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Online Games Analytics - Data Science for Fun
This document discusses how a data analytics lab can help a small European online game company optimize their business using data science techniques. It provides examples of how the company could use analytics to improve marketing campaigns, predict customer value, analyze social gaming communities, and optimize their freemium business model. The document advocates establishing a small cross-functional data team with the right expertise, tools, and focus on experimentation to help drive business decisions with data and analytics.
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Online Games Analytics - Data Science for Fun
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- 2. 5/22/2013Dataiku 2 Collocation Big Apple BigMama Big Data Games Analytics Current Life: CEO, Dataiku Tweet about this @dataiku @capital_games Past Life: Criteo IsCool Entertainment Exalead Hello, My Name is Florian Douetteau Available on: http://www.slideshare.net/Dataiku
- 3. The Stakes -Summary 5/22/2013Dataiku 3 Million Events Billion $ Billion Events Million $ Classic Business Social Gaming
- 4. Meet Hal Alowne 5/22/2013Dataiku4 Big Guys • 100M$+ Revenue • 10M+ games • 10+ Data Scientist Hal Alowne BI Manager Dim’s Private Showroom Hey Hal ! We need a big data platform like the big guys. Let’s just do as they do! ‟ ”European Online Game Leader • 10M$ Revenue • 1 Million monthly games • 1 Data Analyst (Hal Himself) Wave Pox CEO & Founder W’ave G’ ames Big Data Copy Cat Project
- 5. MERIT = TIME+ ROI 5/22/2013Dataiku 5 Targeted Newsletter For New Comers Facebook Campaign Optimization Adapted Product / Promotions TIME : 6 MONTHS ROI : APPS Build a lab in 6 months (rather than 18 months) Find the right people (6 months?) Choose the technology (6 months?) Make it work (6 months?) Build the lab (6 months) Deploy apps that actually deliver value 2013 2014 2013 • Train People • Reuse working patterns
- 6. Our Goal 5/22/2013Dataiku 6 It’sutterly complex and unreasonable
- 7. Our Goal 5/22/2013Dataiku 7 It’sutterly complex and unreasonable Our Goal: Change his perspective on data science projects (sorry, we couldn’t find a picture of Hal Smiling)
- 8. Do theBasics Understand Analytics What to expect out of analytics Quick Agenda 5/22/2013Dataiku 8
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- 10. Do youtrack ? ◦ Customer Goals For most important features ◦ Time Spent Level Progresison Money Spent ◦ Campaigns and generated campaign Value 5/22/2013Dataiku 10 Suggestion #1 Check The Basics
- 11. Do A/BTests ◦ Use Proven Solutions ◦ Start small (button size and color) ◦ Check Impacts ◦ Treat new and existing users differently ◦ Don’t give up after the first A/B Test 5/22/2013Dataiku 11 Suggestion #2 DO A/B Tests (and not yourself)
- 12. Register Now/ Give Email Graphics: From 25% to 2X More Clicks http://bit.ly/VOruXt Changing button from green to red: Up to 21% http://bit.ly/qFEBdK 5/22/2013Dataiku 12 Some Results A/B Tests
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- 14. Can beBuilt on top of your production systems Do you have ◦ Cohorts ◦ Daily $$ Reports ◦ Basic $$ Segments 5/22/2013Dataiku 14 Suggestion #3 Have the Basic BI
- 15. Defined CustomerSegments ◦ New Installs ◦ Engaged Users ◦ Engaged Paying Users ◦ …? Defined Customer Sources ◦ Social Ads / Social Posts / .. Top Charts / … ◦ Country Segments Do you have for each segment, evey day ◦ Rolling last 30 days ARPUU ? ◦ Rolling last 30 days DAY ? Do you follow every week ◦ The Segment Conversion Rate per source ? 5/22/2013Dataiku 15 Sample Check list (Gaming)
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- 17. Product Success drivenby Quality Margin / Customer Value / Traffic / Acquisition 5/22/2013Dataiku 17 At the Beginning
- 18. Margin fornew customers might decline … Margin for new features might decline … Is your business really scalable ? 5/22/2013Dataiku 18 But when you continue growing
- 19. Existing Customers Existing Product Assets Existing Specific Business Model And your KNOWLEDGE of it 5/22/2013Dataiku 19 Where is your core business advantage ?
- 20. 5/22/2013Dataiku 20 Data DrivenBusiness What your value ? Number of Customers Customer Knowledge Increase over time with: - Time spend in your app - User relationship (network effet) - Partner / Other Apps Interactions Your Value
- 21. 5/22/2013Dataiku 21 To ApplyIt ? Product Optimization Customer Acquisition Optimization Recommender/ Targeting for newsletters
- 22. Dark Side ◦Technology Bright Side ◦ Business 5/22/2013Dataiku 22 Apply It !!
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- 24. Technology is complex 5/22/2013Dataiku24 Hadoop Ceph Sphere Cassandra Spark Scikit-Learn Mahout WEKA MLBase RapidMiner Panda D3 Crossfilter InfiniDB LucidDB Impala Elastic Search SOLR MongoDB Riak Membase Pig Hive Cascading Talend Machine Learning Mystery Land Scalability CentralNoSQL-Slavia SQL Colunnar Republic Vizualization County Data Clean Wasteland Statistician Old House R
- 25. Machine learning iscomplex 5/22/2013Dataiku 25 Find People that understand machine learning and all this stuff Try to understand myself
- 26. Plumbing is notcomplex (but difficult) 5/22/2013Dataiku 26 Implicit User Data (Views, Searches…) Content Data (Title, Categories, Price, …) Explicit User Data (Click, Buy, …) User Information (Location, Graph…) 500TB 50TB 1TB 200GB Transformation Matrix Transformation Predictor Per User Stats Per Content Stats User Similarity Rank Predictor Content Similarity
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- 28. People Microsoft Excel 5/22/2013Dataiku 28 How did you build your great product ?
