Two case studies in Digital Advertising

1.
Copyright
©
2013.
Tiger
Analy7cs
Using
Advanced
Analy7cs
to
bring
Business
Value
Two
case
studies
in
Digital
Adver7sing
_________________________
Mahesh
Kumar
CEO,
Tiger
Analy6cs

2.
Copyright
©
2013.
Tiger
Analy7cs
Tiger
Analy7cs
• Bou6que
consul6ng
firm
solving
business
problems
using
advanced
data
analy6cs
• Focus
areas
– Digital
adver6sing
and
Social
Media
– Marke6ng
and
Customer
Analy6cs
– Retail
and
CPG
– Transporta6on
• Offices
in
Bay
area,
North
Carolina,
and
India

3.
Copyright
©
2013.
Tiger
Analy7cs
• Display
Adver6sing
through
Real-­‐6me
bidding
(RTB)
– Background
on
RTB
– Business
problem:
improve
CTR
– Solu6on
Approach
and
Results
• Credit
Card
Customer
Acquisi6on
via
Facebook
Ads
– Facebook
ads
plaOorm
– Business
problem:
op6mal
targe6ng
and
bidding
– Applica6on
to
credit
card
marke6ng
3
Overview
–
Two
case
studies

4.
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©
2013.
Tiger
Analy7cs
Display
Adver7sing
through
Real-­‐7me
bidding
(RTB)
Case
Study
#
1

5.
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©
2013.
Tiger
Analy7cs
5
Display
Adver7sing
–
Real
Time
Bidding
Milliseconds to bid and load ad …
Waiting for ad
from ad exchange
…
Male,
20-30 yrs, NYC
Tech user

6.
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©
2013.
Tiger
Analy7cs
6
Display
Adver7sing
–
Real
Time
Bidding
Targeted
Ads

7.
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©
2013.
Tiger
Analy7cs
Real
Time
Bidding
(RTB)
for
Display
Ads
7
Ad-­‐Exchange
Ad-network 2
Publisher
(NYT.com)
Tech section
Ad-network 1
Ad-network 3
Advertiser
Advertiser
Advertiser
Male,
20-30 yrs,
New York,
Tech user
• The
en6re
process
takes
less
than
500
milliseconds
• RTB
share
of
online
ads
is
es6mated
to
be
$2B
per
year

8.
Copyright
©
2013.
Tiger
Analy7cs
Business
Problem
• Click-­‐through
rate
(CTR)
predic6on:
Given
a
campaign
line,
what
is
the
predicted
CTR
for
an
impression
based
on
– User
characteris6cs
– Webpage
characteris6cs
• Iden6fy
impressions
with
highest
CTR?
8

9.
Copyright
©
2013.
Tiger
Analy7cs
Maximizing
the
CTR
is
Cri7cal
For
Cost
Op7miza7on
9
High CTR is good for everyone: users, advertiser, and publisher
High
CTR
Relevant
content
for
Users
Revenue
maximiza6on
for
Publisher
Relevant
audience
for
Adver6ser

10.
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©
2013.
Tiger
Analy7cs
Sample
data
10

11.
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©
2013.
Tiger
Analy7cs
Data
challenges
• Challenges
– More
than
5000
variables
– Hundreds
of
millions
of
data
points
– Sparse
and
missing
data
– Clicks
are
very
rare
(typically
1
click
in
every
3,000
impressions)
11

12.
Copyright
©
2013.
Tiger
Analy7cs
• Case
sampling
– Keep
all
impressions
with
clicks
– Keep
only
a
random
sample
of
1%
non-­‐clicks
• This
reduced
the
data
size
by
100-­‐fold,
but
predic6on
accuracy
was
as
good
as
when
using
all
data
12
Reducing
the
number
of
data
sets

13.
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©
2013.
Tiger
Analy7cs
Logis7c
Regression
results
13
- "
50 "
100 "
150 "
200 "
250 "
300 "
350 "
400 "
450 "
1" 1001" 2001" 3001" 4001" 5001"
predicted!
baseline!
K K K K K K
0% 20% 40% 60% 80% All data
• Top
20%
of
data
got
232
out
of
415
(56%)
of
clicks
• A
liY
of
180%

14.
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©
2013.
Tiger
Analy7cs
14
Insights
–
Final
set
of
variables

15.
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©
2013.
Tiger
Analy7cs
Twier
–
CTR
Predic7on
15
NLP

16.
Copyright
©
2013.
Tiger
Analy7cs
Credit
Card
Customer
Acquisi7on
Through
Social
Media
Marke7ng
Case
Study
#
2

17.
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©
2013.
Tiger
Analy7cs
Social Media provides rich data to marketers

