Computational Advertising has recently emerged as a new scientific sub-discipline, bridging the gap among the areas such as information retrieval, data mining, machine learning, economics, and game theory. In this tutorial, I shall present a number of challenging issues by analogy with financial markets. The key vision is that display opportunities are regarded as raw material “commodities” similar to petroleum and natural gas – for a particular ad campaign, the effectiveness (quality) of a display opportunity shouldn’t rely on where it is brought and whom it belongs, but it should depend on how good it will benefit the campaign (e.g., the underlying web users’ satisfactions or respond rates). With this vision in mind, I will go through the recently emerged real-time advertising, aka Real-Time Bidding (RTB), and provide the first empirical study of RTB on an operational ad exchange. We show that RTB, though suffering its own issue, has the potential of facilitating a unified and interconnected ad marketplace, making it one step closer to the properties in financial markets. At the latter part of this talk, I will talk about Programmatic Premium, i.e., a counterpart to RTB to make display opportunities in future time accessible. For that, I will present a new type of ad contracts, ad options, which have the right, but no obligation to purchase ads. With the option contracts, advertisers have increased certainty about their campaign costs, while publishers could raise the advertisers’ loyalty. I show that our proposed pricing model for the ad option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines.
3. When
online
adver3sing
goes
wrong
h9p://mashable.com/2008/06/19/contextual-‐adverNsing/
Web
users
were
unlikely
to
click
a
shoes
ad
that
appeared
along
side
an
arNcle
about
the
rather
gruesome
story
about
severed
feet
washing
up
on
shore
4. What
is
the
(fundamental)
problem?
• AutomaNcally
place
the
right
ad
for
the
right
web
page
at
the
right
-me
for
the
right
user
• The
match
is
scienNfically
difficult
– because
ulNmately
ads
shouldn’t
be
matched
to
the
web
pages
where
they
are
to
be
allocated,
– but
the
underlying
web
users,
whose
informaNon
needs
and
reacNons
are,
however,
not
known
a
priori
5. Why
financial
methods?
• Our
observaNons
3+
years
ago:
– although
mulNple
parNcipants
from
ad
providers,
adver-sers,
and
web
users,
the
industry
and
research
seem
unbalanced
• It
is
unclear
who
should
do
that.
“search
engines”
as
publishers,
ad
networks,
doing
the
keyword
matching
etc
• li9le
research
has
been
found
from
the
bidders
(the
adverNsers)’s
perspecNve
to
help
them
manage
their
campaigns
6. An
analogy
with
Financial
markets
• Ads
(display
opportuniNes)
are
“traded”
based
on
the
dual
force
of
supply
(publishers)
and
demand
(adverNsers)
Display
OpportuniNes
=?
“raw
materials”
like
petroleum
and
natural
gas
7. Ads
prices
are
vola3le
(a) (b)The
price
movement
of
a
display
opportunity
from
Yahoo!
ads
data
Under
GSP
(generalized
second
price
aucNon)
(a)
(c)
8. Research
opportuni3es
• Need
Ad’s
Futures
Contract
and
Risk-‐reduc4on
Capabili4es
– AutomaNon
is
constrained
mainly
to
“spots”
markets,
i.e.,
any
transacNon
where
delivery
takes
place
right
away
– No
principled
technologies
to
support
efficient
forward
pricing
&risk
management
mechanisms
• If
we
got
Futures
Market,
adverNsers
could
lock
in
the
campaign
cost
and
Publishers
could
lock
in
a
profit
• Need
to
engineer
a
Unified
Ad
Exchange
– The
ad
market
is
in
the
hands
of
a
few
key
players.
Each
individual
player
defines
its
own
ad
system
– Arbitrage
opportuniNes
exist.
Two
display
opportuniNes
with
similar
targeted
audiences
and
visit
frequency
may
sell
for
quite
different
prices
on
two
different
markets.
9. Current
trends
in
online
adver3sing
• Spot
markets:
Real-‐Time
Bidding
(RTB)
allows
selling
and
buying
online
display
adverNsing
in
real-‐Nme
one
ad
impression
at
a
Nme
•
Future
markets:
ProgrammaNc
Premium
to
use
automated
procedures
to
reach
agreement
of
sales
between
buyers
(adverNsers)
and
sellers
(publishers)
• With
the
two
trends,
a
step
closer
to
the
financial
markets
where
unificaNon
and
interconnecNon
are
strongly
promoted
10. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
11. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
14. Real-‐3me
bidding
“This
is
Lawrence
from
India.
I
was
searching
Recommender
model
in
web
and
found
your
webpage
in
search
engine.
Then,
I
visited
your
webpage
searching
relevant
contents
and
saw
unrelevant
Google
add
in
"Research
Team"
page
(aRached
screenshot).
This
add
might
vary
from
country
to
country.
But
I
feel
it
will
mislead
and
give
wrong
opinion
to
users
who
visit
your
webpage.”
