How Moore’s Law is Influencing Design - from Roadmap 2013
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
1. THE 3V’S OF BIG DATA: VARIETY, VELOCITY,
and VOLUME
SPEAKER: Nick Weir
CEO
ChoozON
Tuesday, November 27, 12
2. Mining
Big
Data
and
the
3V’s:
The
Business
and
Science
of
Big
Data
Nick
Weir,
Ph.D.
CEO
ChoozOn
Corpora,on
Twi$er:
@usamaf
March
21,
2012
GigaOM
Structure:
Data
Conference
New
York,
NY
Tuesday, November 27, 12
3. Outline
• Big
Data
all
around
us
• The
role
of
Data
Mining
and
PredicEve
AnalyEcs
• On-‐line
data
and
facts
• Case
studies
from
mulEple
verEcals:
1. Yahoo!
Big
Data
2. Social
Network
Data
3. ChoozOn
Big
Data
from
offers
Tuesday, November 27, 12
4. What
MaPers
in
the
Age
of
Big
Data?
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
acEons
2.
ProliferaEng
analyEcs
throughout
your
organizaEon
• make
every
part
of
your
business
smarter
3.
Driving
significant
business
value
• embedding
analyEcs
into
every
area
of
your
business
can
help
you
drive
top
line
revenues
and/or
boPom
line
cost
efficiencies
Tuesday, November 27, 12
6. Why
Big
Data?
A
new
term,
with
associated
“Data
Scien:st”
posi:ons:
Tuesday, November 27, 12
7. 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
tradiEonal
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
operaEons
Tuesday, November 27, 12
8. 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
tradiEonal
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
operaEons
• Above
leads
to
“messy”
situaEons
with
no
standard
recipes
or
architecture:
hence
the
need
for
“data
scienEsts”
–Conduct
“Data
ExpediEons”
–Discovery
and
learning
on
the
spot
Tuesday, November 27, 12
10. What
Makes
Data
“Big
Data”?
• Big
Data
is
Characterized
by
the
3-‐V’s:
–Volume:
larger
than
“normal”
–
challenging
to
load
and
process
• Expensive
to
do
ETL
• Expensive
to
figure
out
how
to
index
and
retrieve
• MulEple
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.
Tuesday, November 27, 12
11. The
DisEncEon
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
descripEons
and
news,
blogs,
etc…
– User
and
customer
commentary
– ReacEons
on
social
media:
e.g.
TwiPer
is
a
mix
of
data
anyway
• Using
standard
transforms,
enEty
extracEon,
and
new
generaEon
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
compuEng
Tuesday, November 27, 12
12. 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
operaEons
with
each
being
a
simple
operaEon
but
done
at
large
scale
• Most
work
on
analysis
of
“images”
and
“video”
data
has
really
been
reduced
to
analysis
of
surrounding
text
Tuesday, November 27, 12
13. To
Hadoop
or
not
to
Hadoop?
When
to
use
techniques
requiring
Map-‐Reduce
and
grid
compu?ng?
• Typically
organizaEons
try
to
use
Map-‐Reduce
for
everything
to
do
with
Big
Data
– Inefficient
and
ojen
irraEonal
– Certain
operaEons
require
specialized
storage
• UpdaEng
segment
memberships
over
large
numbers
of
users
• Defining
new
segments
on
user
or
usage
data
• Map-‐Reduce
is
useful
when
a
very
simple
operaEon
is
to
be
applied
on
a
large
body
of
unstructured
data
– Typically
this
is
during
enEty
and
aPribute
extracEon
– SEll
need
Big
Data
analysis
post-‐Hadoop
• Map-‐Reduce
is
not
efficient
or
effecEve
for
tasks
involving
deeper
staEsEcal
modeling
–
Good
for
gathering
counts
and
simple
(sufficient)
staEsEcs
• E.g.
how
many
Emes
a
keyword
occurs,
quick
aggregaEon
of
simple
facts
in
unstructured
data,
esEmates
of
variances,
density,
etc…
– Mostly
pre-‐processing
for
Data
Mining
Tuesday, November 27, 12
17. Big
Data
Applica:ons
and
Uses
100%
Capture
IT
Log
&
Security
Forensics
&
AnalyEcs Find
New
Signal Predict
Events
Product
Planning
Automated
Device
Data Failure
Analysis
AnalyEcs
ProacEve
Fixes
RecommendaEon
AdverEsing SegmentaEon
AnalyEcs Social
Media
Hadoop
+
MPP
+
EDW
Big
Data Cost
ReducEon Ad
Hoc
Insight
Warehouse
AnalyEcs
PredicEve
AnalyEcs
Tuesday, November 27, 12
18. So
this
Internet
thing
is
going
to
be
big!
Big
Opportunity,
Big
Data,
Big
Challenges!
Tuesday, November 27, 12
20. Stats
about
on-‐line
usage
• How
many
people
are
on-‐line
today?
– 2.1
Billion
(per
Comscore
esEmates)
– 30%
of
world
PopulaEon
Tuesday, November 27, 12
21. Stats
about
on-‐line
usage
• How
many
people
are
on-‐line
today?
