which yu are bliged t meet in rder t persist n the market. Custmer-riented businesses usually feature cnsiderable well-kept CRM systems, which allw a structured analysis f custmer data. Licensed under a Creative Cmmn License BY-NC 3.0 Seite 2/5

3 A digitally active and custmer-riented business evaluates, amng thers, a number f custmer actins (nline purchase histry, newsletter retrieval, surf and click behavir n the website, etc.) in different channels (such as nline shp, stres, scial media etc.). The amunt f accessible data seems just abut endless. A well-kept CRM data base prvides yu with the pssibilities f first big data analyses, which can shrten the duratin f the analysis significantly. Apart frm the shrter analysis f big amunts f data, a further main pint fr manufacturers, frm a CRM cntext, is the pssibility t prcess unstructured data which can be generated thrugh the Internet f Things and Wearables. Cmpared t cmmn CRM analyses, these analyses als becme mre extensive because f the integratin f new data surces and links. Their results can supprt the ptimizatin f yur custmer jurney as a unique business differentiatr frm the cmpetitin. With functining CRM slutins as a sund basis, yu can meet yur bjectives mre easily and frmulate new bjectives that were nt even pssible t act up t with CRM alne. Hence, big data bradens the view n yur custmers and fsters better CRM. Fig. 1: Placing big data in the cntext f cnventinal CRM systems t generate custmer insights With big data yur standard f custmer insight increases as well. Efficiently used data enables manufacturers and brands t further expand already existing custmer and business mnitring. While big data can be regarded as a sensible sequel f all knwn CRM measures, it is nt advisable t emply these new technlgies in all cases. The use f big data can nly be successful, if nt nly large amunts f data are gathered but are als cnfrnted with structured questins. Get a detailed image f yur situatin and yur bjectives befre thinking abut tapping new data surces and pting fr new technlgies and big data prviders. The ideal starting pint fr manufacturers and brands is a cmpletely integrated CRM and a careful chice f new technlgies and expert knwledge. Licensed under a Creative Cmmn License BY-NC 3.0 Seite 3/5

4 Checklist fr the intrductin f big data At which pints f my value chain can I generate added value thrugh the use f big data fr custmer insights r new prducts? Which pssibilities and limits d my existing CRM systems have and what kind f extensins will be necessary? What des big data cntribute t existing bjectives and which new bjectives will be pssible by the use f big data? D my bjectives and requirements cmply with the technical pssibilities f big data and the level f maturity f its implementatin in my wn business? In a structured way, we nt nly supprt yu in frmulating the right questins fr yur business in yur individual cntext, but als generate adequate answers and a manual fr executin in cperatin with yu. Just get in tuch. Dialg Digital Handeln The questins and appraches described abve are part f results generated in the cntext f the Dialg Digital Handeln 2015/I in cperatin with cmpanies and research. Dialg Digital Handeln is a serial event by the dmc.cc, which deals with current and future tpics f e-cmmerce. The participants vary and cnsist f relevant cmmerce representatives and industry experts frm science and practice. External dialgue partner Harald Eichsteller is prfessr f internatinal media management and dean f the master prgram Electrnic Media. After 20 years f management in industry, cnsulting, agency and the media, Prfessr Eichsteller started teaching at the famus Stuttgart Media University in His experience frm wrking fr Siemens arund the wrld, being head f strategy at RTL Televisin in Clgne as well as strategic and nline CEO at Aral, are mirrred in his lectures at the Media University in Stuttgart and his cnsulting and lecture activities all ver Eurpe. As CEO f the transfer and qualificatin rganizatin f the Stuttgart Media University, he supprts the strategic aim t fcus the university s resurces fr this future market. Further persns respnsible fr distributin and digital cmmerce f well-knwn brand manufacturers were invlved in the appraches described abve. Licensed under a Creative Cmmn License BY-NC 3.0 Seite 4/5

5 Authrs Bartsz Przybylek, management cnsultant at dmc cmmerce cnsultants, develps strategic appraches in cperatin with prducers and retailers t identify and meet their challenges in e-cmmerce. Befre wrking fr dmc.cc, he studied philsphy and, as an entrepreneur, he implemented innvative retail and marketing cncepts fcusing n crss-channel retail. David Bdrgi wrte his master thesis abuth the tpic Big Data in digital direct-t-cnsumer cmmerce in rder t generate custmer insights in cperatin with dmc cmmerce cnsultants. He studied media management and infrmatin technlgy and supprted in the curse f his educatin bth glbal acting pubishers and agencies within the digital transfrmatin f their business mdels. dmc cmmerce cnsultants GmbH is an independent management cnsultancy cmbining hands-n executin experience f the dmc grup since 1995 with expert knwledge and methdical cmpetence. We pave the way fr prducers and retailers twards a strategic market entry r the realignment f their digital cmmerce activities efficiently with relevant decisin strategies and results. Thus we supprt businesses in defining and realigning their business mdels fr digital cmmerce. Sebastian Whlrapp is funder and managing partner f dmc.cc. He leads the business develpment and the cnsulting peratins f the management cnsultancy fcusing exclusively n digital cmmerce. Further publicatins by and with dmc.cc visit Kntakt dmc cmmerce cnsultants GmbH Rmmelstraße Stuttgart Licensed under a Creative Cmmn License BY-NC 3.0 Seite 5/5

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