Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling by Usama Fayyad

Big
Data
Analy,cs:

Applica,ons
&
Opportuni,es
in
On-‐line

Predic,ve
Modeling

Usama
Fayyad,
Ph.D.

Chairman
&
CTO

ChoozOn
Corpora/on

Twi$er:
@usamaf

August
12,
2012

BigMine:
BigData
Mining
Workshop

KDD-‐2012
–
Beijing,
China

1

Outline

•  Big
Data
all
around
us

•  IntroducIon
to
Data
Mining
and
PredicIve
AnalyIcs

•  On-‐line
data
and
facts

•  Case
studies
from
mulIple
verIcals:

1.  Yahoo!
Big
Data

2.  Social
Network
Data

3.  Case
Study
from
nPario
ApplicaIons

4.  ChoozOn
Big
Data
from
oﬀers

•  High-‐level
view:
don’t
forget
the
basics

•  Summary
and
conclusions

2

What
Ma3ers
in
the
Age
of
Analy/cs?

1. Being
Able
to
exploit
all
the
data
that
is
available

•  not
just
what
you've
got
available

•  what
you
can
acquire
and
use
to
enhance
your
acIons

2. 
ProliferaIng
analyIcs
throughout
your

organizaIon

•  make
every
part
of
your
business
smarter

3. 
Driving
signiﬁcant
business
value

•  embedding
analyIcs
into
every
area
of
your
business

can
help
you
drive
top
line
revenues
and/or
bo]om

line
cost
eﬃciencies

3

What
OrganizaIons
Are
Struggling
With

•  Data
Strategy
-‐

how
much
data?
why
data?
how
does
it

impact
my
business?

•  PrioriIzaIon
conducted
based
on
business
need,
not
IT

–  Business
jusIﬁcaIons
for
Big
Data

–  DemonstraIng
value
of
data
in
impacIng
the
business

–  Looking
at
specialized
stores
to
reduce
TCO

–  File
systems
for
grid
compuIng
(Hadoop)

•  We
do
need
to
stay
on
top
of
our
basic
business
ops

–  billing,
monitoring,
inventory
management,
etc...

–  Most
can
be
handled
by
stream
processing
and
tradiIonal
BI

•  But,
a
new
generaIon
of
requirements
are
becoming
a

priority
for
data-‐driven
business

–  PredicIve
analyIcs,
advanced
forecasIng,
automated
detecIon
of

events
of
interest

4

Why
Big
Data?

A
new
term,
with
associated
“Data
Scien:st”
posi:ons:

•  Big
Data:
is
a
mix
of
structured,
semi-‐structured,
and

unstructured
data:

–  Typically
breaks
barriers
for
tradiIonal
RDB
storage

–  Typically
breaks
limits
of
indexing
by
“rows”

–  Typically
requires
intensive
pre-‐processing
before
each
query

to
extract
“some
structure”
–
usually
using
Map-‐Reduce
type

operaIons

•  Above
leads
to
“messy”
situaIons
with
no
standard

recipes
or
architecture:
hence
the
need
for
“data

scienIsts”

–  conduct
“Data
ExpediIons”

–  Discovery
and
learning
on
the
spot

5

What
Makes
Data
“Big
Data”?

•  Big
Data
is
Characterized
by
the
3-‐V’s:

–  Volume:
larger
than
“normal”
–
challenging
to
load/process

•  Expensive
to
do
ETL

•  Expensive
to
ﬁgure
out
how
to
index
and
retrieve

•  MulIple
dimensions
that
are
“key”

–  Velocity:
Rate
of
arrival
poses
real-‐/me
constraints
on
what

are
typically
“batch
ETL”
opera/ons

•  If
you
fall
behind
catching
up
is
extremely
expensive
(replicate
very

expensive
systems)

•  Must
keep
up
with
rate
and
service
queries
on-‐the-‐ﬂy

–  Variety:
Mix
of
data
types
and
varying
degrees
of
structure

•  Non-‐standard
schema

•  Lots
of
BLOB’s
and
CLOB’s

•  DB
queries
don’t
know
what
to
do
with
semi-‐structured
and

unstructured
data.