- 29. Data Team Data Tools 5/22/2013Dataiku 29 How will you continue growing your great product(s) ? The Business Guy who knows maths The Crazy Analyst that reveals patterns The Coding Guy That is enthusiastic
- 30. data lab,(n. m): a small group with all the expertise, including business minded people, machine learning knowledge and the right technology A proven organization used by successful data-driven companies over the past few years (eBay, LinkedIn, Walmart…) TEAM + TOOLS= LAB 5/22/2013Dataiku 30
- 31. Short Term FocusLong Term Drive Business People Optimize Margin, …. Create new business revenue streams Marketing People Optimize click ratio Brand awareness and impact IT People Make IT work Clean and efficient Architecture Data People Get Stats Right, make predictions Create Data Driven Features It’s just a new team 5/22/2013Dataiku 31
- 32. Data ! Product Designer Business & Marketing Engineers User Voice Data Innovation: fillthe gap! 5/22/2013Dataiku 32 Targeted campaings Price optimization A common ground to federate your product teams towards a common goal Personalized experience Quality Assurance Workload and yield management User Feedback (A/B Test) Continuous improvement
- 33. You can’t «design » insights, you explore and discover them… Iterate quickly with constant feedback Try a lot, don’t be afraid to fail! Free but not as “free beer” 5/22/2013Dataiku 33 Function Form Experience Emotion Surprise Culture Explore and Refine Experiment Generate Ideas Select & Develop Enhance or Discard Gather Feedback
- 34. Prepare for someGeeky Porn 5/22/2013Dataiku 34
- 35. Classic Columnar Architecture 5/22/2013Dataiku35 Some data Some Place To Pour It In Some Tool To To Some Maths And Graphs
- 36. Classic Columnar Architecture 5/22/2013Dataiku36 Lots of data Some Place To Pour It In Some Tool To To Some Maths And Graphs Web Tracking Logs Raw Server Logs Order / Product / Customer Facebook Info Open Data (Weather, Currency …)
- 37. The Corinthian Architecture 5/22/2013Dataiku37 Lots of data Some Place To Perform Rapid Calculations Some Tools To Do Some Maths And Charts Some Place To Pour It In And Clean / Prepare It
- 38. The Corinthian Architecture 5/22/2013Dataiku38 Lots of data Some Place To Perform Rapid Calculations Some Tools To Do Some Maths And Charts Some Place To Pour It In And Clean / Prepare It Statistics Cohorts Regressions Bar Charts For Marketing Nice Infography for you Company Board
- 39. The Corinthian Architecture 5/22/2013Dataiku39 Lots of data Some Database To Perform Rapid Calculations Some Tools To Do Some Maths Some Other To Do Some Charts Some Place To Pour It In And Clean / Prepare It
- 40. The One Databasewon’t make it all problem 5/22/2013Dataiku 40 Lots of data Some Database To Perform Rapid Calculations Some Tools To Do Some Maths Some Other To Do Some Charts Some Place To Pour It In And Clean / Prepare It JOIN / Aggregate Rapid Goup By Computations Direct Access to the computed Results to production etc..
- 41. The Roman SocialForum 5/22/2013Dataiku 41 Lots of data Some Database To Perform Rapid Calculations And some database for graphs Some Tools To Do Some Maths Some Other To Do Some Charts Some Place To Pour It In And Clean / Prepare It
- 42. The Key ValueStore 5/22/2013Dataiku 42 Lots of data Some Database To Perform Rapid Calculations And some database for graphs And Some Distributed Key Value Store Some Tools To Do Some Maths Some Other To Do Some Charts Some Place To Pour It In And Clean / Prepare It
- 43. Action requires Prediction 5/22/2013Dataiku43 Lots of data Some Database To Perform Rapid Calculations And some database for graphs And Some Distributed Key Value Store Some Tools To Do Some Maths Some Other To Do Some Charts Some Place To Pour It In And Clean / Prepare It Draw A Line For the future What are my real users groups ? Should I launch a discount offering or not ? To everybody or to specific users only ?
- 44. The Medieval FairyLand 5/22/2013Dataiku 44 Lots of data Some Tools To Do Some Maths Some Other To Do Some Charts and some MACHINE LEARNING Some Place To Pour It In And Clean / Prepare It Some Database To Perform Rapid Calculations And some database for graphs And Some Distributed Key Value Store
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- 46. Launch AMarketing campaign After a few days PREDICT based on behaviours ◦ Total ARPU for users after 3 months ◦ Efficiency of a campaign ◦ Continue or not ? Example Marketing Campaign Prediction Dataiku 46
- 47. A very largecommunity Some mid-size communities Lots of small clusters mostly 2 players) Correlation ◦ between community size and engagement / virality Meaningul patterns ◦ 2 players patterns ◦ Family play ◦ Group Play ◦ Open Play (language community) Example Social Gaming Communities 5/22/2013Dataiku 47
- 48. Two-Way Clustering ◦Assess customer behaviours ◦ Assess items equivalent classes Modeling + Simulation ◦ Evaluate free items / item bought ration per item kind ◦ Simulate future rules ◦ Sensibility to price evaluation Enhance customer buy recurrence Example Fremium Model Optimization 5/22/2013Dataiku 48 Business Model User Profiling Simulation
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