– 2.1
Billion
(per
Comscore
esEmates)
– 30%
of
world
PopulaEon
• How
much
Eme
is
spent
on-‐line
per
month
by
the
whole
world?
– 4M
person-‐years
per
month
Tuesday, November 27, 12
22. Stats
about
on-‐line
usage
• How
many
people
are
on-‐line
today?
– 2.1
Billion
(per
Comscore
esEmates)
– 30%
of
world
PopulaEon
• How
much
Eme
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)
Tuesday, November 27, 12
23. Stats
about
on-‐line
usage
• How
many
people
are
on-‐line
today?
– 2.1
Billion
(per
Comscore
esEmates)
– 30%
of
world
PopulaEon
• How
much
Eme
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
Tuesday, November 27, 12
26. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
Tuesday, November 27, 12
27. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
Tuesday, November 27, 12
28. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
– More
than
250M
Tuesday, November 27, 12
29. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
– More
than
250M
• Facebook:
Updates
per
day?
Tuesday, November 27, 12
30. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
– More
than
250M
• Facebook:
Updates
per
day?
– More
than
800M
Tuesday, November 27, 12
31. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
– More
than
250M
• Facebook:
Updates
per
day?
– More
than
800M
• YouTube:
Views/day
Tuesday, November 27, 12
32. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$er:
How
many
Tweets/day?
– More
than
250M
• Facebook:
Updates
per
day?
– More
than
800M
• YouTube:
Views/day
– 4
Billion
views
– 60
hours
of
video
uploaded
every
minute!
Tuesday, November 27, 12
33. Volume,
Velocity,
Variety
• Google:
How
many
queries
per
day?
– More
than
1
Billion
• Twi$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%
Tuesday, November 27, 12
34. Volume,
Velocity,
Variety
• 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
Tuesday, November 27, 12
35. Turning
the
three
Vs
of
Big
Data
into
Value
Tuesday, November 27, 12
36. Turning
the
three
Vs
of
Big
Data
into
Value
Do
we
understand
what
each
individual
is
trying
to
achieve?
Tuesday, November 27, 12
37. Turning
the
three
Vs
of
Big
Data
into
Value
Do
we
understand
what
each
individual
is
trying
to
achieve?
• What
is
user
intent?
Tuesday, November 27, 12
38. Turning
the
three
Vs
of
Big
Data
into
Value
Do
we
understand
what
each
individual
is
trying
to
achieve?
• What
is
user
intent?
• Cri,cal
in
mone,za,on,
adver,sing,
etc…
Tuesday, November 27, 12
39. Turning
the
three
Vs
of
Big
Data
into
Value
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?
Tuesday, November 27, 12
40. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
Tuesday, November 27, 12
41. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
Tuesday, November 27, 12
42. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
•
What
is
the
health
of
my
brand
online?
Tuesday, November 27, 12
43. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
•
What
is
the
health
of
my
brand
online?
Do
we
understand
context
and
content?
Tuesday, November 27, 12
44. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
•
What
is
the
health
of
my
brand
online?
Do
we
understand
context
and
content?
•
What
are
appropriate
ads?
Tuesday, November 27, 12
45. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
•
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?
Tuesday, November 27, 12
46. Turning
the
three
Vs
of
Big
Data
into
Value
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
emoEon?
•
Is
it
negaEve
or
posiEve?
•
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?,
informaEve?
Tuesday, November 27, 12
47. Case
Studies
Yahoo!
Big
Data
18
Tuesday, November 27, 12
48. Yahoo!
–
One
of
Largest
DesEnaEons
on
the
Web
More people visited Yahoo!
in the past month than:
• Use coupons
80%
of
the
U.S.
Internet
popula:on
uses
Yahoo!
• Vote
–
Over
600
million
users
per
month
globally!
• Recycle
• Global
network
of
content,
commerce,
media,
search
and
access
products • Exercise regularly
• 100+
properEes
including
mail,
TV,
news,
shopping,
finance,
autos,
travel,
games,
movies,
• Have children living at
health,
etc. home
• Wear sunscreen regularly
• 25+
terabytes
of
data
collected
each
day
• RepresenEng
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
Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005.
Tuesday, November 27, 12
49. Yahoo!
Big
Data
–
A
league
of
its
own…
GRAND CHALLENGE PROBLEMS OF DATA PROCESSING
TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET
Y! Data Challenge Exceeds others by 2 orders of magnitude
Tuesday, November 27, 12
51. Behavioral
TargeEng
(BT)
Targe:ng
your
ads
to
consumers
whose
recent
behaviors
online
indicate
that
your
product
category
is
relevant
to
them
Tuesday, November 27, 12
52. Yahoo!
User
DNA
• On a per consumer basis: maintain a behavioral/interests
profile and profitability (user value and LTV) metrics
Tuesday, November 27, 12
53. How
it
works
|
Network
+
Interests
+
Modelling
Tuesday, November 27, 12
54. How
it
works
|
Network
+
Interests
+
Modelling
Varying Product Purchase Cycles Analyze predictive patterns for purchase cycles in over
100 product categories
Tuesday, November 27, 12
55. How
it
works
|
Network
+
Interests
+
Modelling
Match Users to the Models 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).