6

The
DisIncIon
between
“Data”
and
“Big

Data”
is
fast
disappearing

•  Most
real
data
sets
nowadays
come
with
a
serious
mix
of

semi-‐structured
and
unstructured
components:

–  Images

–  Video

–  Text
descripIons
and
news,
blogs,
etc…

–  User
and
customer
commentary

–  ReacIons
on
social
media:
e.g.
Twi]er
is
a
mix
of
data
anyway

•  Using
standard
transforms,
enIty
extracIon,
and
new

generaIon
tools
to
transform
unstructured
raw
data
into

semi-‐structured
analyzable
data

•  Hadoop
vs.
Not
Hadoop
-‐

when
to
use
what
kind
of

techniques
requiring
Map-‐Reduce
and
grid
compuIng

7

Text
Data:
The
Big
Driver

•  While
we
speak
of
“big
data”
and
the
“Variety”
in
3-‐V’s

•  Reality:
biggest
driver
of
growth
of
Big
Data
has
been
text

data

•  In
fact
Map-‐Reduce
became
popularized
by
Google
to
address

the
problem
of
processing
large
amounts
of
text
data:

–  Indexing
a
full
copy
of
the
web

–  Frequent
re-‐indexing

–  Many
operaIons
with
each
being
a
simple
operaIon
but
done
at
large

scale

•  Most
work
on
analysis
of
“images”
and
“video”
data
has
really

been
reduced
to
analysis
of
surrounding
text

8

To
Hadoop
or
not
to
Hadoop?

when
to
use
techniques
requiring
Map-‐Reduce
and
grid
compu,ng?

•  Typically
organizaIons
try
to
use
Map-‐Reduce
for
everything
to

do
with
Big
Data

–  This
is
actually
very
inefficient
and
oqen
irraIonal

–  Certain
operaIons
require
specialized
storage

•  UpdaIng
segment
memberships
over
large
numbers
of
users

•  Defining
new
segments
on
user
or
usage
data

•  Map-‐Reduce
is
useful
when
a
very
simple
operaIon
is
to
be

applied
on
a
large
body
of
unstructured
data

–  Typically
this
is
during
enIty
and
a]ribute
extracIon

–  SIll
need
Big
Data
analysis
post
Hadoop

•  Map-‐Reduce
is
not
efficient
or
effecIve
for
tasks
involving
deeper

staIsIcal
modeling

– 
good
for
gathering
counts
and
simple
(sufficient)
staIsIcs

•  E.g.
how
many
Imes
a
keyword
occurs,
quick
aggregaIon
of
simple
facts
in
unstructured

data,
esImates
of
variances,
density,
etc…

–  Mostly
pre-‐processing
for
Data
Mining

9

Hadoop
Use
Cases
by
Data
Type

ERP
Financial
Data
Supply
Chain
Data

Sensor
Data
Financial
Trading
Data

1%
2%

2%
4%

CRM
Data

4%

Science
Data

7%

Content
and
Preference
Data

24%

AdverIsing
Data

10%

IT
Log
Data

19%

Social
Data

11%

Text
and
Language
Data

16%

12

Big
Data
Applica:ons
and
Uses

100%
Capture

IT
Log
&
Security

Forensics
&
AnalyIcs
Find
New
Signal
Predict
Events

Product
Planning

Automated
Device
Data

Failure
Analysis

AnalyIcs

ProacIve
Fixes

RecommendaIon

AdverIsing
SegmentaIon

AnalyIcs

Social
Media

Hadoop
+

MPP
+
EDW

Big
Data
Cost
ReducIon
Ad
Hoc
Insight

Warehouse
AnalyIcs

PredicIve
AnalyIcs

13

Analysis
&
Programming
Soqware

PIG

HIPI

14

From
Basic
Dashboards
to
Advanced
Analy:cs

•  Data
ReducIon
to
get

–  Advanced
views
oriented
by
customer
or
product

–  SegmentaIon

–  Pa]ern
analysis
and
summaries

•  PredicIve
AnalyIcs

–  Data
Mining

–  StaIsIcal
analysis

–  OpImizaIon
of
processes
and
spend

The
same
analyIcs
technique
apply
across

many
industries:
fraud
detecIon
is
fraud

detecIon,
is
fraud
detecIon

16

What
is
Data
Mining?

