big data and data-driven marketing in...

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Big Data and Data-Driven Marketing in Brazil Finger, Vítor; Reichelt, Valesca and Capelli, João Escola Superior de Propaganda e Marketing, Universidade Luterana do Brasil, Escola Superior de Propaganda e Marketing

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BigDataandData-DrivenMarketinginBrazil

Finger,Vítor;Reichelt,ValescaandCapelli,João

EscolaSuperiordePropagandaeMarketing,UniversidadeLuteranadoBrasil,EscolaSuperiordePropagandaeMarketing

Introduction•  There’sanincreasinglyandmultifacetedsetofdatathat

growsprogressively(Lohr,2012)•  Wemustfollowadispersiveevolutionofexistingchannels•  Marketingautomationprovidestechnologiesthatoptimize

interactionsacrosschannelsandmonitorcustomerbehavior

Introduction“WhichmarketingstrategiesrelatedtoBigDataarebeingimplementedbyBraziliancompaniesindifferentsectors.”

BigDataAdoption•  TheReal-WorlduseofBigData–  IBMInstituteforBusinessValue&SaïdBusinessSchool•  1144professionals,95countriesand26industries•  BigData:Volume,Variety,VelocityandVeracity•  Fivecommontrendswerefound

BigDataAdoption•  TheReal-WorlduseofBigData

•  Usageofbigdatafocusingonconsumercentricity;•  Demandofanextensibleandscalableinformationmanagementsystemtodevelopbigdata;•  Exploitationofinternaldataforthefirsteffortsinthearea;•  Theneedforgreatanalyticalcapacityinordertoobtaingreaterdatavalue;•  Identificationoffourstagesofbigdataadoption;

BigDataAdoption•  TheReal-WorlduseofBigData–  IdentificationoffourstagesofBigDataadoption:

•  Education(24%ofrespondents)•  Exploration(47%)•  Engagement(22%)•  Execution(6%)

Methods•  Exploratoryresearch•  Qualitative•  Datacollectiontechnique:bibliographical,documentary

researchandsemi-structuredinterviews•  Studyunity:ThreemanagersandfourBigDataspecialists•  Studyofmultiplecases•  Analysistechnique:categoryanalysisandcontentanalysis

Methods

BibliographicalandDocumentaryResearch

Interviews MultipleCases

Analysis

Conclusion

Methods–InterviewsInterviewee Education Position Company Experience Company'sIndustry

G01 BusinessAdministration FounderandCEO Pmweb 20years Data-drivenMarketing

G02 BusinessAdministration SalesPartner Pmweb 8years Data-drivenMarketing

G03 Journalism,withgraduationin

MarketingDigital

ProfessorandCEO FabulosaIdeia,

PUCRSeESPM

10years ContentMarketingand

College/University

E01 BusinessAdministration Professor ESPM 12years College/University

E02 Advertising,withmastersin

Marketing

SolutionArchitect Pmweb 10years Data-drivenMarketing

E03 BusinessAdministration

focusingonInnovationand

Leadership

DataScientist CappraData

Science

3.5years DataAnalysisandits

application

E04 I.T.Analyst,specializedin

DigitalMarketing

FounderandCEO Raizzer 12years DataAnalysis

Methods–MultipleCases

Company3–WineE-Commerce

Company2–E-marketplace

Company1–RetailStore

Company4–AirlineCompany

Results–Interviews•  BigDatastillhascountlessmeanings–  Volume,VarietyandVelocity(E02andE03).–  Performingcomplexanalysesneverseenbefore(E04).–  Abilitytodeeplyanalyzealargeamountofdata,thesebeingtransformedintousefulinformation.

Results–Interviews•  Whatcanbedonewithsuchanimmenseamountofdataworthmorethanthefactthatitisvoluminous.–  Personalizationofuserexperience.–  Predictiveactionsregardingdemandsandbehaviors.

Results–Interviews•  Brazilianreality–  TherearesomebigdataadoptioninBrazil,butthere’snocompanythatfullyexploreit.•  Serasa:usageofpredictivemodelsbasedoncrossingdatabasesandunstructureddata(socialmedia).

•  Bibi:usageofpredictiveanalysistoforecastcustomer’sneeds.

–  Therearethreemainlimitations:•  Maturityofdatacollection.•  Professionalqualification.•  Corporateselfishness.

Results–CasesBeforeturningbigdataintoasingleconceptthatinvolvesabinary“yesorno”responsetoitsadoption,itmustbeunderstoodthatitencompassesanumberofstrategiesandactionsthatmaybeusedinwholeorinpartbycompanies

Results–Cases•  Volume

–  Thedefinitionofwhatconstitutesalargeamountofdatavariesaccordingtotheindustrystudied(Schroecketal.,2012).

–  Allcaseshavevolumesofdatastartingfrommillionsofusers.

Results–Cases•  Variety

–  Thestudiedcompanieshaveasmalladoption,speciallyonunstructureddata.

–  Company2usesdifferentdatasources.–  Company4crossesdatafrombehavioralandtransactionalsources.

Results–Cases•  InternalData

–  TheusageofinternaldataisthefirsteffortinworkingwithBigData(Schroecketal.,2012).

–  Company2analysesusersorigininordertocreatespecificapproachesforeachnationalityandculture.

Results–Cases•  CustomerCentricity

–  Theusageofdatatocustomizeandoptimizeuserexperienceisthemainaspecttobeworkedwithbigdata(G01).

–  Company1understandsthecustomer’slifecyclethroughautomatedprogramsandaccordingtoconsumerinteractions.

Discussion•  Thecharacterizationofbigdatatakesplacebothinthetechnicalaspects

andintherealuseofdata•  Althoughacertainpartofbigdataconceptualizationhasbeencoveredby

thecompanies'actionsintheirdataandcommunicationworks,therearepointsdefinedasessentialbyintervieweesandscholarswhichwerenotmentioned.–  Theuseofsemi-structuredandunstructureddata.–  Theabilitytoforecastfuturetrendsandbehaviors.

Discussion•  Inshort,itispossibletorecognizetheexistenceofstrategies

gearedtowardsbigdata-advanceddataextractionandanalysis-inBraziliancompanies,buttheycannotbeclassifiedasbigdatausers.

Futureresearches•  What’stherealvalueofunstructureddatatodigital

marketing?•  HowcanweusenewtechnologiessuchasIoTindigital

marketing?

Thankyou

“Ithasbecomeappallinglyobviousthatourtechnologyhasexceededourhumanity.”AlbertEinstein