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1Dataiku5/22/2013
5/22/2013Dataiku 2CollocationBig AppleBig MamaBig DataGames AnalyticsCurrent Life:CEO, DataikuTweet about this@dataiku@cap...
The Stakes - Summary5/22/2013Dataiku 3Million EventsBillion $Billion EventsMillion $Classic BusinessSocial Gaming
Meet Hal Alowne5/22/2013Dataiku 4Big Guys• 100M$+ Revenue• 10M+ games• 10+ Data ScientistHal AlowneBI ManagerDim’s Private...
MERIT = TIME + ROI5/22/2013Dataiku 5TargetedNewsletterFor New ComersFacebookCampaignOptimizationAdapted Product/ Promotion...
Our Goal5/22/2013Dataiku 6It’s utterly complex andunreasonable
Our Goal5/22/2013Dataiku 7It’s utterly complex andunreasonableOur Goal:Change his perspectiveon data science projects(sorr...
 Do the Basics Understand Analytics What to expect out of analyticsQuick Agenda5/22/2013Dataiku 8
5/22/2013Dataiku 9
 Do you track ?◦ Customer Goals Formost importantfeatures◦ Time SpentLevel ProgresisonMoney Spent◦ Campaigns andgenerated...
 Do A/B Tests◦ Use Proven Solutions◦ Start small (button sizeand color)◦ Check Impacts◦ Treat new and existingusers diffe...
 Register Now / GiveEmail Graphics:From 25% to 2X MoreClickshttp://bit.ly/VOruXt Changing buttonfrom green to red:Up to ...
Statistical Signifiance5/22/2013Dataiku 13http://visualwebsiteoptimizer.com/ab-split-significance-calculator/
 Can be Built on topof your productionsystems Do you have◦ Cohorts◦ Daily $$ Reports◦ Basic $$ Segments5/22/2013Dataiku ...
 Defined Customer Segments◦ New Installs◦ Engaged Users◦ Engaged Paying Users◦ …? Defined Customer Sources◦ Social Ads /...
Embodiment of Knowledge5/22/2013Dataiku 16
 Product Successdriven by Quality Margin / CustomerValue / Traffic /Acquisition5/22/2013Dataiku 17At the Beginning
 Margin for newcustomers mightdecline … Margin for newfeatures mightdecline … Is your businessreally scalable ?5/22/201...
 Existing Customers Existing Product Assets Existing SpecificBusiness Model And your KNOWLEDGEof it5/22/2013Dataiku 19...
5/22/2013Dataiku 20Data Driven BusinessWhat your value ?Number ofCustomersCustomer KnowledgeIncrease over time with:- Time...
5/22/2013Dataiku 21To Apply It ?Product OptimizationCustomer AcquisitionOptimizationRecommender/Targeting fornewsletters
 Dark Side◦ Technology Bright Side◦ Business5/22/2013Dataiku 22Apply It !!
The Dark Side5/22/2013Dataiku 23
Technology is complex5/22/2013Dataiku 24HadoopCephSphereCassandraSparkScikit-LearnMahoutWEKAMLBaseRapidMinerPandaD3Crossfi...
Machine learning is complex5/22/2013Dataiku 25 Find People that understand machine learningand all this stuff Try to und...
Plumbing is not complex(but difficult)5/22/2013Dataiku 26Implicit User Data(Views, Searches…)Content Data(Title, Categorie...
The Bright Side5/22/2013Dataiku 27
 People  Microsoft Excel5/22/2013Dataiku 28How did you build your greatproduct ?
 Data Team  Data Tools5/22/2013Dataiku 29How will you continue growing yourgreat product(s) ?The Business Guywho knows m...
 data lab, (n. m): a small groupwith all the expertise, includingbusiness minded people,machine learning knowledge andthe...
Short Term Focus Long Term DriveBusiness People Optimize Margin, …. Create new businessrevenue streamsMarketing People Opt...
Data!ProductDesignerBusiness&MarketingEngineersUserVoiceData Innovation: fill the gap!5/22/2013Dataiku 32Targeted campaing...
 You can’t« design »insights, youexplore anddiscover them… Iterate quicklywith constantfeedback Try a lot, don’tbe afra...
Prepare for some Geeky Porn5/22/2013Dataiku 34
Classic Columnar Architecture5/22/2013Dataiku 35Some data Some Place ToPour It InSome Tool ToTo Some Maths And Graphs
Classic Columnar Architecture5/22/2013Dataiku 36Lots of data Some Place ToPour It InSome Tool ToTo Some Maths And GraphsWe...