Finding
interes,ng
structure
in
data

•  Structure:
refers
to
staIsIcal
pa]erns,
predicIve
models,
hidden

relaIonships

•  Interes/ng:
Accurate
predicIons,
associated
with
new
revenue

potenIal,
associated
with
cost
savings,
enables
opImizaIon

•  Examples
of
tasks
addressed
by
Data
Mining

–  PredicIve
Modeling
(classificaIon,
regression)

–  SegmentaIon
(Data
Clustering
)

–  Affinity
(SummarizaIon)

•  relaIons
between
fields,
associaIons,
visualizaIon

17

Data
Mining
and
Databases

Many
interesIng
analysis
queries
are
diﬃcult
to
state

precisely

•  Examples:

–  which
records
represent
fraudulent
transac:ons?

–  which
households
are
likely
to
prefer
a
Ford
over
a
Toyota?

–  Who
is
a
good
credit
risk
in
my
customer
DB?

–  Why
are
these
automobiles
in
need
of
unusual
repairs?

•  Yet
database
contains
the
informaIon

–  good/bad
customer,
proﬁtability

–  did/didn’t
respond
to
mailout/survey/campaign/...

–  automobile
repair
and
warranty
records

18

Many
Business
Uses

Analy:c
technique
Uses
in
business

Marke:ng
and
sales

IdenIfy
potenIal
customers;
establish
the

effecIveness
of
a
campaign

Understanding
customer
behavior
model
churn,
affiniIes,
propensiIes,
…

Web
analy:cs
&
metrics
model
user
preferences
from
data,
collaboraIve

filtering,
targeIng,
etc.

Fraud
detec:on
IdenIfy
fraudulent
transacIons

Credit
scoring
Establish
credit
worthiness
of
a
customer
requesIng
a

loan

Manufacturing
process
analysis
IdenIfy
the
causes
of
manufacturing
problems

PorWolio
trading
opImize
a
porvolio
of
financial
instruments
by

maximizing
returns
&
minimizing
risks

Healthcare
Applica:on
fraud
detecIon,
cost
opImizaIon,
detecIon
of
events

like
epidemics,
etc...

Insurance
fraudulent
claim
detecIon,
risk
assessment

Security
and
Surveillance
intrusion
detecIon,
sensor
data
analysis,
remote

sensing,
object/person
detecIon,
link
analysis,
etc...

19

So
this
Internet
thing
is
going
to

be
big!

Big
opportunity,
Big
Data,
Big

Challenges!

20

Stats
about
on-‐line
usage

•  How
many
people
are
on-‐line
today?

–  2.1
Billion
(per
Comscore
esImates)

–  30%
of
world
PopulaIon

•  How
much
Ime
is
spent
on-‐line
per
month
by

the
whole
world?

–  4M
person-‐years
per
month

•  How
many
hours
per
month
per
Internet
User?

–  16
hours
(global
average)

–  32
hours
(U.S.
Average)

*Sources: Feb.2012 - from Go-Gulf.com compiled from Comscoredatamine.com, Nielsen.com,
thisDigitalLife.com, PewInternet.org
21

How
Are
Users
Distributed
Geographically

thisDigitalLife.com, PewInternet.org 22

How Do People Spend Their On-line Time?

•  On-line Shopping? 5%
•  Searches? 21%
•  Email/Communication? 19%
•  Reading Content? 20%
•  Social Networking? 22%
•  Multimedia Sites? 13%
23

Most
Popular
AcIviIes
On-‐Line?

thisDigitalLife.com, PewInternet.org
24

Top
10
Sites
Visited?