-‐
Lawrence
from
India
15. The
winning
bids
and
hourly
average
Figure 7: The time series snippet of winning bids and its hourly
average of a single placement. The hourly average series peaks
around 6-8am every day when there are less impressions but more
Figure 8: The d
against hour-of
16. Daily
Pacing
ons but more
Wilk test [26]
the number
tisers spend
iser submits
from spend-
) and as fast
may lead to
depletes too
r in the day,
tance of pre-
The uniform
f high qual-
, the pacing
m; if there is
the pacing
get (usually
d daily pac-
Ps.
er of bidders
ised respec-
series reach
e number of
against hour-of-day. Their correlation plot shows a clear lag of
when they reach the maximum in a day. This lag indicates the
unbalance of supply and demand of the market in certain hours.
Besides, the fact that there are more bidders in the morning may
be due to the mixture of hour-of-day targeting and no daily pacing
setup. The plots used 3 months worth of data sampled from a
single placement. Note for some placements the lag was not very
clear.
Figure 9: An interesting instance we found in the dataset: an
advertiser switched from no pacing to even daily pacing. He was
bidding at a flat CPM. With ad exchanges, the large amount of
choose
from
spending
it
evenly
throughout
a
day
(uniform
pacing)
and
as
fast
as
possible
(no
pacing).
17. The
frequency
factor
• How
many
Nmes
ads
(a.k.a.
creaNves)
would
be
displayed
to
a
single
user.
llo-
ent
n a
nta-
red
g
nd-
ery
ors
re-
18. The
Recency
Factor
• helps
to
decide
to
bid
or
not
based
on
how
recently
the
ad
was
displayed
to
the
same
user.
19. Summary
• Spot
market:
Real-‐Nme
Bidding
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
• Ad
opNons
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
20. Why
Ad
Futures
(1)
• Suppose
there
is
a
travel
insurance
company
whose
major
customers
are
found
through
online
adverNsing
21. Why
Ad
Futures
(2)
• In
March
the
company
plans
an
adverNsement
campaign
in
three
months
Nme
as
they
think
there
will
be
more
opportuniNes
to
sell
their
travel
insurance
products
in
the
summer
22. Why
Ad
Futures
(3)
• If
the
company
worries
that
the
future
price
of
the
impressions
will
go
up,
they
could
hedge
the
risk
(lock
in
the
campaign
cost)
by
agreeing
to
buy
(taking
a
long
posiNon)
the
display
impressions
in
3
months
Nme
for
an
agreed
price
(taking
a
long
posiNon
in
a
3-‐
month
forwarding
market).
23. Why
Ad
Futures
(4)
• Equally,
search
engines
and
large
publishers
could
agree
to
sell
(taking
a
short
posiNon)
the
display
impressions
in
the
future
if
worry
the
price
will
go
down
(lock
in
a
profit).
• IntuiNvely,
also
useful
for
inventory
management
24. However,
online
adver3sing
is
different
• A.
Non-‐storability:
unlike
stocks
or
other
common
communiNes
(petroleum
and
natural
gas
),
we
cannot
buy
and
keep
an
impression
(thus
ad
display
slot)
for
a
period
of
Nme
and
sell
it
later
in
order
to
gain
the
profit
by
its
price
moment
– This
is
in
fact
more
similar
to
electricity/energy
markets
• B.
Not
just
about
the
price
movements:
the
risk
also
lies
in
the
uncertainty
of
the
number
of
impressions
(number
of
visits)
and
click-‐through
rate
in
the
future
25. C.
There
are
many
pricing
schemes
Based
on
cost
per
click,
cost
per
impression
etc.
However,
online
adver3sing
is
different
26. Ad
Op3ons
Pricing
is
the
Core
• Due
to
the
differences
menNoned,
financial
models
and
theories
cannot
be
directly
employed
–
need
to
rethink
what
is
the
“fair”
price
for
ads
• We
have
developed
a
novel
ad
opNon
pricing
model
to
understand
a
“fair”
price
in
various
senngs,
e.g.,
sponsored
search,
contextual
adverNsing,
and
banner
ads
27. GSP-Based Keyword Auction
Search
engine
Online
adver3sers
1st
ad
slot
£
0.2
£
0.32
£
0.15
£
0.22
2nd
ad
slot
…
£
0.2
£
0.22
Choose
the
adverNser
with
the
highest
bid,
but
normally
charge
him
based
on
the
second
highest
bid
price,
called
Generalised
Second
Price
(GSP)
auc3on
model.
28. • VolaNlity
in
revenue.
• Uncertainty
in
the
bidding
and
charged
prices
for
adverNsers'
keywords.
• Weak
brand
loyalty
between
the
adverNser
and
the
search
engine.
Problems of Auction Mechanism
30. An
opNon
is
a
contract
in
which
the
opNon
seller
grants
the
opNon
buyer
the
right
but
not
the
obligaNon
to
enter
into
a
transacNon
with
the
seller
to
either
buy
or
sell
an
underlying
asset
at
a
specified
price
on
or
before
a
specified
date.
