An introduction to Ad Networks, incl. types, business models, pricing, reporting, etc. From the Innovation Works mentoring sessions (February 2010).

1. Wang
Hua
Founding
Partner,
Innova1on
Works
February
2010
Innovation Works Confidential and Proprietary

2. What is an Ad Network?
Media
• Aggrega&ng
Media’s
Posi0on
• Media
planning
• Ad
crea&ng
Ad
Network
• Ad
serving
Aggregate
&
Convert
• Tracking
• Repor&ng
Adver+sers
• Transac&on
Adver0ser’s
Demand
•
Both
Media
and
Adver+ser
are
clients
of
the
Ad
Network,
but
fundamentally
Adver+ser
is
the
client
of
the
whole
value
chain.
•
Branding
ads
network
/
performance
ads
network
/
ver+cal
ads
network.
© 2010 – Innovation Works – All rights reserved.

3. Branding Ads vs. Performance Ads
Branding
Ads
Performance
Ads
Brand
Awareness,
Immediate
Transac+on
Reputa+on,
etc.
Long-­‐term
Profit
Short-­‐term
Profit
• Ads’
purpose
is
to
impact
user
decision.
•
The
closer
ads
to
user
decision,
the
higher
the
value
of
the
ads.
© 2010 – Innovation Works – All rights reserved.

4. Advertiser’s Decision-Making Flow
Set
Goal
and
Ads
Media
Small-­‐Scale
Matrix
Crea+ve
Planning
Running
Crea+ve
Agency
Media
Agency
SEM
Bad
Tracking
Stop
Result
Medium
Tracking
tool
Good
Large-­‐Scale
Running
© 2010 – Innovation Works – All rights reserved.

5. Why Ad Networks make money?
• Buy
low
-­‐
Sell
high
• Create
addi&onal
value
and
share
a
split:
 Long-­‐term
strategies
• Service
(Double
Click)
• BeQer
algorithm,
beQer
conversion
(Google
vs.
Yahoo)
• Network
effect
to
get
monopoly
posi+on
 Short-­‐term
strategies
• Control
of
specific
traffic
• Control
of
specific
adver+ser
• Arbitrage
• Re-­‐package
undervalued
resources
(Focus
Media)
• Economy-­‐of-­‐scale
(Allyes)
© 2010 – Innovation Works – All rights reserved.

6. Pricing Model
• Fixed
Price
 Upside:
• very
simple,
easy
to
understand,
easy
to
execute
• predictable
revenue
stream
or
spending
 Downside:
• hard
to
price
right
• can’t
capture
the
max
value,
and
lose
poten+al
long
tail
revenue
• Bidding
 automa+c
pricing,
capture
the
most
value
 less
transparency,
harder
to
understand
and
use
by
adver+ser
 loss
of
revenue
when
there
is
not
enough
bidding
pressure
• The
evolvement
of
the
Bidding
System
 from
Overture
to
Google
 case
study;
why
every
winner
only
need
to
pay
+1
cent
of
next
place
© 2010 – Innovation Works – All rights reserved.

7. Tracking and Reporting
• Tracking
and
repor&ng
is
essen&al,
as
Adver&ser
pays
for
matrix.
• One
advantage
of
online
ads
is
accurate
tracking
and
fast
itera&on.
• Compare
to
inven&ng
a
new
matrix,
it’s
beIer
to
be
compa&ble
with
the
long-­‐
accepted
old
matrix.
Main
budget
Experimental
budget
New
Premium
media
Discount
media
media
© 2010 – Innovation Works – All rights reserved.

8. Matrix
•
Mismatch
(Adver+ser):
Payment
matrix
&
Internal
matrix
or
real
demands
Higher
Adver+ser's
risk
Lower
Adver+ser’s
willingness
• CPT
•
The
trend
is
going
down.
• CPM
•
But
it
will
require
beQer
tracking
• CPC
and
targe+ng
capability.
• CPA
• CPS
• CPR
Lower
Publisher's
willingness
Higher
publisher’s
risk
© 2010 – Innovation Works – All rights reserved.

9. Spam
• Destroy
the
adver&ser’s
trust
&
kill
the
network
(Alimama).
• Comes
from
a
flaw
of
the
business
model.
• Misalignment
between
payment
matrix
&
adver&ser’s
real
demand.
 It’s
almost
impossible
to
spam
CPS
or
CPR,
the
bigger
the
gap
the
harder
for
us
to
an+-­‐
spam.
© 2010 – Innovation Works – All rights reserved.

10. Ways to Anti-spam
• Business
model:
minimize
the
gap.
• Technology:
gather
as
much
informa&on
as
possible,
and
recognize
the
spam
paIern:
• Tracking
system
on
both
publisher
side
and
adver+ser
side.
• Example:
IP,
user
agent,
cookie,
referral,
click
through
rate,
click
paQern,
traffic
paQern,
user
stay
+me,
user
behavior
on
landing
page,
conversion
rate.
• Adver&ser,
user
report
and
manual
analysis.
© 2010 – Innovation Works – All rights reserved.

11. Serving & Matching ( )
An
Example:
Publisher
Adver&ser
inventory
inventory
Matching,
serving,
and
conversion
Context
info,
Ads
content,
user
info,
historical
data
historical
data
• Categorize
traffic,
recognize
user
inten&on,
matching
with
right
ads
and
landing
page,
and
inspiring
user
conversion.
• Goal:
maximize
publisher
revenue
and
adver&ser’s
conversion.
© 2010 – Innovation Works – All rights reserved.

12. Serving & Matching ( )
• Contextual
targe&ng,
behavioral
targe&ng,
profile
targe&ng:
 Contextual
targe+ng:
• Upside:
No
need
of
addi+onal
informa+on,
current
user
status,
best
matching
when
user
have
clear
intension
• Downside:
No
individual
informa+on,
worse
when
user
does
not
have
clear
intension
(e.g.
MySpace)
 Profile
targe+ng,
Behavioral
targe+ng:
• Upside:
BeQer
when
user
haven’t
clear
intension
• Downside:
No
user
current
status
informa+on,
need
specific
user
privacy
data,
hard
to
get.
© 2010 – Innovation Works – All rights reserved.

13. Serving & Matching ( )
• Currently
good
ad
networks
normally
use
all
three
methods.
• Matching
quality
depends
not
only
on
quality
of
algorithm,
but
also
quality
of
publisher
inventory,
ads
inventory
size
and
data
size.
• Normally,
beIer
algorithm
and
more
input
get
beIer
result,
but
must
balance
with
cost
and
speed.
• Ads
format,
ad
quality,
landing
page
quality,
serving
quality
have
big
impact
(e.g.
ads
similar
with
main
content).
© 2010 – Innovation Works – All rights reserved.

14. Serving & Matching ( )
• Example
system
on
slide
11.
• Star&ng
from
contextual
targe&ng,
now
have
profile
and
behavioral
targe&ng,
support
mul&ple
form
bidding,
CPC
and
CPM
bidding.
• Revenue
=
Traffic
x
RPM
• RPM
=
CPC
x
CTR
x
Quality
Score
(conversion)
• Extract
keywords,
calculate
eCPM,
trial
running,
refine
eCPM,
adjust
with
conversion
score.
© 2010 – Innovation Works – All rights reserved.

15. Ads Front-End
• Normally
includes
ads
crea&on,
media
planning,
report
and
transac&on
part.
• Could
be
internal
facing
or
external
facing.
• Help
user
create
and
manage
campaign,
simplify
user
decision
making.
© 2010 – Innovation Works – All rights reserved.