•  Google:

153.4M
visitors/month

–  each
spending
1h
47mins

•  Facebook:
137.6M
visitors
per
month

–  each
spending
7h
50mins

25

InteresIng
Events

•  Google:

How
many
queries
per
day?

–  More
than
1
Billion

•  Twi$er:
How
many
Tweets/day?

–  More
than
250M

•  Facebook:
Updates
per
day?

–  More
than
800M

•  YouTube:
Views/day

–  4
Billion
views

–  60
hours
of
video
uploaded
every
minute!

•  Social
Networks:
users
who
have
used
sites
for
spying

on
their
partners?

–  56%

*Sources: Feb.2012 - from Go-Gulf.com compiled from Comscoredatamine.com,
Nielsen.com, thisDigitalLife.com, PewInternet.org
26

InteresIng
Events

•  Country
with
Highest
online
friends?

–  Brazil

–  481
friends
per
user

–  Japan
has
least
at
29

•  Country
with
maximum
Ime
spent
shopping
on-‐line??

–  China:
5
hours/week

*Sources: Feb.2012 - from Go-Gulf.com compiled from Comscoredatamine.com,
Nielsen.com, thisDigitalLife.com, PewInternet.org
27

So
Internet
is
a
big
place
with

lots
happening?

Do
we
understand
what
each
individual
is
trying
to

achieve?

•  What
is
user
intent?

•  Cri/cal
in
mone/za/on,
adver/sing,
etc…

Do
we
understand
what
a
community’s
sen:ment
is?

• 
What
is
the
emoIon?

• 
Is
it
negaIve
or
posiIve?

• 
What
is
the
health
of
my
brand
online?

Do
we
understand
context
and
content?

• 
What
are
appropriate
ads?

• 
Is
it
Ok
to
associate
my
brand
with
this
content?

•  Is
content
sad?,
happy?,
serious?,
informaIve?

28

Case
Studies

Yahoo!
Big
Data

29

Yahoo!
–
One
of
Largest

DesInaIons
on
the
Web

More people visited
Yahoo! in the past
month than:"
"

80%
of
the
U.S.
Internet
popula:on
uses
Yahoo!

•  Use coupons"
–
Over
600
million
users
per
month
globally!
•  Vote"

•  Recycle"
•  Global
network
of
content,
commerce,
media,
search
and
access

•  Exercise regularly"
products
•  Have children
•  100+
properIes
including
mail,
TV,
news,
shopping,
ﬁnance,
living at home"
autos,
travel,
games,
movies,
health,
etc.
•  Wear sunscreen
regularly"
•  25+
terabytes
of
data
collected
each
day

•  RepresenIng
1000’s
of
cataloged
consumer
behaviors

Data is used to develop content, consumer, category and campaign
insights for our key content partners and large advertisers

30

Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005.

Yahoo!
Big
Data
–
A
league
of
its
own…

Terrabytes of Warehoused Data
Millions of Events Processed Per Day

14,000 5,000

1,000
500
2,000 25 49 94 100
50 120 225

Amazon

Walmart

warehouse
Telecom

Warehouse
AT&T

Y! Panama
Y! LiveStor
Korea

Y! Main
SABRE VISA NYSE YSM Y! Global

GRAND CHALLENGE PROBLEMS OF DATA PROCESSING
TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET

Y! Data Challenge Exceeds others by 2 orders of magnitude

31

Behavioral
TargeIng
(BT)

Search

Content

BT

Search Clicks

Ad Clicks

Targe:ng
your
ads
to

consumers
whose
recent

behaviors
online
indicate

that
your
product
category
is

32
relevant
to
them

Yahoo!
User
DNA

Registration Campaign Behavior Unknown

Clicked on Checks Yahoo!
Lawyer Mail daily via
Lives in SF Sony Plasma TV Searched on:
“Hillary Clinton” PC & Phone Searched on:
SS ad
“Italian
restaurant
Palo Alto”

Searched on
Male, age 32 from London
Spends 10 hour/week
Purchased Da Has 25 IM Buddies,last week
On the internet
Vinci Code from Moderates 3 Y!
Amazon Groups, and hosts a
360 page viewed by
10k people

•  On a per consumer basis: maintain a behavioral/interests profile and
profitability (user value and LTV) metrics

33

How
it
works
|
Network
+
Interests
+
Modelling

Varying ProductRelevant Users
Identify Users to the Models
Rewarding Good Behaviour
Match Most Purchase Cycles Analyze predictive patterns for purchase
cycles in over 100 product categories

In each category, build models to describe
behaviour most likely to lead to an ad
response (i.e. click).

