This is a draft of the cross-business unit IBM Customer Analytics strategy that I led post Tealeaf's acquisition by IBM. This strategy included all of IBM's analytics assets at the time, Tealeaf, Coremetrics, Cognos, etc.
© 2014 IBM CorporationPredictive Customer Intelligence• Future Buying Insight• Anticipate and delightcustomers with solutions• Buying propensities andpatterns• Product preferences• Related productsDigital Analytics• Quantitative Insight• Web site and mobile trafficdata• Customers in purchasefunnel• Completed transactions• Conversion metricsOptimizing Customer Experience Requires Combining Digital,Behavioral, Sentimental & Predictive Analytics1Customer Behavior Analytics• Qualitative Insight• Surface Customer struggleon digital channels• Session replays tounderstand actual customerjourney• Identify impacted customersSentiment Analytics• Soclal Media Insight• Prevailing sentiments• Relevant relationships• Potential risks
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© 2014 IBM CorporationCustomer Analytics For The EnterpriseUnderstanding The Context of All Customer Interactions Has ProfoundBusiness Impacts§ Increased Revenue– Better Conversion/Adoption– Higher Lifetime Customer Value– Increased Share of Wallet– More Repeat Customers/Buyers§ Higher Customer Satisfaction– Reduced Customer Struggle & Friction– Better Net Promoter Scores– Reduced Churn; Higher Customer Loyalty– Understanding Social Commentary &Sentiment§ More Effective Customer Acquisition– Better Targeting Based on Intents/Interests§ Reduced Operational Costs– Higher Call Avoidance/Deflection– Faster Site Maintenance, ProblemResolution2CustomerWWW
© 2014 IBM CorporationCA Use Case 1Customer Acquisition - RetailMarch 2014INTERNAL USE ONLY – Do not Distribute
© 2014 IBM CorporationStep 1 - Analysis of Keywords data with Digital AnalyticsPPC and SEO keywordsSite search keywords
© 2014 IBM CorporationStep 2 – Develop list of sites where the potential customershangout with SMA using DA keywords listIBM DigitalAnalyticsKeywords dataIBM SMAIdentify potentialcustomersList of websites
© 2014 IBM CorporationStep 3 – Targeting the websites with targeted display adsList of websitesDisplay adtargeting/retargeting
© 2014 IBM CorporationStep 4 – Measuring Attribution Digital Analytics
© 2014 IBM CorporationStep 4 – Measuring Attribution xxxx
© 2014 IBM CorporationStep 4 – PCI Optimizes Attribution9WWWWWWWWWPCIIBM DigitalAnalyticsIBM SMAAttributionMarketingCenter
© 2014 IBM CorporationQuestions?10
© 2014 IBM CorporationCA Use Case 2Customer Retention - TelcoMarch 2014INTERNAL USE ONLY – Do not Distribute
© 2014 IBM CorporationStep 1 – Building complete customer profile12Repeat customerclicks on a linkfrom a trustedbrand tweet•User lands on a TL and DA monitored site• DA matches cookie data to session ID•TL session ID synched with DA session ID•TL captures all behavioral data• Customer opted in with social media IDsEnvironmental data
© 2014 IBM CorporationStep 2 – Understand Customer Behaviors13Browsing and addingproducts to shopping cartTL captures customer behaviorsRemove an item Check out
© 2014 IBM CorporationStep 3 – Understand Customer Profile14Browsing and addingproducts to shopping cartDA captures customer profileCheck out
© 2014 IBM CorporationStep 4 – Understand User Sentiments15SMA captures customer sentiment:brands, preferences, and challengesLove the newAndroid phonefrom TelcoEspecially thegold oneBut will haveto wait forsales…L
© 2014 IBM CorporationStep 5 – Complete Insights16TL, DA, and SMA provides complete insightsBehaviors SentimentsProfiles
© 2014 IBM CorporationStep 6 – PCI improves Meaningful Engagement17BehaviorsPCISentimentsProfilesXtify10% off on GoldAndroid phone fromTelair10% off on Gold Androidphone from Telair
© 2014 IBM CorporationCA Use Case 3Reduce Churn – Financial ServicesMarch 2014INTERNAL USE ONLY – Do not Distribute
© 2014 IBM CorporationStep 1 – Capturing All Interactions19Customer clickson a link from anemail from herbankBank site deploys IBM CA that capturesboth Behavior and Profile dataEnvironmental dataNarrative:•Customer receives an email offer for a low rate for refinancing home loan from herbank• She is interested and clicks on the link from her smartphone to find out moreinformation•Bank site is monitored by IBM CA that includes both Behavior and Profile data• All interactions are being captured by IBM CA (behaviors and profiles)•Being an existing customer, she as opted in with contact information includingsocial media IDs
© 2014 IBM CorporationStep 2 – Understand Customer Behaviors and Profiles20Downloadednative appIBM CA captures customer behavior and lifecycle dataStarted the refinancingapplication processStopped usingthe appXCalled ContactCenterNarrative:•She saw the call to action to down load the newly available mobile app. Sheproceed with the download and installation process.•She logs in using her username and password• She proceeds to start the quick quote option in the app•At the enter the house address field she keeps receiving an error messaging:invalid address•After several tries she called the contact center. She abandoned the process
© 2014 IBM CorporationStep 3 – Understand User Sentiments21IBM CA captures customer sentiment:brands, preferences, and challengesLooking into newrefi rates fromFinance1Especially the30 fixed rateBut their appsucks…LNarrative:•She tweeted her friends about her experience.•With the opted in social media IDs IBM CA can monitor for customer sentiment
© 2014 IBM CorporationStep 4 – Complete Insight22IBM CA provides complete insightBehaviors SentimentsProfilesNarrative:•Combining Behavior, Profile, and Sentiment data IBM CA provides completeinsight into customer interactions.
© 2014 IBM CorporationStep 5 – PCI Identifies Potential Churn Segments23Behaviors SentimentsProfilesPCINarrative:•PCI provides deep insight analytics leveragingBehavior, Sentiment, and Profile data•Discovers that home loan is the key differencebetween churned and loyal customers•Need to improve the refinancing product andprocesses
© 2014 IBM CorporationStep 6 – IBM CA Uncovers Challenges24Home Loan is keyloyalty metricsPCIBehaviors•Mobile appaddress errorstruggles•WebsitestrugglesProfiles•Churnedsegment profiles•Lifecycle dataSentiment•Mobile Appissues•Quick quote didnot work on site•Rates are goodNarrative:•Sentiment uncovers 3 themes aroundmortgage:•Mobile app sucks•Website quick quote did not work•Rates are competitive•With data from Sentiment, Profile provideskey segments on abandoned mortgagenew/refinancing processes•Leveraging segments from Profile,Behavior analyzed specific interaction andfound•Mobile app has non-descriptive error•Error was caused by “-” (programlogics)•Quick quote from website has thesame issue•Need to improve the refinancing productand processes
© 2014 IBM CorporationStep 7 – Actionable insight25Home Loankey metricsPCIBehaviors•Mobile appaddress errorstruggles•Website strugglesProfiles•Churnedsegment profile•Lifecycle dataIT•Fixed “-”program logicsSentiment•Mobile Appissues•Quick quote didnot work•Rates are goodSocial Media•Check out our new app•Same great ratesReal time interactions•Check out our new app•Same great ratesWWW