The big deal behind Big Data marketing

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Mieke is de Customer Intelligence Lead South-West Europe bij SAS, waar ze verantwoordelijk is voor alle “Integrated Marketing Management” projecten. In het verleden heeft zij bij een aantal multinationals (PWC, SAP, Microsoft en Selligent) gewerkt aan alle aspecten van klantrelaties. Ze is gespecialiseerd in het helpen van klanten bij het creëren van klantervaringen, customer intimacy en customer life cycle management. Later ging zij zich focussen op de waarde van Big Data in marketing context. De komst van de datastromen (online & social data) aan de ene kant en de rekenkracht aan de andere kant, levert aan marketeers interessante real-time inzichten die, als ze slim worden gebruikt, de klantervaring en customer experience naar een hoger niveau kunnen brengen. Vanuit haar wetenschappelijke track-record (PhD onderzoeken in Artificial Intelligence en in Advanced Marketing) en praktijk gaat Mieke vertellen hoe Big Data kan worden ingezet om belangrijke management beslissingen te maken.

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The big deal behind Big Data marketing

  1. 1. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.THE BIG DEAL BEHIND BIG DATA MARKETINGMAKING SMARTER CUSTOMER CENTRIC DECISIONS WITH BIG DATAMIEKE DE KETELAERE, SAS, SWE CI LEAD, APRIL 2013
  2. 2. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.ABOUT MYSELF• Customer Intelligence Lead at SAS for the SWE region• Worked for several multinationals on CRM• Transactional CRM• Analytical Customer Insight• Interactive Marketing• Marketing Accountability• Main focus on Telco, Retail, Finance and Utilities• Development of roadmaps towards customer centricity• Strong interest in Digital Intelligence
  3. 3. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.JANUARY 2007
  4. 4. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CHALLENGE 1
  5. 5. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CHALLENGE 2On top of the mind of most CMOs• Trusted Brand• Customer Experience• Customer Intimacy• Relevant InteractionsCMOCFOOn top of the mind of every CXO:• Revenu• Cost• Margin
  6. 6. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.ObjectivesValue/ResultsData ManagementPeople and ProcessesDATA DRIVEN CUSTOMER CENTRIC MARKETING
  7. 7. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATA DRIVEN CUSTOMER CENTRIC MARKETINGDATA TYPEDATASOURCEInternalExternalStructured Unstructured
  8. 8. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATA DRIVEN CUSTOMER CENTRIC MARKETINGDATA SIZEBIG ANALYTICSBI BIG DATA BIBIG DATAANALYTICSANALYTICALCAPABILITYReactiveProactiveLarge Big Data
  9. 9. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.AnalyticalSocialWho TheyAreWhat TheyReceiveHow TheyRespondHow TheyInteractWhatThey SayWhoTheyInfluenceTheirLoyaltyTheir ValueTheirPotential360 CustomerInsightContactHistorySocio-demographic& transactional infoOnlineBehaviouralCUSTOMER DNABIG DATA INSIGHTS
  10. 10. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.EXISTING INTERNAL STRUCTURED DATABIG DATA INSIGHTS Who TheyAreCreditcard/Payment Transactions Loyalty Card Information
  11. 11. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.Referring domainCampaign IDAffiliateNatural searchSearch keywordsDirect/bookmarkReferrer VariablesCustomer/prospectNew/return visitorAll click dataTools usagePrevious Product interestsSearchesPrevious online purchasesPrevious Campaign exposurePrevious Campaign responsesSite Behaviour VariablesIP addressCountry of originTime zoneOperating systemBrowser typeMobile typeEnvironment VariablesTemporal VariablesTime of dayDay of weekRecencyFrequencyLength of visitEmail interactionsSocial Media interactionsChannel VariablesNEW (UN)STRUCTURED ONLINE DATABIG DATA INSIGHTS How TheyInteract
  12. 12. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.OPsychographic/IAO (Interest/Activities/Opinions) variablesAttributes relating to personality, values, attitudes, interests, or lifestyles.Explicit data (directly given by user)–Movies/Books–SportsImplicit data (indirectly given by user)–Context- Relationship analysisContent Categorisation/Context ExtractionSentiment Analysis Network Analysis Mood DetectionBIG DATA INSIGHTS NEW (UN)STRUCTURED SOCIAL DATAWhoTheyInfluenceWhatThey Say
  13. 13. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATA INSIGHTS NEW DATASOURCES – NEW IDEASRFID Tags Sensors
  14. 14. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.Response ModelingCustomer Lifetime ValueMarket Basket AnalysisCross and Up SellingWeb MiningCustomer Link AnalyticsChurn PredictionCredit ScoringSocial Media AnalyticsCustomer SegmentationFraud DetectionLocation AnalysisKPI ForecastingMarketing OptimizationMarketing Mix AnalysisOVERVIEW CUSTOMER ANALYTICSBIG DATAANALYTICS
  15. 15. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.?THE MARKETING CHALLENGE: CUSTOMER ANALYTICSBIG DATAANALYTICS
  16. 16. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CASE RETAIL – NEW INSIGHTSBIG DATAANALYTICSQUESTIONS• Do we understand how our customers decisionmaking process is changing?• How well are our merchant strategies alignedwith our customer expectations and corporateobjectives?• Are our customer insights leveraged acrossour organization?• Who are my most profitable customers?• What drives purchasing behavior?• What messages or promotions have mycustomers responded to?• What can I influence my customers to buy inthe future?
  17. 17. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CASE RETAIL – NEW INSIGHTSBIG DATAANALYTICS
  18. 18. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CASE RETAIL – MARKET BASKET ANALYSISBIG DATAANALYTICS
  19. 19. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATAANALYTICSCASE TELCO – LIFESTYLE ANALYSIS ON IPDRQUESTIONS• Explore consumer web surfing behavior in IP data records (IPDR)• IPDR describes who (IP), what (URL), when (timestamp) of web surfingbehavior• Categorize web surfing behavior via a deep rooted taxonomy• E.g. Top/Entertainment/Video Games/Music;• Based on standards in International Press Telecommunications Council• Visualize the results over various dimensions to discover onlinelifestyles of consumers• Visualize interests against customer segments/ profile, events, location, etc
  20. 20. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATAANALYTICSCASE TELCO – LIFESTYLE ANALYSIS
  21. 21. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATAANALYTICS• Consumers• Being able to monitor their energy usage• Cost saving• CO2 reduction• Remote access• Suppliers• Avoided meter reading/ site visit• Price optimisation• Debt handling• Fraud detection• General• Reduced losses• Micro-generation technologiesCASE UTILITIES – SMART METER READING
  22. 22. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.CASE UTILITIES – SMART METER READINGBIG DATAANALYTICS
  23. 23. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATAANALYTICSCASE BANKING – ONLINE CUSTOMER LEAD MANAGEMENTBadLeadsLeadsfornurturingSalesLeadsCall centerLeadsMarketingClosed SalesQualifiedLeadsSales Ready LeadsConversionsInquires/SuspectsNurtureTouchpoints: Email, Online, Contacts, etcSales
  24. 24. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.BIG DATAANALYTICSCASE BANKING – ONLINE CUSTOMER LEAD MANAGEMENTWhen is my customers online andwhat is he doing?How is the evolution of interest inproducts?Where is my customers located andwhat is his behaviour?What is the relation between certainonline variables?
  25. 25. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.WHITEPAPERS: BIG DATA @ MARKETINGNEXT STEPS
  26. 26. Copyr ight © 2012, SAS Institute Inc. All rights reser ved.THANK YOU!MIEKE.DE.KETELAERE@SAS.COM

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