Score each user for fit with every
category…daily.

Target ads to users who get highest
‘relevance’ scores in the targeting
categories

34

Recency
Ma]ers,
So
Does
Intensity

Active now… …and with feeling

35

DiﬀerenIaIon
|
Category
speciﬁc
modelling

Example 1: Category Automotive Example 2: Category Travel/Last Minute

Intense Click Zone
intensity score

intensity score

Intense Click Zone
time time

Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click

Different models allow us to weight and determine intensity and recency

36

DiﬀerenIaIon
|
Category
speciﬁc
modelling

Example 1: Category Automotive
user is in the Intense Click Zone
intensity score

Intense Click Zone

with no further activity, decay takes effect time

Alt Behaviour 1: 5 pages, 2 search keywords, 1 search click, 1 ad click

Different models allow us to weight and determine intensity and recency

37

Automobile
Purchase
Intender
Example

•  A
test
ad-‐campaign
with
a
major
Euro
automobile
manufacturer

–  Designed
a
test
that
served
the
same
ad
creaIve
to
test
and
control
groups

on
Yahoo

–  Success
metric:
performing
speciﬁc
acIons
on
Jaguar
website

•  Test
results:
900%
conversion
liq
vs.
control
group

–  Purchase
Intenders
were
9
Imes
more
likely
to
conﬁgure
a
vehicle,
request

a
price
quote
or
locate
a
dealer
than
consumers
in
the
control
group

–  ~3x
higher
click
through
rates
vs.
control
group

38

Mortgage
Intender
Example

Results from a client campaign on Yahoo!
Example: Mortgages Network

+626%
Conv Lift

We found: +122%
1,900,000 people looking CTR Lift

for mortgage loans.
Example search terms qualified for this target:

Mortgages Home Loans Refinancing Ditech

Example Yahoo! Pages visited:
Financing section in Real Estate
Mortgage Loans area in Finance
Real Estate section in Yellow Pages

Source: Campaign Click thru Rate lift is determined by Yahoo! Internal
research. Conversion is the number of qualified leads from clicks over
number of impressions served. Audience size represents the audience

Date: March 2006 39

within this behavioral interest category that has the highest propensity to
engage with a brand or product and to click on an offer.

Experience summary at Yahoo!
•  Dealing with the largest data source in the world (25
Terabyte per day)
•  BT business was grown from $20M to about $500M
in 3 years of investment!
•  Building the largest database systems:
–  World’s largest Oracle data warehouse
–  World’s largest single DB
–  Over 300 data mart data
–  Analytics with thousands of KPI’s
–  Over 5000 users of reports
–  Largest targeting system in the world
•  Big demands for grid computing (Hadoop)
40

Social
Network

Social
Graph
Analysis
(no
Ime)

Social
Network
MarkeIng

Understanding
Context
for
Ads

41

Case Study: TWITTER Social Marketing?
Viacom’s VH1 Twitter campaign on ANVIL (the movie)

(week of May 14th , 2009 – see AdAge article)

Marketing ANVIL movie

Very niche audience, how do you reach them?

Diffusion

Give 20 free DVD’s to major related artists/groups, ask them

To notify Twitter groups – reached over 2M people

Social Identity: power of word of mouth...

What is the Cost to VH-1?

Compare with traditional approach: TV commercials to promote a
documentary ﬁlm?

42

42

The
Display
Ads
Challenge
Today

What
Ad
would
you

place
here?

43

The
Display
Ads
Challenge
Today

Damaging
to
Brand?

44

The
Display
Ads
Challenge
Today

What
Ad

would
you

place
here?