Tuesday, November 27, 12
56. How
it
works
|
Network
+
Interests
+
Modelling
Rewarding Good Behaviour 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.
Tuesday, November 27, 12
57. How
it
works
|
Network
+
Interests
+
Modelling
Identify Most Relevant Users 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
Tuesday, November 27, 12
58. Recency
MaPers,
So
Does
Intensity
Active now… …and with feeling
Tuesday, November 27, 12
63. Automobile
Purchase
Intender
Example
• A
test
ad-‐campaign
with
a
major
Euro
automobile
manufacturer
– Designed
a
test
that
served
the
same
ad
creaEve
to
test
and
control
groups
on
Yahoo
– Success
metric:
performing
specific
acEons
on
Jaguar
website
• Test
results:
900%
conversion
lij
vs.
control
group
– Purchase
Intenders
were
9
Emes
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
Tuesday, November 27, 12
64. Mortgage
Intender
Example
Results from a client campaign on Yahoo! Network
Example: Mortgages
+626%
Conv Lift
We found: +122%
CTR Lift
1,900,000 people looking Y! Finance Audience Mortgage Loans Target Mortgage Loans Target
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 within this behavioral interest category that has the highest propensity to
engage with a brand or product and to click on an offer.
Date: March 2006
Tuesday, November 27, 12
65. 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)
Tuesday, November 27, 12
66. Social
Network
Social
Network
MarkeEng
Understanding
Context
for
Ads
Tuesday, November 27, 12
67. 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?
Tuesday, November 27, 12
68. 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
Tuesday, November 27, 12
69. 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?
Tuesday, November 27, 12
70. The
Display
Ads
Challenge
Today
Damaging to Brand Irrelevant and Damaging to Brand
Mismatch
Completely Irrelevant
Tuesday, November 27, 12
71. NetSeer:
Intent
for
Display
• And
Currently
Processing
4
Billion
Impressions
per
Day
Tuesday, November 27, 12
72. NetSeer:
Intent
for
Display
• And
Currently
Processing
4
Billion
Impressions
per
Day
Tuesday, November 27, 12
73. NetSeer:
Intent
for
Display
• And
Currently
Processing
4
Billion
Impressions
per
Day
Tuesday, November 27, 12
74. NetSeer:
Intent
for
Display
• And
Currently
Processing
4
Billion
Impressions
per
Day
Tuesday, November 27, 12
75. Problem:
Hard
to
Understand
User
Intent
Contextual
Ad
served
by
Google
Tuesday, November 27, 12
76. Problem:
Hard
to
Understand
User
Intent
Contextual
Ad
served
by
Google What
NetSeer
Sees:
Tuesday, November 27, 12
79. Chaos
for
Consumers
Deep
Discount
Sites Coupon
Flash Sites
Sale Sites
Loyalty
Programs
Daily Deals
Sites
Consumer
Online
Loyalty
Programs
Social
Search Networks
Engines
Tuesday, November 27, 12
80. The
Business
Model
behind
ChoozOn’s
Use
of
Big
Data
Tuesday, November 27, 12
81. The
Business
Model
behind
ChoozOn’s
Use
of
Big
Data
Consumers
• Value from brands they love
• Tame the deal chaos
Tuesday, November 27, 12
82. The
Business
Model
behind
ChoozOn’s
Use
of
Big
Data
Consumers
• Value from brands they love
• Tame the deal chaos
Marketers
• Reach targeted consumers
• Build loyalty
• Create brand evangelists
Tuesday, November 27, 12
83. The
Business
Model
behind
ChoozOn’s
Use
of
Big
Data
Consumers
• Value from brands they love
• Tame the deal chaos
Marketers
• Reach targeted consumers
• Build loyalty
• Create brand evangelists
Tuesday, November 27, 12
89. What
Carol
Gets
Carol’s
Consumer
Network
Tuesday, November 27, 12
90. What
Carol
Gets
Carol’s
Consumer
Network The
Universe
of
Deals
Affiliate
Deals
Loyalty
Programs
Daily
Deals
Flash
Sales
Deep
Discounters
Intelligent Matching
A
Personal
Shopper
for
Deals
Tuesday, November 27, 12
91. What
Carol
Gets
Carol’s
Consumer
Network The
Universe
of
Deals
Affiliate
Deals
Loyalty
Programs 1,600+
brands
Daily
Deals
Flash
Sales 100,000+
offers
Deep
Discounters
Intelligent Matching
A
Personal
Shopper
for
Deals
Tuesday, November 27, 12
92. What
Carol
Gets
Carol’s
Consumer
Network The
Universe
of
Deals
Affiliate
Deals
Loyalty
Programs 1,600+
brands
Daily
Deals
Flash
Sales 100,000+
offers
Deep
Discounters
PLUS her Inbox™
Intelligent Matching
A
Personal
Shopper
for
Deals
Tuesday, November 27, 12