The Corinthian Architecture5/22/2013Dataiku 37Lots of dataSome PlaceTo PerformRapid CalculationsSome Tools ToDo Some Maths...
The Corinthian Architecture5/22/2013Dataiku 38Lots of dataSome PlaceTo PerformRapid CalculationsSome Tools ToDo Some Maths...
The Corinthian Architecture5/22/2013Dataiku 39Lots of dataSome DatabaseTo PerformRapid CalculationsSome Tools ToDo Some Ma...
The One Database won’tmake it all problem5/22/2013Dataiku 40Lots of dataSome DatabaseTo PerformRapid CalculationsSome Tool...
The Roman Social Forum5/22/2013Dataiku 41Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphsS...
The Key Value Store5/22/2013Dataiku 42Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphs And...
Action requires Prediction5/22/2013Dataiku 43Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor gra...
The Medieval Fairy Land5/22/2013Dataiku 44Lots of dataSome Tools ToDo Some MathsSome OtherTo Do SomeCharts and someMACHINE...
5/22/2013Dataiku 45
 Launch A Marketingcampaign After a few daysPREDICT based onbehaviours◦  Total ARPU for usersafter 3 months◦  Efficien...
A very large communitySome mid-sizecommunitiesLots of small clustersmostly 2 players) Correlation◦ between community size...
 Two-Way Clustering◦ Assess customer behaviours◦ Assess items equivalent classes Modeling + Simulation◦ Evaluate free it...
Questions5/22/2013Dataiku 49
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Online Games Analytics - Data Science for Fun

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Transcript of "Online Games Analytics - Data Science for Fun"

  1. 1. 1Dataiku5/22/2013
  2. 2. 5/22/2013Dataiku 2CollocationBig AppleBig MamaBig DataGames AnalyticsCurrent Life:CEO, DataikuTweet about this@dataiku@capital_gamesPast Life:CriteoIsCool EntertainmentExaleadHello, My Name isFlorian DouetteauAvailable on:http://www.slideshare.net/Dataiku
  3. 3. The Stakes - Summary5/22/2013Dataiku 3Million EventsBillion $Billion EventsMillion $Classic BusinessSocial Gaming
  4. 4. Meet Hal Alowne5/22/2013Dataiku 4Big Guys• 100M$+ Revenue• 10M+ games• 10+ Data ScientistHal AlowneBI ManagerDim’s Private ShowroomHey Hal ! We needa big data platformlike the big guys.Let’s just do as they do!‟”European Online Game Leader• 10M$ Revenue• 1 Million monthly games• 1 Data Analyst (Hal Himself)Wave PoxCEO & FounderW’ave G’ amesBig DataCopy CatProject
  5. 5. MERIT = TIME + ROI5/22/2013Dataiku 5TargetedNewsletterFor New ComersFacebookCampaignOptimizationAdapted Product/ PromotionsTIME : 6 MONTHS ROI : APPS Build a lab in 6 months(rather than 18 months)Find the rightpeople(6 months?)Choose thetechnology(6 months?)Make it work(6 months?)Build the lab(6 months) Deploy appsthat actually deliver value2013 20142013• Train People• Reuse working patterns
  6. 6. Our Goal5/22/2013Dataiku 6It’s utterly complex andunreasonable
  7. 7. Our Goal5/22/2013Dataiku 7It’s utterly complex andunreasonableOur Goal:Change his perspectiveon data science projects(sorry, we couldn’tfind a picture of HalSmiling)
  8. 8.  Do the Basics Understand Analytics What to expect out of analyticsQuick Agenda5/22/2013Dataiku 8
  9. 9. 5/22/2013Dataiku 9
