1Dataiku5/27/2013
5/27/2013Dataiku 2
Collocation
Big Apple
Big Mama
Big Data
Games Analytics
Current Life:
CEO, Dataiku
Tweet about this
@da...
The Stakes - Summary
5/27/2013Dataiku 3
Million Events
Billion $
Billion Events
Million $
Classic Business
Social Gaming
Meet Hal Alowne
5/27/2013Dataiku 4
Big Guys
• 100M$+ Revenue
• 10M+ games
• 10+ Data Scientist
Hal Alowne
BI Manager
Dim’s...
MERIT = TIME + ROI
5/27/2013Dataiku 5
Targeted
Newsletter
For New Comers
Facebook
Campaign
Optimization
Adapted Product
/ ...
Our Goal
5/27/2013Dataiku 6
It’s utterly complex and
unreasonable
Our Goal
5/27/2013Dataiku 7
It’s utterly complex and
unreasonable
Our Goal:
Change his perspective
on data science project...
 Do the Basics
 Understand Analytics
 What to expect out of analytics
Quick Agenda
5/27/2013Dataiku 8
5/27/2013Dataiku 9
 Do you track ?
◦ Customer Goals For
most important
features
◦ Time Spent
Level Progresison
Money Spent
◦ Campaigns and
g...
 Do A/B Tests
◦ Use Proven Solutions
◦ Start small (button size
and color)
◦ Check Impacts
◦ Treat new and existing
users...
 Register Now / Give
Email Graphics:
From 25% to 2X More
Clicks
http://bit.ly/VOruXt
 Changing button
from green to red:...
Statistical Signifiance
5/27/2013Dataiku 13
http://visualwebsiteoptimizer.com/ab-split-significance-calculator/
 Can be Built on top
of your production
systems
 Do you have
◦ Cohorts
◦ Daily $$ Reports
◦ Basic $$ Segments
5/27/2013D...
 Defined Customer Segments
◦ New Installs
◦ Engaged Users
◦ Engaged Paying Users
◦ …?
 Defined Customer Sources
◦ Social...
Embodiment of Knowledge
5/27/2013Dataiku 16
 Product Success
driven by Quality
 Margin / Customer
Value / Traffic /
Acquisition
5/27/2013Dataiku 17
At the Beginning
 Margin for new
customers might
decline …
 Margin for new
features might
decline …
 Is your business
really scalable ?
...
 Existing Customers
 Existing Product Assets
 Existing Specific
Business Model
 And your KNOWLEDGE
of it
5/27/2013Data...
5/27/2013Dataiku 20
Data Driven Business
What your value ?
Number of
Customers
Customer Knowledge
Increase over time with:...
5/27/2013Dataiku 21
Apply It !
Product Optimization
Customer Acquisition
Optimization
Recommender/
Targeting for
newslette...
 Dark Side
◦ Technology
 Bright Side
◦ Business
5/27/2013Dataiku 22
How ?
The Dark Side
5/27/2013Dataiku 23
Technology is complex
5/27/2013Dataiku 24
Hadoop
Ceph
Sphere
Cassandra
Spark
Scikit-Learn
Mahout
WEKA
MLBase
RapidMiner
Pa...
Machine learning is complex
5/27/2013Dataiku 25
 Find People that understand machine learning
and all this stuff
 Try to...
Plumbing is not complex
(but difficult)
5/27/2013Dataiku 26
Implicit User Data
(Views, Searches…)
Content Data
(Title, Cat...
The Bright Side
5/27/2013Dataiku 27
 People  Microsoft Excel
5/27/2013Dataiku 28
How did you build your great
product ?
 Data Team  Data Tools
5/27/2013Dataiku 29
How will you continue growing your
great product(s) ?
The Business Guy
who kn...
 data lab, (n. m): a small group
with all the expertise, including
business minded people,
machine learning knowledge and...
Short Term Focus Long Term Drive
Business People Optimize Margin, …. Create new business
revenue streams
Marketing People ...
Data
!
