Predictive analytics the customer lifecycle in a btb company

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Predictive analytics the customer lifecycle in a btb company

  1. 1. Welcome
  2. 2. Manutan -Program16.20 – Welcome by Marc Perin, Safeshops16.30 – Introduction Overtoom - Manutan16.35 – Predictive modeling & the customer lifecycle17.15 – Questions17.30 – NetworkingSafeshops Manutan Belgium 20132
  3. 3. Predictive analytics & the customerlifecycle in a BTB company29 April 2013
  4. 4. Manutan -Who is Overtoom & Manutan?
  5. 5. Manutan - Safeshops Manutan Belgium 20135OvertoomWho are Overtoom & Manutan?Manutan1947 Founded by Rein Sjenitzer19741995199720141947 Founded by Rein Sjenitzer1974 Start Belgian Branch1995 Acquisition by Manutan1997 Launch of the Overtoom web shop2014 Manutan-Overtoom 1 Belgian Brand1966 Founded by André Guichard1973 Start European expansion / BelgianBranch1985 Introduction of Manutan SA on EuronextMarket2011 Move to the Overtoom-Building in Ternat2014 Manutan-Overtoom 1 Belgian Brand
  6. 6. Manutan - Safeshops Manutan Belgium 20136Who is Overtoom & Manutan?One big European FamilyOur presence in 19 European countries through 23 affiliates reflects our desire to spread ourcompany vision throughout the world. We are keen to share our principles with the widest possibleaudience across all borders, wherever and whatever they may be.
  7. 7. Manutan -Predictive Analytics at Overtoom02
  8. 8. Manutan - Safeshops Manutan Belgium 20138Analytics & the customer lifecycleProspect NewCustomerActiveCustomerInactiveCustomerSuspect CustomerAt RiskAcquisitionCustomizedOffersSegmenting& TargetingProfit / LTValueChurnPreventionReactivationSegmenting& TargetingCustomizedOffersCustomizedOffersLoyaltyReactivationProspectconversionSuspectPurchase
  9. 9. Manutan - Safeshops Manutan Belgium 20139Segmenting & targetingCustomer InformationOrdersFirmo-graphicsComplaintsProductsSurveysActionHistoryID NaceCodeCompanySizeProductPossessionPurchaseVariety001 2651 1 0 2002 4020 5 4 3003 4544 2 2 1004 7412 1 1 7
  10. 10. Manutan - Safeshops Manutan Belgium 201310Segmentation vs. PredictionPredictioCustomer Base ActionPredictioSegmentationWho to select ?Prediction
  11. 11. Manutan - Safeshops Manutan Belgium 201311Segmentation vs. PredictionCumulativeTurnoverCustomerRankingField test 2008PredictionSegmentationbestcustomersworstcustomersThe predictive model shows an increase in turnover of more than 10% compared to thetraditional segmentation (RFM) schemeForrester Research: There is a causal effect of personalization on loyalty, customer satisfactionand customer retention (Feb 17,2012; Whitepaper: Use customer analytics to get personal)
  12. 12. Manutan - Safeshops Manutan Belgium 201312Segmentation vs. PredictionOther traditional segmentation used in our industry is not always helpful
  13. 13. Manutan - Safeshops Manutan Belgium 201313Analytics & the customer lifecycleProspect NewCustomerActiveCustomerInactiveCustomerSuspect CustomerAt RiskAcquisitionCustomizedOffersSegmenting& TargetingProfit / LTValueChurnPreventionReactivationSegmenting& TargetingCustomizedOffersCustomizedOffersLoyaltyReactivationProspectconversionSuspectPurchase
  14. 14. Manutan - Safeshops Manutan Belgium 201314Customized offersDefine content for each individual... Which products to offer?Prediction40.000Products
  15. 15. Manutan - Safeshops Manutan Belgium 201315Customized offersPersonalization is a win-win initiative:Customer benefits from better recognition and gets relevant offers & experiencesOrganizations benefit from increased customer retention & realize significant returnsForrester Research, Feb 17,2012; Whitepaper: Use customer analytics to get personal)
  16. 16. Manutan - Safeshops Manutan Belgium 201316Customized offersCustomized Offers are 300% more relevantAverage response after 1 monthOffer 1: most relevant product based on predictions1 2 3 4 5 6 7 8 9 10 11 12 13 14VitrineFolderBaselineConversionOffer rankingResponse most relevant product is 300% higher than average response
  17. 17. Manutan - Safeshops Manutan Belgium 201317Analytics & the customer lifecycleProspect NewCustomerActiveCustomerInactiveCustomerSuspect CustomerAt RiskAcquisitionCustomizedOffersSegmenting& TargetingProfit / LTValueChurnPreventionReactivationSegmenting& TargetingCustomizedOffersCustomizedOffersLoyaltyReactivationProspectconversionSuspectPurchase
  18. 18. Manutan - Safeshops Manutan Belgium 201318Other Examples: Net impact modelingResponse modeling: Traditional ApproachLimitations: Neglects impact of campaignsReact if not treatedby marketing campaign?YES NOReact iftreated bymarketingcampaign?YESNOActionNo ActionLost-Net impact modelingTest : # of customers have not received a catalog in 2012, to find out whichcustomer groups are not influenced in their purchase by a catalog (i.e. what isthe actual impact of our catalog on customer’s purchasing behavior)Outcome: sending out considerably less catalogs in 2013 (saving $$$)
  19. 19. Manutan - Safeshops Manutan Belgium 201319Other Examples: Mailing plan on economically justified costsCalculation of customer lifetime valueOutcome: know how much we can spend marketing on each customerExpected profit/Contact = Probability * (TO * Margin) – Action costTopScoresMediumScoresLowScoresBadScores
  20. 20. Manutan - Safeshops Manutan Belgium 201320Suspect purchase• New suspects we purchase from Graydon (based on Twins)• Outcome: reduction in cost with Graydon + reduction in mailing costChurn = customers at risk• Look at maximum inter-purchase time• Outcome: clean d-baseSet up of customer lifecycle action plan• E.g. new customer, different automated actions we take: call after week 1, letter & emailafter x months if not repurchased; with personalized offers for new customers.• Outcome: better customer knowledge + increased customer durationOther examples
  21. 21. Manutan -Questions?03

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