45

The
Display
Ads
Challenge
Today

Irrelevant
and

Damaging
to
Brand

Completely
Irrelevant

46

NetSeer:
Intent
for
Display

•  Currently
Processing
4
Billion
Impressions
per
Day

47

Problem:
Hard
to
Understand
User

Intent

Contextual
Ad
served
by
Google
What
NetSeer
Sees:

48

Case
Studies

nPario
–
Data
Management
Plavorm

ChoozOn
–
Big
Data
over
Oﬀers
Universe

49

Example
of
a
Big
Data
DMP

Scale
nPario builds an infinitely scalable data management platform (DMP) that
allows advertisers and marketers to manage, understand, and monetize their
data. Their technology has been proven at companies such as Yahoo and EA.

Applications
nPario applications include segmentation of audiences for increasing the value
of advertising, reporting/analytics for examining performance, attribution to
show which advertising works, and experimentation to test ideas.

Access
nPario emphasizes putting access to data in the hands of marketing,
advertising and other business users

50

Powerful
Technology.

540m
Users,
8+
Petabytes,

&
16
Patents

nPario has the only commercially In Production at Yahoo
available Big Data management nPario technology manages the world’s
technology built
for
one
of
the
“Big
Five”.
largest data system. Used for Yahoo’s
Marketing and Advertising business. Used
across Yahoo’s platforms and 120 online
Significant Investment properties. Used by hundreds of analysts
nPario’s technology is the result of more than
$50m investment in development and 16
issued patents.

51

DMP

Data Sources

First-Party Self-Service Data Intake & ETL Deep Insights & Big Data Analytics

Offline Data Processing, Insights
CRM
Data

Tag Mgmt
Enrichment and Analytics
Normalization
Events Modeling
& Discovery

Multiple Channels

Search eMail
Self-Service Applications
1
User DNA Segment Target
Mobile Video
IntegraIon
&
APIs

Display Site

Third-Party Real-Time
Data Availability Attribution Experimentation

Data Management Platform
52

52

nPario
Case
Study

Marketing to
100+ million Gamers

Challenge: Provide cross-platform campaign
insights for advertisers and enable audience
discovery across channels.

Result: Unified view of gamers “EA increased its worldwide audience reach by
30% this year […]. Combining that major jump in
across multiple cross platform data
reach with the launch of EA Legend puts us in
sources. perfect position to compete”

Dave Madden, Senior Vice President of Global
Pogo (online), sponsored content, Console Media Solutions
game interaction and ad interaction (Xbox, Electronic Arts.
PS3), Mobile, Playfish (facebook), external
sources (Collective Media, Comscore,
Omniture, Dynamic Logic)

53

Big
Data
in
AcIon:
EA
Legend

54

Specialized
Search
through
Big
Data

AnalyIcs
over
the
Oﬀers
Universe

55

55

Chaos
for

Consumers

Deep
Discount
Sites Coupon
Flash Sites
Sale
Sites
Loyalty
Programs

Daily
Deals
Sites
Consumer
Online
Loyalty
Program
Social s
Search Network
Engines s

56

Chaos
for

Marketers

Deep
Discou
nt Sites
Flash Coupon
Sale Sites
Sites
Loyalty
Program
s
Daily
Deals
Sites
Online
Loyalty
Progra
Social ms
Search
Networ
Engines
ks

57

What
Consumers
&
Marketers
Want

Consumers
•  Value from brands they love
•  Tame the deal chaos

Marketers
•  Reach targeted consumers
•  Build loyalty
•  Create brand evangelists
58

58

Keys
to
Being
THE
Consumer
Network

Comprehensive Personalization Intelligent
Deal Coverage Matching

Permission- Loyalty Multi-
based Solution Channel
Targeting
Reach
Solution

10/18/2010
ChoozOn
ConﬁdenIal

59

59

Solu:on
for
Consumers

Daily Deals Flash Sale Deep Affiliate Deals & Loyalty
Sites Sites Discounters Coupon Sites Programs