  10. 10.  Do you track ?◦ Customer Goals Formost importantfeatures◦ Time SpentLevel ProgresisonMoney Spent◦ Campaigns andgenerated campaignValue5/22/2013Dataiku 10Suggestion #1Check The Basics
  11. 11.  Do A/B Tests◦ Use Proven Solutions◦ Start small (button sizeand color)◦ Check Impacts◦ Treat new and existingusers differently◦ Don’t give up after thefirst A/B Test5/22/2013Dataiku 11Suggestion #2DO A/B Tests (and not yourself)
  12. 12.  Register Now / GiveEmail Graphics:From 25% to 2X MoreClickshttp://bit.ly/VOruXt Changing buttonfrom green to red:Up to 21%http://bit.ly/qFEBdK5/22/2013Dataiku 12Some ResultsA/B Tests
  13. 13. Statistical Signifiance5/22/2013Dataiku 13http://visualwebsiteoptimizer.com/ab-split-significance-calculator/
  14. 14.  Can be Built on topof your productionsystems Do you have◦ Cohorts◦ Daily $$ Reports◦ Basic $$ Segments5/22/2013Dataiku 14Suggestion #3Have the Basic BI
  15. 15.  Defined Customer Segments◦ New Installs◦ Engaged Users◦ Engaged Paying Users◦ …? Defined Customer Sources◦ Social Ads / Social Posts / .. Top Charts/ …◦ Country Segments Do you have for each segment, eveyday◦ Rolling last 30 days ARPUU ?◦ Rolling last 30 days DAY ? Do you follow every week◦ The Segment Conversion Rate persource ?5/22/2013Dataiku 15Sample Check list(Gaming)
  16. 16. Embodiment of Knowledge5/22/2013Dataiku 16
  17. 17.  Product Successdriven by Quality Margin / CustomerValue / Traffic /Acquisition5/22/2013Dataiku 17At the Beginning
  18. 18.  Margin for newcustomers mightdecline … Margin for newfeatures mightdecline … Is your businessreally scalable ?5/22/2013Dataiku 18But when you continue growing
  19. 19.  Existing Customers Existing Product Assets Existing SpecificBusiness Model And your KNOWLEDGEof it5/22/2013Dataiku 19Where is your core businessadvantage ?
  20. 20. 5/22/2013Dataiku 20Data Driven BusinessWhat your value ?Number ofCustomersCustomer KnowledgeIncrease over time with:- Time spend in your app- User relationship (network effet)- Partner / Other Apps InteractionsYour Value
  21. 21. 5/22/2013Dataiku 21To Apply It ?Product OptimizationCustomer AcquisitionOptimizationRecommender/Targeting fornewsletters
  22. 22.  Dark Side◦ Technology Bright Side◦ Business5/22/2013Dataiku 22Apply It !!
  23. 23. The Dark Side5/22/2013Dataiku 23
  24. 24. Technology is complex5/22/2013Dataiku 24HadoopCephSphereCassandraSparkScikit-LearnMahoutWEKAMLBaseRapidMinerPandaD3CrossfilterInfiniDBLucidDBImpalaElastic SearchSOLRMongoDBRiakMembasePigHiveCascadingTalendMachine LearningMystery LandScalability CentralNoSQL-SlaviaSQL Colunnar RepublicVizualization CountyData Clean WastelandStatistician OldHouseR
  25. 25. Machine learning is complex5/22/2013Dataiku 25 Find People that understand machine learningand all this stuff Try to understandmyself
  26. 26. Plumbing is not complex(but difficult)5/22/2013Dataiku 26Implicit User Data(Views, Searches…)Content Data(Title, Categories, Price, …)Explicit User Data(Click, Buy, …)User Information(Location, Graph…)500TB50TB1TB200GBTransformationMatrixTransformationPredictorPer User StatsPer Content StatsUser SimilarityRank PredictorContent Similarity
  27. 27. The Bright Side5/22/2013Dataiku 27
  28. 28.  People  Microsoft Excel5/22/2013Dataiku 28How did you build your greatproduct ?
