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Big Data
                 + Social
                 + Games
                 @Is Cool
                        16/03/2012
TITRE DOCUMENT
Who is IsCool Entertainment?


  Social game publisher based in    Agenda
  Paris, France                     • What do we do?
                                    Social Gaming
  #1 French publisher in terms of   •   What kind of (Big) Analytics we do?
  audience (450k Daily Active       Lots
  Users) & revenue                  •   How we do it ?
                                    Hadoop, Python, R, Tableau, Geph and stuff…
  2.8 Millions Fans
  80 employees
                                                          Florian Douetteau
  9.1 million € revenue in 2010                           CTO
  4 live applications on Facebook                         @fdouetteau
Is Cool Games

                              IsCool,   Absolute Solitaire,
                Delirious Collectible   The best solitaire game
                               Game     available online




             Temple Of Mahjong,         Belote Multijoueur,
           Collect, Play, Exchange      Play, Win, Meet
Games & Virtual Goods

                         Play the Game & Gain some
                         virtual goods
                         Play again & Gain more
                         Collaborate with other players
                         & Gain More
                         ….
                         Possibly buy
                         To grow quicker
                         To help others
Virtual Goods  Virtual Economy

  Virtual Goods Must not be too
  easy to get
  The game would not be fun !
  No monetization
  Virtual Goods must not be hard
  to get
  People would churn because of    Let’s Trade 1
                                    Watch against
    frustration !                    3 Hammers
  Virtual Goods can be usually
  traded between players
  Virtual and actual “Price” of a
  good
Why is this Big Data ?

                           Number of object transactions per day
                           NYSE              3,600,000,000
   18 Million users
   generated actions
   per day                 IsCool            2,150,000,000
   7 Billions per year.
                           Nasdaq            1,600,000,000

   9,8 TB Data to
                           Nikkey            1,500,000,000
   analyze
                           Footsie           860,000,000

                           CAC 40            142,500,000
The Real Big Data Challenge
  Collaborate for collective insights
                                                        Programmers’ Perspective :
 Game Designer Perspective :                            Log Files & Work ?
 Nice Charts ?
                                                 Realtime?

                  what
                 metrics?
                               data scientist?




BI Veteran:                                           Business Guy Perspective:
Schema Definition ?                                   Revenue Forecast ?
Specifics of Game Analytics

  Virtual Goods
  We are the Factory AND the
    Shop, and most of the products
    are free.
  Social Networks
  Network effects are key
  Games
  The product changes EVERY day !
  Sudden wage of unexpected
    players from Guatemala !
  People try to cheat !
Use Case 1 : Understanding Users

  1: Defining engagement

                                        Tenure length
                                        Visit frequency
                                        Virality
 Traffic               Key drivers???   Paying user conversion
                                        ARPPU
                                        Score
                                        Use of feature A,B,C…
Case Study 1 - Segment User Behaviours

  2: Describing engagement patterns: Running a segment analysis
Use Case 2 : Understanding Users as a whole


  10 Million Nodes
  Around 1 000 Billion
  Edges




                               How does the graph evolve in
                               time ?
                               What are the
                               communities?
Understanding Users as a Whole

Lots of small clusters ((mostly 2
players)
                                              Some mid size communities




                                    A very large community
Use Case 3 : Analyze Long Terms effect of a feature

                             A/B Tests
                             Some features can be A/B tested
                             …and some cannot !
                             How to measure the uplift ?
                             Are players using the new feature…
                             More engaged?
                             Generate more virality ?
                             etc….
                             Complexity
                             Multiple variable to observe
                               (other features, history )



                                                 TITRE DOCUMENT   16/03/2012
… How




over the last 3 years   Analyzing the Offer
• Tools changed         • Online Analytics Platform
• Scale changed         • Commercial / Open Source ETL
• Focus Changed         • Commercial BI Visualization Software
                        • Commercial / Open Source databases
                          (column stores)
                        •…
What we learned


         Diversity                  Relativity                    Superciality

• There's no Hadoop+R       • Windows / Linux ? Cloud     • Ability to display is more
  Magic (Expertise, Entry     or on-premise ?               important than the
  Costs, Maintenance)       • Do you have internal data     result.
• There’s no XYZ Magical      mining experts (yes/no) ?
  Product                   • Do you have internal
                              scalability
                              experts (yes/no) ?
                            • What is _real_ budget ?
                              0K ? 10K ? 100K ? 1000K
                              ?
Mixed Approach
  SaaS Analytics Platforms
  For common, business metrics (virality,
     traffic, engagement)
  Corporate Level Visibility
  Day-to-day
  Internal Datawarehousing
  Detailed Business Metrics
  Virtual Economy Modeling
  Long term behaviours
  Business Level Visibility
  Week-to-Week
  Datamining tools
  Ad-hoc analytics
  Graph Analytics
Datawarehouse for the Big Data era

   Hadoop/Hive (through Amazon’s                   Open Source ETL (PyBabe)
   Elastric Map Reduce)                            • Pure Python ETL
   • Used to reduce the amount of information :    • Good integration with AWS/ S3
     10 GB a day => 1GB a day                      • Easy to integrate in our development
   • High cost of development for "business"         environment
     related processing




   Columnar Database (Infinidb, Open               Dashboarding (Tableau Software)
   Source)                                         • +Direct connection to the database
   • Free (as beer)                                • +Excel fan biz guy can use it with no training !
   • Good performance for analytics tasks on a
     few hundreds million lines ( SELECT … GROUP
     BY … ORDER … )
   • Featured and limited performance compared
     to commercial Column Stores
Questions ?