Product
Designer
Business
&
Marketing
Engineers
User
Voice
Data Innovation: fill the gap!
5/27/2013Dataiku 32
Targe...
Prepare for some Geeky Porn
5/27/2013Dataiku 33
Classic Columnar Architecture
5/27/2013Dataiku 34
Some data Some Place To
Pour It In
Some Tool To
To Some Maths And Graphs
Classic Columnar Architecture
5/27/2013Dataiku 35
Lots of data Some Place To
Pour It In
Some Tool To
To Some Maths And Gra...
The Corinthian Architecture
5/27/2013Dataiku 36
Lots of data
Some Place
To Perform
Rapid Calculations
Some Tools To
Do Som...
The Corinthian Architecture
5/27/2013Dataiku 37
Lots of data
Some Place
To Perform
Rapid Calculations
Some Tools To
Do Som...
The Corinthian Architecture
5/27/2013Dataiku 38
Lots of data
Some Database
To Perform
Rapid Calculations
Some Tools To
Do ...
The One Database won’t
make it all problem
5/27/2013Dataiku 39
Lots of data
Some Database
To Perform
Rapid Calculations
So...
The Roman Social Forum
5/27/2013Dataiku 40
Lots of data
Some Database
To Perform
Rapid Calculations
And Some Database
For ...
The Key Value Store
5/27/2013Dataiku 41
Lots of data
Some Database
To Perform
Rapid Calculations
And Some Database
For Gra...
Action requires Prediction
5/27/2013Dataiku 42
Lots of data
Some Database
To Perform
Rapid Calculations
And some database
...
The Medieval Fairy Land
5/27/2013Dataiku 43
Lots of data
Some Tools To
Do Some Maths
Some Other
To Do Some
Charts and some...
5/27/2013Dataiku 44
 Launch A Marketing
campaign
 After a few days
PREDICT based on
behaviours
◦  Total ARPU for users
after 3 months
◦  E...
A very large community
Some mid-size
communities
Lots of small clusters
mostly 2 players)
 Correlation
◦ between communit...
 Two-Way Clustering
◦ Assess customer behaviours
◦ Assess items equivalent classes
 Modeling + Simulation
◦ Evaluate fre...
Questions
5/27/2013Dataiku 48
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Conférence Laboratoire des Mondes Virtuels_Dataiku_Choix technologiques pour la mise en place d'un Data lab

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Capital Games organise une conférence le mercredi 22 mai, de 9h à 17h au Centre de Conférences de Microsoft, à Issy-les-Moulineaux. Elle permettra aux professionnels du jeu vidéo de monter en compétences sur les nouvelles méthodes de production de jeux connectés, parmi lesquelles l'analyse de données.
Présentation du cabinet Altana sur la Réglementation des données.

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Conférence Laboratoire des Mondes Virtuels_Dataiku_Choix technologiques pour la mise en place d'un Data lab

  1. 1. 1Dataiku5/27/2013
  2. 2. 5/27/2013Dataiku 2 Collocation Big Apple Big Mama 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. 3. The Stakes - Summary 5/27/2013Dataiku 3 Million Events Billion $ Billion Events Million $ Classic Business Social Gaming
  4. 4. Meet Hal Alowne 5/27/2013Dataiku 4 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. 5. MERIT = TIME + ROI 5/27/2013Dataiku 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. 6. Our Goal 5/27/2013Dataiku 6 It’s utterly complex and unreasonable
  7. 7. Our Goal 5/27/2013Dataiku 7 It’s utterly complex and unreasonable Our Goal: Change his perspective on data science projects (sorry, we couldn’t find a picture of Hal Smiling)
  8. 8.  Do the Basics  Understand Analytics  What to expect out of analytics Quick Agenda 5/27/2013Dataiku 8
  9. 9. 5/27/2013Dataiku 9
  10. 10.  Do you track ? ◦ Customer Goals For most important features ◦ Time Spent Level Progresison Money Spent ◦ Campaigns and generated campaign Value 5/27/2013Dataiku 10 Suggestion #1 Check The Basics
  11. 11.  Do A/B Tests ◦ 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 5/27/2013Dataiku 11 Suggestion #2 DO A/B Tests (and not yourself)
  12. 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 5/27/2013Dataiku 12 Some Results A/B Tests
  13. 13. Statistical Signifiance 5/27/2013Dataiku 13 http://visualwebsiteoptimizer.com/ab-split-significance-calculator/