  29. 29.  Data Team  Data Tools5/22/2013Dataiku 29How will you continue growing yourgreat product(s) ?The Business Guywho knows mathsThe Crazy Analystthat reveals patternsThe Coding Guy Thatis enthusiastic
  30. 30.  data lab, (n. m): a small groupwith all the expertise, includingbusiness minded people,machine learning knowledge andthe right technology A proven organization used bysuccessful data-drivencompanies over the past fewyears (eBay, LinkedIn, Walmart…)TEAM + TOOLS= LAB5/22/2013Dataiku 30
  31. 31. Short Term Focus Long Term DriveBusiness People Optimize Margin, …. Create new businessrevenue streamsMarketing People Optimize click ratio Brand awareness andimpactIT People Make IT work Clean and efficientArchitectureData People Get Stats Right, makepredictionsCreate Data DrivenFeaturesIt’s just a new team5/22/2013Dataiku 31
  32. 32. Data!ProductDesignerBusiness&MarketingEngineersUserVoiceData Innovation: fill the gap!5/22/2013Dataiku 32Targeted campaingsPrice optimizationA common ground tofederate your product teamstowards a common goalPersonalizedexperienceQuality AssuranceWorkload and yieldmanagementUser Feedback (A/B Test)Continuous improvement
  33. 33.  You can’t« design »insights, youexplore anddiscover them… Iterate quicklywith constantfeedback Try a lot, don’tbe afraid to fail!Freebut not as “free beer”5/22/2013Dataiku 33FunctionFormExperienceEmotionSurpriseCultureExploreand RefineExperimentGenerateIdeasSelect &DevelopEnhanceorDiscardGatherFeedback
  34. 34. Prepare for some Geeky Porn5/22/2013Dataiku 34
  35. 35. Classic Columnar Architecture5/22/2013Dataiku 35Some data Some Place ToPour It InSome Tool ToTo Some Maths And Graphs
  36. 36. Classic Columnar Architecture5/22/2013Dataiku 36Lots of data Some Place ToPour It InSome Tool ToTo Some Maths And GraphsWeb Tracking LogsRaw Server LogsOrder / Product / CustomerFacebook InfoOpen Data (Weather, Currency …)
  37. 37. The Corinthian Architecture5/22/2013Dataiku 37Lots of dataSome PlaceTo PerformRapid CalculationsSome Tools ToDo Some MathsAnd ChartsSome Place ToPour It In AndClean / Prepare It
  38. 38. The Corinthian Architecture5/22/2013Dataiku 38Lots of dataSome PlaceTo PerformRapid CalculationsSome Tools ToDo Some MathsAnd ChartsSome Place ToPour It In AndClean / Prepare ItStatisticsCohortsRegressionsBar Charts For MarketingNice Infography for you Company Board
  39. 39. The Corinthian Architecture5/22/2013Dataiku 39Lots of dataSome DatabaseTo PerformRapid CalculationsSome Tools ToDo Some MathsSome OtherTo Do SomeChartsSome Place ToPour It In AndClean / Prepare It
  40. 40. The One Database won’tmake it all problem5/22/2013Dataiku 40Lots of dataSome DatabaseTo PerformRapid CalculationsSome Tools ToDo Some MathsSome OtherTo Do SomeChartsSome Place ToPour It In AndClean / Prepare ItJOIN / AggregateRapid Goup By ComputationsDirect Access to the computed Resultsto production etc..
  41. 41. The Roman Social Forum5/22/2013Dataiku 41Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphsSome Tools ToDo Some MathsSome OtherTo Do SomeChartsSome Place ToPour It In AndClean / Prepare It
  42. 42. The Key Value Store5/22/2013Dataiku 42Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphs AndSome Distributed KeyValue StoreSome Tools ToDo Some MathsSome OtherTo Do SomeChartsSome Place ToPour It In AndClean / Prepare It
  43. 43. Action requires Prediction5/22/2013Dataiku 43Lots of dataSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphs AndSome Distributed KeyValue StoreSome Tools ToDo Some MathsSome OtherTo Do SomeChartsSome Place ToPour It In AndClean / Prepare ItDraw A Line  For the futureWhat are my real users groups ?Should I launch a discount offering or not ?To everybody or to specific users only ?
  44. 44. The Medieval Fairy Land5/22/2013Dataiku 44Lots of dataSome Tools ToDo Some MathsSome OtherTo Do SomeCharts and someMACHINE LEARNINGSome Place ToPour It In AndClean / Prepare ItSome DatabaseTo PerformRapid CalculationsAnd some databasefor graphs AndSome Distributed KeyValue Store
  45. 45. 5/22/2013Dataiku 45
  46. 46.  Launch A Marketingcampaign After a few daysPREDICT based onbehaviours◦  Total ARPU for usersafter 3 months◦  Efficiency of a campaign◦ Continue or not ?ExampleMarketing Campaign PredictionDataiku 46
  47. 47. A very large communitySome mid-sizecommunitiesLots of small clustersmostly 2 players) Correlation◦ between community sizeand engagement / virality Meaningul patterns◦ 2 players patterns◦ Family play◦ Group Play◦ Open Play (languagecommunity)ExampleSocial Gaming Communities5/22/2013Dataiku 47
  48. 48.  Two-Way Clustering◦ Assess customer behaviours◦ Assess items equivalent classes Modeling + Simulation◦ Evaluate free items / item boughtration per item kind◦ Simulate future rules◦ Sensibility to price evaluation Enhance customer buyrecurrenceExampleFremium Model Optimization5/22/2013Dataiku 48BusinessModelUserProfilingSimulation
  49. 49. Questions5/22/2013Dataiku 49
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