Yetizen presentation on LTV


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Presentation given to Yetizen on LTV: the importance of it, what to measure and how to affect it, and the predictive nature of LTV. Similar to previous presentation at Groundworks Lab with an added section on Uncertainty.

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Yetizen presentation on LTV

  1. 1. H O W L I F E T I M E V A L U E D E T E R M I N E S Y O U RS U C C E S S A N D H O W T O I M P A C T I TLifetime Value
  2. 2. Presentation OverviewRelevant backgroundWhy Lifetime ValueImportance beyond social mediaViralityRetentionMonetizationThe Cost SideLTV varies among customersUncertainty of LTV
  3. 3. F R O M S T A R T - U P T O B I G C ORelevant Background
  4. 4. • Grew to top-5 casual game company• Initiated and negotiated sale to Playdom, which was then rolled into $570 million DisneyacquisitionCo-founded Merscom CCO, led all marketing/sales/distribution• Responsible for Europe, Latin America, Russia and India• Grew it from scratch to 25 percent of Playdom’s revenueGM of Playdom’s International Publishing team• Joint venture of EW Scripps and Capitol Broadcasting• Launched Facebook and mobile gamesCEO of FiveOneNine Games• Lead UA, analytics, monetization and community• Social Casino spaceCurrently Chief Growth Officer at Spooky Cool Labs and Chairman ofGlobalization Committee at NC Central’s School of BusinessBeen there, done that
  5. 5. T H E T H R E E M O S T I M P O R T A N T L E T T E R S F O RY O U R B U S I N E S S : L T VWhy Lifetime Value
  6. 6. Based on three performance metricsMonetizationViralityRetention
  7. 7. Interdepence
  8. 8. Relationship between LTV and CPA• SuccessLTV > CPA• FailureLTV < CPA
  9. 9. Think of LTV starting day 1Green-lightDesign anddevelopfocused onLTVBeta andother testingto optimizeLTVPost launchto focus onimprovingLTV
  10. 10. C R U C I A L T O A N Y B U S I N E S SImportance beyond social media
  11. 11. Online offerings Netflix Ebates Farmville
  12. 12. B2BSquareRackspaceBronto
  13. 13. SaaS Salesforce HootSuite Basecamp
  14. 14. RetailRestaurantsDepartmentstoresCar dealers
  15. 15. W O R D O F M O U T H ’ S E X P O N E N T I A L E F F E C TVirality
  16. 16. Definition of virality K-score K=i*conv% (conversion percentage), where “i” is thenumber of invites sent out by each new customer and“conv%” is the percentage of invites that convert intocostumers
  17. 17. An advanced look
  18. 18. Importance of viralityLowers cost ofcustomeracquisitionExponentialgrowth
  19. 19. Improving ViralityK=i*conv%Increasing I• Generate virality quickly• Cater to ConnectorsIncreasing conv%• Quality of communication• Provide value for virality
  20. 20. C R U C I A L A N D H A R D T O F I XRetention
  21. 21. Definition of retentionCustomerlifetimeN-day retentionChurn rate
  22. 22. Importance of retentionIf they do not comeback, monetizationimprovements arevirtually useless
  23. 23. Improving retention Product quality Get in their heads Make it social Make it global Use email andadvertising for re-engagement
  24. 24. S H O W M E T H E M O N E YMonetization
  25. 25. Definition of monetizationARPU (Averagerevenue per user)ARPDAU(Averagerevenuer perdaily active user)Percentage ofcustomers whomonetizeAveragetransactionAverage numberof monetizationevents percustomer
  26. 26. Importance of monetization
  27. 27. Improving monetizationProductqualityValueMoreselectionBalancingShoppingexperiencePromotionsand sales
  28. 28. D O N O T F O R G E T V A R I A B L E C O S T SThe Cost Side
  29. 29. Cost Drivers• Hosting• SupportRunning Costs• Platform fees• Payment processorsPayment Processing• Engine, such as Epic’s UDK• AnalyticsSoftware Royalties• Properties• TalentIP Licensing
  30. 30. T H E R E I S N O S I N G L E L T VLTV varies among customers
  31. 31. CohortsTime of yearStage of product lifecycleHolidays
  32. 32. SegmentsAgeSexIncomeInterests
  33. 33. SourcesIncentivedadsTargetedsearch adsTelevisionVirality PressCrosspromotion
  34. 34. I T I S A P R E D I C T I O NUncertainty of LTV
  35. 35. Uncertainty PrincipleQuantum Mechanics• The universe is random• Perfect predictions are impossible if the universe is randomNot a function that creates a valueYou are predicting a future eventCreate a range, not a number• Albert Pujols is likely to hit 30-40 home runs is more accurate than Pujols islikey to hit 36 home runsModels are simplifications of the world
  36. 36. Risk vs UncertaintyRiskSomething you can put aprice onUncertaintyRisk that is hard tomeasureDo notconfuseuncertaintyfor riskCorrelation of past datadoes not create certainty
  37. 37. The major difference between a thing that might go wrong and a thing that cannot possibly go wrong isthat when a thing that cannot possibly go wrong goes wrong it usually turns out to be impossible to get ator repair,” wrote Douglas Adams in The Hitchhiker’s Guide to the Galaxy series.Wrong assumptions can have profound effects• Independence of variables• Mortgage industryChaos Theory• Not a synonym for the game industry• A small change in initial conditions can produce a largeand unexpected divergence in outcomes• Major risk when modeling against past performance
  38. 38. Do not discount qualitative informationMore data is better than lessThis includes non-quantitive measuresBilly Beane has dramatically increased scoutingThe Smell Test
  39. 39. Avoid OverfittingMistaking noise for signalFitting a statistical model tomatch past observationsTest how much of thevariability of the data isaccounted for by your model
  40. 40. Solutions• Think probabilistically• Distribution shows honest uncertaintyCreate LTV range• Can validate assumptionsA/B Test• Regularly (weekly or monthly) compare data with predictions• “When the facts change, I change my mind”, John Maynard KeynesSurveillanceAvoid OverfittingInclude qualitative data
  41. 41. L L O Y D @ V E R U S E N T E R T A I N M E N T G R O U P . C O MW W W . L L O Y D M E L N I C K . C O M@ L L O Y D M E L N I C KThank you