BigData +Social$ + Games$ @IsCool$$ · PDF fileDayhTohDay%h%SaaS%AnalyKcs%Plaorms%!...
Transcript of BigData +Social$ + Games$ @IsCool$$ · PDF fileDayhTohDay%h%SaaS%AnalyKcs%Plaorms%!...
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Big Data + Social + Games @Is Cool
3/24/12 TITRE DOCUMENT
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IsCool Entertainment
! Social game publisher based in Paris, France
! #1 French publisher in terms of audience (450k Daily AcKve Users) & revenue
! 2.8 Million Fans ! 80 employees ! €9.1 million revenue in 2010 ! 4 live applicaKons on Facebook
Florian DoueTeau CTO @fdoue?eau
Agenda • What do we do Social Gaming • What kind of (Big) AnalyKcs we do Lots • How we do it Hadoop, Python, R, Tableau, Gephi and stuff…
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Is Cool Games
Is Cool, Delirious CollecKble
Game
Absolute Solitaire, The best solitaire game available online
Temple Of Mahjong, Collect, Play, Exchange
Belote MulKjoueur, Play, Win, Meet
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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
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Virtual Goods Virtual Economy
! Virtual Goods Must not be too easy to get § The game would not be fun! § No moneKzaKon
! Virtual Goods must not be hard to get § People would churn because of
frustraKon! ! Virtual Goods can usually be
traded between players ! Virtual and actual “Price” of a
good
Let’s Trade 1 Watch against 3 Hammers
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Why is this Big Data ?
! Number of object transacKons per day § NYSE 3,600,000,000 § 1,600,000,000
§ 1,500,000,000
§ IsCool 1,400,000,000
§ 860,000,000
§ CAC 40 142,500,000
9,8 TB Data to analyze
18 Million user-‐generated acKons per day 7 Billion per year.
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The Real Big Data Challenge Collaborate for collecQve insights
data scienKst?
what metrics?
Real-‐Kme?
Game Designer PerspecKve : Nice Charts ?
Programmers’ PerspecKve : Log Files & Work ?
Business Guy PerspecKve: Revenue Forecast ?
BI Veteran: Schema DefiniKon ?
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Specifics of Game AnalyQcs
! 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 !
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Use Case 1: Engagement Drivers
! StaKsKcal Mesaure of Engagement § Visit Frequency, DAU /
MAU ! Analyze Engagement Drivers § Use of Features ? § Demographics ? § How does it relate in Kme
with moneKzaKon ? § ….
3/24/12
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Understanding Engagement -‐ Results
! Establish class of users with different engagement profile and use of features
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Understanding Engagement – Benefits
! Adapt the features correlated with strong engagement and interesKng profile so that they can easily be accessed by other players
3/24/12
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Use Case 2 : Understanding Users as a whole
! 10 Million Nodes ! Around 1 Billion Edges
! How does the graph evolve in Kme ?
! What are the communiKes? ! Leaders ? ! CorrelaKon with engagement,
virality , etc.. ?
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Understanding Users as a Whole – Clusters and Graphs
A very large community
Some mid-‐size communi6es Lots of small clusters mostly 2 players)
! Specific communiKes in the graph
! CorrelaKon between community size and engagement / virality
! DetecKon of paTerns § 2 players paTerns § Family play § Group Play § Open Play (language
community)
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Understanding Users as a Whole -‐ Benefits
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! Cluster-‐oriented Community-‐Management § Engage with a community as
a whole as much as possible
! Nurture communiKes § Make communiKes grow unKl
they reach a criKcal mass § Reduce language barrier to
help community aggregaKons ! DetecKon of “opinion leaders”
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Use Case 3 : Long Terms effects of a feature
3/24/12 TITRE DOCUMENT
! Are players using the new feature… § Happy with it ? § More engaged? § Generate more virality ? § etc….
! A/B Tests § Some features can be A/B tested § …and some cannot ! § How to measure the uplio ?
! Complexity § MulKple variable to observe (other
features, history, and 20 more ….) § Long term non local effect (game
economics)
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Long Terms Effects of a feature -‐ Results
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! Adapt game rules to fit most of the players § No InflaKon § But maintain Growth !!
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How did we do that ?
In the past 4 years …. • Tools changed • Scale changed • Focus Changed
Technological Offering • Commercial / Open Source ETL • Commercial BI VisualizaKon Sooware
• Commercial / Open Source databases (column stores)
• …
• Big Data Approach
2010-‐2011
• BI Approach
2009-‐2010
• Basic Approach
2008-‐2009
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What we learned
Diversity
RelaKvity
CollaboraKon
No Hadoop+R Magic
Windows / Linux ?
What is real budget ?
VisualizaQon is more important than precision
Do you have data mining experts (yes/no) ?
Do you have scalability experts ?
Cloud or on-‐premise ?
Do you want anybody to play with the data ?
No XYZ Magical Product
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! Ad-‐hoc -‐ Datamining tools § To Discover new trends § Ad-‐hoc analyKcs § Graph AnalyKcs
! Week-‐to-‐Week -‐ Datawarehousing § Detailed Business Metrics § Virtual Economy Modeling § Long-‐term behaviours § Business Level Visibility
AdapQve AnalyQcs
! Day-‐To-‐Day -‐ SaaS AnalyKcs Plarorms § For common, business metrics
(virality, traffic, engagement) § Corporate Level Visibility § Day-‐to-‐day
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Internal Data Warehousing
• +Direct connecKon to the database
• +Excel fan biz guy can use it with no training !
VisualizaKon (Tableau Sooware)
• Free (as beer) • Good performance for analyKcs tasks on a few hundreds million lines ( SELECT … GROUP BY … ORDER … )
• Featured and limited performance compared to commercial Column Stores
Columnar Database (Infinidb)
• Pure Python ETL • Good integraKon with AWS/ S3
• Easy to integrate in our development environment
ETL (PyBabe)
• Used to reduce the amount of informaKon : 10 GB a day => 1GB a day
• High cost of development for business-‐related processing
MapReduce (Hadoop/Hive)
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?