Choozer
Interests
&
Preferences

Machine- Intelligent
Social
based Matching
(Pals,Clubs)

Web App Mobile App Email Digital
Media

60

60

Carol’s
Consumer
Network

“Chozen”
Brands
Interests
(Intent)

Shopping
Pals

Deal
Clubs
61

What
Carol
Gets

Carol’s
Consumer
Network
The
Universe
of
Deals

Aﬃliate
Deals

Loyalty
Programs
1,500+
brands

Daily
Deals

Flash
Sales
100,000+
oﬀers

Deep
Discounters

PLUS her Inbox™

Intelligent Matching

A
Personal
Shopper
for
Deals

62

Big
Picture
on
Big
Data
AnalyIcs

Key
points

63

Don’t
Forget
The
Basics

•  Metrics
and
Scorecards
are
the
ﬁrst
steps
to

awareness

•  Plays
a
huge
role
in
deploying
predicIve
models
and

monitoring
and
proving
their
eﬀecIveness

•  Oqen
scorecards
require

–  Going
through
huge
amounts
of
data
to
produce
the
required
metrics

–  Ability
to
get
to
the
metrics
in
low
latency

–  Ability
to
modify
metrics
and
update
quickly

–  IntegraIon
with
data
warehouse

64

Focus
On
The
Right
Measure

Referral Site Metrics
2.5 •  Total
traffic
not
a
good

Response performance
measure

2.0
Conversion
•  High-‐traffic
referral
sites

1.5
oqen
produce
poorer

1.0 Margin quality
click
throughs

0.5 •  Ads
best
response
not

0.0 most
effecIve

Site 1 Site 2
•  Target
the
message

65

Focus
On
The
Right
Measure

2.5
•  Total
traﬃc
not
a
good

measure

2.0
referral
sites

1.5 Conversion
oqen
produce
poorer

1.0 Margin quality
click
throughs

0.5 •  Ads
best
response
not

0.0 most
eﬀecIve

Site 1 Site 2 •  Target
the
message

66

Focus
On
The
Right
Measure

2.5
•  Total
traﬃc
not
a
good

measure

2.0
referral
sites

1.5 Conversion
oqen
produce
poorer

1.0 Margin quality
click
throughs

0.5 •  Ads
best
response
not

0.0 most
eﬀecIve

Site 1 Site 2 •  Target
the
message

67

SomeImes,

Simple
is
Very
Powerful!

Retaining
New
Yahoo!
Mail
Registrants

IntegraIng
Mail
and
News

•  Data
showed
that
users
oqen
check
their
mail
and

news
in
the
same
session

–  But
no
easy
way
to
navigate
to
Y!
News
from
Y!
Mail

•  Mail
users
who
also
visit
Y!
News
are
3X
more
acIve

on
Yahoo

–  Higher
retenIon,
repeat
visits
and
Ime-‐spent
on

Yahoo

69

“In
the
news”
Module
on
Mail
Welcome
Page

•  Increased
retenIon
on
Mail
for
light
users
by
40%!

–  Est.
Incremental
revenue
of
$16m
a
year
on
Y!
Mail
alone

70

Beneﬁts
of
Advanced
AnalyIcs

•  Advanced
AnalyIcs
brings
out
the
real
value
of
data

•  The
business
begins
to
understand
the
true
value
and
role
of

data
in
moving
the
big
needles

•  Focus
on
useful
requirements
from
data,
rather
than
“data

acrobaIcs”

•  Value
creaIon
from
data
leads
to
proper
investment
scoping

–  Many
are
realizing
predicIve
analyIcs
and
data
mining

are
much

more
useful
than
reporIng

–  IntegraIon
of
analyIcs
story
with
data
storage
very
criIcal

•  Big
data
makes
analyIcs
even
more
essenIal
and
more
useful

–  Avoiding
the
challenges
of
separaIng
analyIcs
from
big
data
are

increasingly
important

71

Thank
You!

&

QuesIons?

Twi]er:
@usamaf

Usama@choozOn.net

www.ChoozOn.com

Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling by Usama Fayyad

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