  14. 14.  Can be Built on top of your production systems  Do you have ◦ Cohorts ◦ Daily $$ Reports ◦ Basic $$ Segments 5/27/2013Dataiku 14 Suggestion #3 Have 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, evey day ◦ Rolling last 30 days ARPUU ? ◦ Rolling last 30 days DAY ?  Do you follow every week ◦ The Segment Conversion Rate per source ? 5/27/2013Dataiku 15 Sample Check list (Gaming)
  16. 16. Embodiment of Knowledge 5/27/2013Dataiku 16
  17. 17.  Product Success driven by Quality  Margin / Customer Value / Traffic / Acquisition 5/27/2013Dataiku 17 At the Beginning
  18. 18.  Margin for new customers might decline …  Margin for new features might decline …  Is your business really scalable ? 5/27/2013Dataiku 18 But when you continue growing
  19. 19.  Existing Customers  Existing Product Assets  Existing Specific Business Model  And your KNOWLEDGE of it 5/27/2013Dataiku 19 Where is your core business advantage ?
  20. 20. 5/27/2013Dataiku 20 Data Driven Business 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. 21. 5/27/2013Dataiku 21 Apply It ! Product Optimization Customer Acquisition Optimization Recommender/ Targeting for newsletters
  22. 22.  Dark Side ◦ Technology  Bright Side ◦ Business 5/27/2013Dataiku 22 How ?
  23. 23. The Dark Side 5/27/2013Dataiku 23
  24. 24. Technology is complex 5/27/2013Dataiku 24 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. 25. Machine learning is complex 5/27/2013Dataiku 25  Find People that understand machine learning and all this stuff  Try to understand myself
  26. 26. Plumbing is not complex (but difficult) 5/27/2013Dataiku 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
  27. 27. The Bright Side 5/27/2013Dataiku 27
  28. 28.  People  Microsoft Excel 5/27/2013Dataiku 28 How did you build your great product ?
  29. 29.  Data Team  Data Tools 5/27/2013Dataiku 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. 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 5/27/2013Dataiku 30
  31. 31. Short Term Focus Long 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 5/27/2013Dataiku 31
  32. 32. Data ! Product Designer Business & Marketing Engineers User Voice Data Innovation: fill the gap! 5/27/2013Dataiku 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. 33. Prepare for some Geeky Porn 5/27/2013Dataiku 33
  34. 34. Classic Columnar Architecture 5/27/2013Dataiku 34 Some data Some Place To Pour It In Some Tool To To Some Maths And Graphs
  35. 35. Classic Columnar Architecture 5/27/2013Dataiku 35 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 …)
  36. 36. The Corinthian Architecture 5/27/2013Dataiku 36 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
  37. 37. The Corinthian Architecture 5/27/2013Dataiku 37 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
  38. 38. The Corinthian Architecture 5/27/2013Dataiku 38 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
  39. 39. The One Database won’t make it all problem 5/27/2013Dataiku 39 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..
  40. 40. The Roman Social Forum 5/27/2013Dataiku 40 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
  41. 41. The Key Value Store 5/27/2013Dataiku 41 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
  42. 42. Action requires Prediction 5/27/2013Dataiku 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 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 ?
  43. 43. The Medieval Fairy Land 5/27/2013Dataiku 43 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
  44. 44. 5/27/2013Dataiku 44
  45. 45.  Launch A Marketing 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 45
  46. 46. A very large community 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 5/27/2013Dataiku 46
  47. 47.  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 5/27/2013Dataiku 47 Business Model User Profiling Simulation
  48. 48. Questions 5/27/2013Dataiku 48
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