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Big data paris 2011 is cool florian douetteau

  • 1. Big Data + Social + Games @Is Cool 16/03/2012 TITRE DOCUMENT
  • 2. Who is IsCool Entertainment? Social game publisher based in Agenda Paris, France • What do we do? Social Gaming #1 French publisher in terms of • What kind of (Big) Analytics we do? audience (450k Daily Active Lots Users) & revenue • How we do it ? Hadoop, Python, R, Tableau, Geph and stuff… 2.8 Millions Fans 80 employees Florian Douetteau 9.1 million € revenue in 2010 CTO 4 live applications on Facebook @fdouetteau
  • 3. Is Cool Games IsCool, Absolute Solitaire, Delirious Collectible The best solitaire game Game available online Temple Of Mahjong, Belote Multijoueur, Collect, Play, Exchange Play, Win, Meet
  • 4. Games & Virtual Goods Play the Game & Gain some virtual goods Play again & Gain more Collaborate with other players & Gain More …. Possibly buy  To grow quicker  To help others
  • 5. Virtual Goods  Virtual Economy Virtual Goods Must not be too easy to get  The game would not be fun !  No monetization Virtual Goods must not be hard to get  People would churn because of Let’s Trade 1 Watch against frustration ! 3 Hammers Virtual Goods can be usually traded between players Virtual and actual “Price” of a good
  • 6. Why is this Big Data ? Number of object transactions per day  NYSE 3,600,000,000 18 Million users generated actions per day  IsCool 2,150,000,000 7 Billions per year.  Nasdaq 1,600,000,000 9,8 TB Data to  Nikkey 1,500,000,000 analyze  Footsie 860,000,000  CAC 40 142,500,000
  • 7. The Real Big Data Challenge Collaborate for collective insights Programmers’ Perspective : Game Designer Perspective : Log Files & Work ? Nice Charts ? Realtime? what metrics? data scientist? BI Veteran: Business Guy Perspective: Schema Definition ? Revenue Forecast ?
  • 8. Specifics of Game Analytics Virtual Goods  We are the Factory AND the Shop, and most of the products are free. Social Networks  Network effects are key Games  The product changes EVERY day !  Sudden wage of unexpected players from Guatemala !  People try to cheat !
  • 9. Use Case 1 : Understanding Users 1: Defining engagement Tenure length Visit frequency Virality Traffic Key drivers??? Paying user conversion ARPPU Score Use of feature A,B,C…
  • 10. Case Study 1 - Segment User Behaviours 2: Describing engagement patterns: Running a segment analysis
  • 11. Use Case 2 : Understanding Users as a whole 10 Million Nodes Around 1 000 Billion Edges How does the graph evolve in time ? What are the communities?
  • 12. Understanding Users as a Whole Lots of small clusters ((mostly 2 players) Some mid size communities A very large community
  • 13. Use Case 3 : Analyze Long Terms effect of a feature A/B Tests  Some features can be A/B tested  …and some cannot !  How to measure the uplift ? Are players using the new feature…  More engaged?  Generate more virality ?  etc…. Complexity  Multiple variable to observe (other features, history ) TITRE DOCUMENT 16/03/2012
  • 14. … How over the last 3 years Analyzing the Offer • Tools changed • Online Analytics Platform • Scale changed • Commercial / Open Source ETL • Focus Changed • Commercial BI Visualization Software • Commercial / Open Source databases (column stores) •…
  • 15. What we learned Diversity Relativity Superciality • There's no Hadoop+R • Windows / Linux ? Cloud • Ability to display is more Magic (Expertise, Entry or on-premise ? important than the Costs, Maintenance) • Do you have internal data result. • There’s no XYZ Magical mining experts (yes/no) ? Product • Do you have internal scalability experts (yes/no) ? • What is _real_ budget ? 0K ? 10K ? 100K ? 1000K ?
  • 16. Mixed Approach SaaS Analytics Platforms  For common, business metrics (virality, traffic, engagement)  Corporate Level Visibility  Day-to-day Internal Datawarehousing  Detailed Business Metrics  Virtual Economy Modeling  Long term behaviours  Business Level Visibility  Week-to-Week Datamining tools  Ad-hoc analytics  Graph Analytics
  • 17. Datawarehouse for the Big Data era Hadoop/Hive (through Amazon’s Open Source ETL (PyBabe) Elastric Map Reduce) • Pure Python ETL • Used to reduce the amount of information : • Good integration with AWS/ S3 10 GB a day => 1GB a day • Easy to integrate in our development • High cost of development for "business" environment related processing Columnar Database (Infinidb, Open Dashboarding (Tableau Software) Source) • +Direct connection to the database • Free (as beer) • +Excel fan biz guy can use it with no training ! • Good performance for analytics tasks on a few hundreds million lines ( SELECT … GROUP BY … ORDER … ) • Featured and limited performance compared to commercial Column Stores