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.

Transcript of Yetizen presentation on LTV

  • 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. Presentation OverviewRelevant backgroundWhy Lifetime ValueImportance beyond social mediaViralityRetentionMonetizationThe Cost SideLTV varies among customersUncertainty of LTV 3. F R O M S T A R T - U P T O B I G C ORelevant Background 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 Playdoms revenueGM of Playdoms 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 Centrals School of BusinessBeen there, done that 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. Based on three performance metricsMonetizationViralityRetention 7. Interdepence 8. Relationship between LTV and CPA SuccessLTV > CPA FailureLTV < CPA 9. Think of LTV starting day 1Green-lightDesign anddevelopfocused onLTVBeta andother testingto optimizeLTVPost launchto focus onimprovingLTV 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. Online offerings Netflix Ebates Farmville 12. B2BSquareRackspaceBronto 13. SaaS Salesforce HootSuite Basecamp 14. RetailRestaurantsDepartmentstoresCar dealers 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. Definition of virality K-score K=i*conv% (conversion percentage), where i is thenumber of invites sent out by each new customer andconv% is the percentage of invites that convert intocostumers 17. An advanced look 18. Importance of viralityLowers cost ofcustomeracquisitionExponentialgrowth 19. Improving ViralityK=i*conv%Increasing I Generate virality quickly Cater to ConnectorsIncreasing conv% Quality of communication Provide value for virality 20. C R U C I A L A N D H A R D T O F I XRetention 21. Definition of retentionCustomerlifetimeN-day retentionChurn rate 22. Importance of retentionIf they do not comeback, monetizationimprovements arevirtually useless 23. Improving retention Product quality Get in their heads Make it social Make it global Use email andadvertising for re-engagement 24. S H O W M E T H E M O N E YMonetization 25. Definition of monetizationARPU (Averagerevenue per user)ARPDAU(Averagerevenuer perdaily active user)Percentage ofcustomers whomonetizeAveragetransactionAverage numberof monetizationevents percustomer 26. Importance of monetization 27. Improving monetizationProductqualityValueMoreselectionBalancingShoppingexperiencePromotionsand sales 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. Cost Drivers Hosting SupportRunning Costs Platform fees Payment processorsPayment Processing Engine, such as Epics UDK AnalyticsSoftware Royalties Properties TalentIP Licensing 30. T H E R E I S N O S I N G L E L T VLTV varies among customers 31. CohortsTime of yearStage of product lifecycleHolidays 32. SegmentsAgeSexIncomeInterests 33. SourcesIncentivedadsTargetedsearch adsTelevisionVirality PressCrosspromotion 34. I T I S A P R E D I C T I O NUncertainty of LTV 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. Risk vs UncertaintyRiskSomething you can put aprice onUncertaintyRisk that is hard tomeasureDo notconfuseuncertaintyfor riskCorrelation of past datadoes not create certainty 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 Hitchhikers 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. Do not discount qualitative informationMore data is better than lessThis includes non-quantitive measuresBilly Beane has dramatically increased scoutingThe Smell Test 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. 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. 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 [email protected] L L O Y D M E L N I C KThank you