Connected CRMGenerating insights using connected data
CRMis the overall process of buildingand maintainingprofitable customerrelationshipsby delivering superiorcustomer value and satisfaction.Strategic level:CRM = customerloyalty, retention, and partnering.Operational level:CRM = databasemarketing, data-mining, networkstrategies, and direct marketing.Managing Customers: CRMManaging Customers: CRM
Customer lifetime valueCustomer Equity•Total combined customer lifetime values of all customers.•Measures a firm’s performance, but in a manner that looksto the future.CRM: Customer Equity over TimeCRM: Customer Equity over Time-2,000-1,500-1,000-50005001,0001,5002,000Year 1Year 2Year 3Year 4Year 5Year 6CostProfit
What is CRM?
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New ways to segment and classifyBehaviors, e.g.,Where is she from?What has she bought?How many times has she come?What channels did she use?Why did she leave?What did she think of theproduct/brand?What will attract her in thefuture?
Data and CRMPersonal + Collective Data = Better Consumer Experiences?(Social)
AcquireCustomerLife CycleEnhanceRetainProactive ServiceCross-sell & Up-sellDirect MarketingSales Force AutomationCustomer SupportIntegrated & big data based CRMPartialFunctionalSolutionsCompleteIntegrateSolutionCross-functional process breaking down department walls(e.g., Banks + Wechat)CRM: Putting it togetherKnow Your Customer
Stitch-fix Online CRM:Big data + Surveys + Learning
Failures in CRM% of marketing budgets companies plan to allocate to analyticsover the next three years will increase from 5.8% to 17.3%The effect of analytics on company-wide performance remainsmodest, with an average performance score of 4.1 (out of 7)Data challengeNot integrated, huge amounts, little info, not causalData analyst challenge“does not have the right talent” (3.7/7 Some marketing analysts excel atmath and coding, and some excel at framing issues, developingexplanations, and connecting to business implications(Mela and Moorman, Harvard Business Review, 2018)
Why You Aren’t Getting More fromYour Marketing AIThe real challenges are:oAsking the Right Questions (Mkt Research 101)What problem are we trying to solve?•Telecom company:Rather than asking the AI who was most likely to leave, theyshould have asked who could best be persuaded to stay•Gaming company: Asked the AI how to increase players’ engagement ratherthan how to increase their in-game spendingoFailure to Recognize the Difference Between the Value of Being Rightand the Costs of Being WrongType 1 vs. 2 errors; increase prediction power but miss out on high-margin‘mistakes’•False positives: Identifying customers who actually stay as probable defectors•False negatives: Identifying customers who subsequently leave as unlikelydefectors•Or miss out on high-margin low-prob opportunitiesoFailure to Leverage Granular Predictions in managerial decision making•Some questions are redundant (e.g., pricing in the age of dynamic pricing),•Failure to update ‘questions to ask’ with new data; e.g., instead of pricing, figureout customer lifetime value per keyword per channel)Ascarza, Ross, and Hardie 2021,HBR
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