Big Data + Social + Games @Is Cool
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Big
Data
+
Social
+
Games
@Is
Cool
3/24/12
TITRE
DOCUMENT
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)
AnalyKcs
we
do
audience
(450k
Daily
AcKve
Lots
Users)
&
revenue
• How
we
do
it
Hadoop,
Python,
R,
Tableau,
Gephi
and
stuff…
! 2.8
Million
Fans
! 80
employees
Florian
DoueTeau
! €9.1
million
revenue
in
2010
CTO
! 4
live
applicaKons
on
Facebook
@fdoue?eau
Is
Cool
Games
Is
Cool,
Absolute
Solitaire,
Delirious
CollecKble
The
best
solitaire
game
Game
available
online
Temple
Of
Mahjong,
Belote
MulKjoueur,
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
moneKzaKon
! Virtual
Goods
must
not
be
hard
to
get
§ People
would
churn
because
of
Let’s
Trade
1
Watch
against
frustraKon!
3
Hammers
! Virtual
Goods
can
usually
be
traded
between
players
! Virtual
and
actual
“Price”
of
a
good
Why
is
this
Big
Data
?
! Number
of
object
transacKons
per
day
§ NYSE
3,600,000,000
18
Million
user-‐generated
acKons
per
day
§
1,600,000,000
7
Billion
per
year.
§
1,500,000,000
9,8
TB
Data
to
§ IsCool
1,400,000,000
analyze
§
860,000,000
§ CAC
40
142,500,000
The
Real
Big
Data
Challenge
Collaborate
for
collecQve
insights
Programmers’
PerspecKve
:
Game
Designer
PerspecKve
:
Log
Files
&
Work
?
Nice
Charts
?
Real-‐
Kme?
what
metrics?
data
scienKst?
BI
Veteran:
Business
Guy
PerspecKve:
Schema
DefiniKon
?
Revenue
Forecast
?
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
!
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
Understanding
Engagement
-‐
Results
! Establish
class
of
users
with
different
engagement
profile
and
use
of
features
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
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..
?
Understanding
Users
as
a
Whole
–
Clusters
and
Graphs
Lots
of
small
clusters
mostly
2
Some
mid-‐size
communi6es
! Specific
communiKes
in
the
players)
graph
! CorrelaKon
between
community
size
and
engagement
/
virality
! DetecKon
of
paTerns
A
very
large
community
§ 2
players
paTerns
§ Family
play
§ Group
Play
§ Open
Play
(language
community)
Understanding
Users
as
a
Whole
-‐
Benefits
! 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”
3/24/12
Use
Case
3
:
Long
Terms
effects
of
a
feature
! 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)
TITRE
DOCUMENT
3/24/12
Long
Terms
Effects
of
a
feature
-‐
Results
! Adapt
game
rules
to
fit
most
of
the
players
§ No
InflaKon
§ But
maintain
Growth
!!
3/24/12
How
did
we
do
that
?
In
the
past
4
years
….
Technological
Offering
• Tools
changed
• Commercial
/
Open
Source
ETL
• Scale
changed
• Commercial
BI
VisualizaKon
• Focus
Changed
Sooware
• Commercial
/
Open
Source
databases
(column
stores)
• …
2008-‐2009
2009-‐2010
2010-‐2011
• Basic
Approach
• BI
Approach
• Big
Data
Approach
What
we
learned
No
Hadoop+R
Magic
VisualizaQon
is
more
important
than
precision
Do
you
want
anybody
to
No
XYZ
Magical
Product
play
with
the
CollaboraKon
Diversity
data
?
Do
you
have
data
mining
experts
(yes/no)
?
What
is
real
Do
you
have
scalability
budget
?
RelaKvity
experts
?
Windows
/
Linux
?
Cloud
or
on-‐premise
?
AdapQve
AnalyQcs
! Day-‐To-‐Day
-‐
SaaS
AnalyKcs
Plarorms
§ For
common,
business
metrics
(virality,
traffic,
engagement)
§ Corporate
Level
Visibility
§ Day-‐to-‐day
! Week-‐to-‐Week
-‐
Datawarehousing
§ Detailed
Business
Metrics
§ Virtual
Economy
Modeling
§ Long-‐term
behaviours
§ Business
Level
Visibility
! Ad-‐hoc
-‐
Datamining
tools
§ To
Discover
new
trends
§ Ad-‐hoc
analyKcs
§ Graph
AnalyKcs
Internal
Data
Warehousing
Columnar
VisualizaKon
MapReduce
ETL
(PyBabe)
Database
(Tableau
(Hadoop/Hive)
(Infinidb)
Sooware)
• Used
to
reduce
the
• Pure
Python
ETL
• Free
(as
beer)
• +Direct
connecKon
amount
of
• Good
integraKon
• Good
performance
to
the
database
informaKon
:
10
GB
with
AWS/
S3
for
analyKcs
tasks
on
• +Excel
fan
biz
guy
a
day
=>
1GB
a
day
• Easy
to
integrate
in
a
few
hundreds
can
use
it
with
no
• High
cost
of
our
development
million
lines
(
SELECT
training
!
development
for
environment
…
GROUP
BY
…
business-‐related
ORDER
…
)
processing
• Featured
and
limited
performance
compared
to
commercial
Column
Stores
?
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