Big Data + Social + Games @Is Cool

16/03/2012

TITRE DOCUMENT

Who is IsCool Entertainment?

Social game publisher based in Paris, France

#1 French publisher in terms of audience (450k Daily Active Users) & revenue

2.8 Millions Fans

80 employees

9.1 million € revenue in 2010

4 live applications on Facebook

Florian Douetteau CTO @fdouetteau

Agenda • What do we do? Social Gaming

• What kind of (Big) Analytics we do? Lots

• How we do it ? Hadoop, Python, R, Tableau, Geph and stuff…

Is Cool Games

IsCool, Delirious Collectible

Game

Absolute Solitaire, The best solitaire game available online

Temple Of Mahjong, Collect, Play, Exchange

Belote Multijoueur, 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

frustration !

Virtual Goods can be usually traded between players

Virtual and actual “Price” of a good

Let’s Trade 1 Watch against

3 Hammers

Why is this Big Data ?

Number of object transactions per day NYSE 3,600,000,000

IsCool 2,150,000,000

Nasdaq 1,600,000,000

Nikkey 1,500,000,000

Footsie 860,000,000

CAC 40 142,500,000

9,8 TB Data to analyze

18 Million users generated actions per day 7 Billions per year.

The Real Big Data Challenge Collaborate for collective insights

data scientist?

what metrics?

Realtime?

Game Designer Perspective : Nice Charts ?

Programmers’ Perspective : Log Files & Work ?

Business Guy Perspective: Revenue Forecast ?

BI Veteran: Schema Definition ?

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

Paying user conversion

ARPPU

Score

Use of feature A,B,C…

Key drivers??? Traffic

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

A very large community

Some mid size communities

Lots of small clusters ((mostly 2 players)

Use Case 3 : Analyze Long Terms effect of a feature

16/03/2012 TITRE DOCUMENT

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 )

… How

over the last 3 years

• Tools changed

• Scale changed

• Focus Changed

Analyzing the Offer

• Online Analytics Platform

• Commercial / Open Source ETL

• Commercial BI Visualization Software

• Commercial / Open Source databases (column stores)

• …

What we learned

Diversity

• There's no Hadoop+R Magic (Expertise, Entry Costs, Maintenance)

• There’s no XYZ Magical Product

Relativity

• Windows / Linux ? Cloud or on-premise ?

• Do you have internal data mining experts (yes/no) ?

• Do you have internal scalability experts (yes/no) ?

• What is _real_ budget ? 0K ? 10K ? 100K ? 1000K ?

Superciality

• Ability to display is more important than the result.

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 Elastric Map Reduce)

• Used to reduce the amount of information : 10 GB a day => 1GB a day

• High cost of development for "business" related processing

Open Source ETL (PyBabe)

• Pure Python ETL

• Good integration with AWS/ S3

• Easy to integrate in our development environment

Columnar Database (Infinidb, Open Source)

• Free (as beer)

• Good performance for analytics tasks on a few hundreds million lines ( SELECT … GROUP BY … ORDER … )

• Featured and limited performance compared to commercial Column Stores

Dashboarding (Tableau Software)

• +Direct connection to the database

• +Excel fan biz guy can use it with no training !

Questions ?