The
specified
price
is
called
strike
price
and
the
specified
date
is
called
expiraNon
date.
The
opNon
seller
grants
this
right
in
exchange
for
a
certain
amount
of
money
at
the
current
Nme
is
called
opNon
price.
Option Contract
31. online
adver3ser
search
engine
sell
a
list
of
ad
keywords
via
a
mulN-‐keyword
mulN-‐click
opNon
mul3-‐keyword
mul3-‐click
op3on
(3
month
term)
upfront
fee
(m
=
100)
keywords
list
fixed
CPCs
£5
‘MSc
Web
Science’
£1.80
‘MSc
Big
Data
AnalyNcs’
£6.25
‘Data
Mining’
£8.67
t
=
T
Timeline
submit
a
request
of
guaranteed
ad
delivery
for
the
keywords
‘MSc
Web
Science’,
‘MSc
Big
Data
AnalyNcs’
and
‘Data
Mining’
for
the
future
3
month
term
[0,
T],
where
T
=
0.25.
t
=
0
pay
£5
upfront
opNon
price
to
obtain
the
opNon.
Selling & Buying An Option
32. 3232
online
adver3ser
search
engine
t
=
T
Timeline
exercise
100
clicks
of
‘MSc
Web
Science’
via
opNon.
t
=
0
pay
£1.80
to
the
search
engine
for
each
click
unNl
the
requested
100
clicks
are
fully
clicked
by
Internet
users.
t
=
t1
reserve
an
ad
slot
of
the
keyword
‘MSc
Web
Science’
for
the
adverNser
for
100
clicks
unNl
all
the
100
clicks
are
fully
clicked
by
Internet
users..
t
=
t1c
Exercising the Option
33. 3333
online
adver3ser
search
engine
t
=
T
Timeline
if
the
adverNser
thinks
the
fixed
CPC
£8.67
of
the
keyword
‘Data
Mining’
is
expensive,
he/she
can
a9end
keyword
aucNons
to
bid
for
the
keyword
as
other
bidders,
say
£8.
t
=
0
pay
the
GSP-‐based
CPC
for
each
click
if
winning
the
bid.
t
=
…
select
the
winning
bidder
for
the
keyword
‘Data
Mining’
according
to
the
GSP-‐based
aucNon
model.
Not Exercising the Option
34. Benefits of Ad Option
Adver3ser
Search
Engine
§ secure
ad
service
delivery
§ reduce
uncertainty
in
aucNons
§ caps
ad
cost.
§ selling
the
inventory
in
advance;
§ having
a
more
stable
and
predictable
revenue
over
a
long-‐term
period;
§ Increasing
adverNsers’
loyalty
35. 3535
§ No-‐arbitrage
[F.Black
and
M.Scholes1973;
H.Varian1994]
§ StochasNc
underlying
keyword
CPC
[P.
Samuelson1965]
§ Terminal
value
formulaNon
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
Ad Option Pricing: Building Blocks
36. Ad Option Pricing: Formula
§ n=1,
Black-‐Scholes-‐Merton
European
call
§ n=2,
Peter
Zhang
dual
strike
European
call
§ n>=3,
Monte
Carlo
method
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980
37. Ad
op3on
pricing
for
the
keywords
`canon
cameras',
`nikon
camera‘
and
`yahoo
web
hos3ng‘.
Experimental Results:
Monte Carlo Simulation
38. Fig.
empirical
example
for
the
keyword
‘lawyer’,
where
the
Shapiro-‐Wilk
test
is
with
p-‐value
0.1603
and
the
Ljung-‐Box
test
is
with
p-‐value
0.6370.
39. 3939
About 15.73% of keywords that can be effectively
priced into an option contract under the GBM.
44. Concluding
remarks:
“Recipe
for
Disaster”-‐
Model
That
“Killed”
Wall
Street?
• Wall
Street’s
math
models
for
minNng
money
worked
brilliantly...
unNl
one
of
them
devastated
the
global
economy
• David
X.
Li's
Gaussian
copula
funcNon
as
first
published
in
2000.
Investors
exploited
it
as
a
quick
way
to
assess
risk
(the
chance
a
company
is
likely
to
default)
• As
Li
himself
said
of
his
own
model:
"The
most
dangerous
part
is
when
people
believe
everything
coming
out
of
it.”
44
h9p://www.wired.com/techbiz/it/magazine/17-‐03/wp_quant?currentPage=all
45. For
more
informa3on,
please
refer
to
Shuai
Yuan,
Jun
Wang,
Real-‐Nme
Bidding
for
Online
AdverNsing:
Measurement
and
Analysis,
AdKDD’13
h9p://arxiv-‐web3.library.cornell.edu/abs/1306.6542
Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
MulN-‐Keyword
MulN-‐Click
OpNon
Contracts
for
Sponsored
Search
AdverNsing,
under
submission,
2013
h9p://arxiv.org/abs/1307.4980