18.
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©
2013.
Tiger
Analy7cs
Ads
on
Facebook
Newsfeed
on
Desktop
Newsfeed
on
Mobile
Right
Hand
Side
on
Desktop
Sponsored
Story
Image source:
Facebook

19.
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©
2013.
Tiger
Analy7cs
Facebook
Ad
Pla`orm
-­‐-­‐
targe7ng
19

20.
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©
2013.
Tiger
Analy7cs
Facebook
Ad
Pla`orm
-­‐-­‐
pricing
20

21.
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©
2013.
Tiger
Analy7cs
Case
study:
credit
card
marke7ng
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Cash
Back
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the file again. If the red x still appears, you may have to
delete the image and then insert it again.
1,000,000
Impressions
300
Clicks
3
Applica7ons
1
Approval
Conversions are rare events when compared to clicks. The challenge is to be able to make
meaningful inferences based on very little data, especially early on in the campaign.
Click-­‐through
rate
0.03%
Conversion
rate
1%
Approval
rate
33%

22.
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©
2013.
Tiger
Analy7cs
Micro
Segments
1
Segment
50
Segments
50
x
2
=
100
Segments
2
Genders
4
Age
Groups
100
x
4
=
400
Segments
25
Interest
Clusters
400
x
25
=
10,000
Segments

23.
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©
2013.
Tiger
Analy7cs
Methodology
• Iden6fy
high
performance
segments
– Sta6s6cally
significant
difference
in
ctr,
cpc,
cost
per
conversion,
etc.
– Use
ctr
as
a
proxy
for
conversion
rate
• Ac6ons
on
high
performance
segments
– Allocate
higher
budget
– Increase
bid
price
23

24.
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©
2013.
Tiger
Analy7cs
Data
aggrega7on
Segment
Level
Data
(Sparse
and
Noisy)
Iden7fy
Important
Dimensions
(Using
Sta7s7cal
Models)

25.
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©
2013.
Tiger
Analy7cs
Segment
performance
es7ma7on
Model
Es7mates
Observed
Performance
Prior
Knowledge
Inferred
Performance

26.
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©
2013.
Tiger
Analy7cs
Bidding
Brand
A
Brand
B
Other
Compe77on
for
Ad
Space
Bid:
$1.60
Bids
WIN
Bids
will
differ
by
Ad
and
Micro
segment,
and
will
change
over
7me

27.
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©
2013.
Tiger
Analy7cs
Budget
Alloca7on
• Increase
budget
for
high
performance
segments
and
reduce
for
low
performance
ones
– Business
rules
around
minimum
and
maximum
limits

28.
Copyright
©
2013.
Tiger
Analy7cs
Methodology
Segment
Level
Observed
Data
Inferred
Performance
Indicators
Based
on
priors,
observed,
model
es6mates
Cost
per
Applica6on
Success
Rate
Dynamic
Budget
Alloca7on
Based
on
inferred
performance
indicators
and
business
constraints
Historical
Campaign
Data
Priors
of
Performance
Indicators
Weighted
Data
Click
vs.
view
through,
card
value,
applica6on
result,
recency,
delay
in
view-­‐through
appls
Cost
per
Acquisi6on
Model
Performance
as
a
func6on
of
targe6ng
dimensions
Model
Es7mates
of
Performance
Indicators
Dynamic
Bid
Alloca7on
Based
on
observed/historical
Bid-­‐Spend
rela6onships
Con7nual
monitoring
and
analysis
Business
Constraints

29.
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©
2013.
Tiger
Analy7cs
Results:
Increased
CTR
29
• Overall increase in CTR by 50% across more than 100 brands

30.
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©
2013.
Tiger
Analy7cs
Results:
Lower
costs
30
• Overall decrease in CPA of 25% across more than 100 brands

31.
Copyright
©
2013.
Tiger
Analy7cs
Concluding
remarks
• Online
and
social
adver6sing
are
fast
growing
areas
with
– Plenty
of
data
– A
large
number
of
interes6ng
problems
• Predic6ve
analy6cs
can
add
a
lot
value
in
this
business
– Significant
improvement
in
CTR
means
beber
targeted
ads
– As
much
as
25%
reduc6on
in
cost
of
media
• Our
solu6ons
are
being
used
by
several
leading
startups
to
serve
billions
of
ads
for
Fortune
500
companies
31

32.
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©
2013.
Tiger
Analy7cs
Ques7ons
/
Comments
?
mahesh@7geranaly7cs.com
www.7geranaly7cs.com
32