In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. In this talk, I aim to provide an overview of the fundamental infrastructure, algorithms, and technical challenges of this new frontier of computational advertising. I shall present our research on RTB including bidding algorithms, revenue optimisation, statistical arbitrage, and futures exchange and ad option pricing.

1. Real-Time Bidding (RTB) based
Display Advertising
- System Mechanisms and Algorithms
Dr. Jun Wang, Senior Lecturer
Computer Science, University College London
Co-founder, CTO, MediaGamma
Email: j.wang@cs.ucl.ac.uk Twitter: @seawan
Joint
work
with
Weinan
Zhang,
Shuai
Yuan,
and
Bowei
Chen

2. Computa(onal
Adver(sing
and
Behavior
Targe(ng
Research
Group
at
UCL
Computer
Science
• Work
on
related
topics,
including
informa=on
retrieval,
real-­‐=me
bidding,
collabora=ve
filtering
and
personalisa=on
21/05/15
Yandex
Talk
2

3. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
Yandex
Talk
3

4. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
Yandex
Talk
4

5. Contextual
ads:
relevant
to
the
webpage
content
Content: iPad
Ads:
Tablet PCs,
mobile phone
etc.
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6. Real-­‐(me
Adver(sing:
Selling
ad
slot
per
impression
targeted
to
the
user
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6

7. Real-­‐(me
Adver(sing:
Selling
ad
slot
per
impression
targeted
to
the
user
s
21/05/15
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7

8. How
big
RTB
is?
• The
AppNexus
(exchange)
sees
more
than
30
billion
impressions
in
a
single
day
• The
Rubicon
Project
(SSP)
has
hit
1
trillion
impressions
• The
Global
Real-­‐Time
Bidding
(RTB)
market
is
forecast
to
GROW
at
41.18%
CAGR
(Compound
annual
growth
rate)
during
2014-­‐2019
21/05/15
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9. Life
of
a
display
ad
in
the
RTB
environment:
0.2
seconds
Ad
Exchange
Demand-Side
Platform
Advertiser
Data
Management
Platform
0.
Ad
Request
1.
Bid
Request
(user,
context)
2.
Bid
Response
(ad,
bid)
3.
Ad
Auc=on
4.
Win
No=ce
(paying
price)
5.
Ad
(with
tracking)
6.
User
Feedback
(click,
conversion,
etc.)
User
Informa=on
User
Demography:
Male,
20+,
etc.
User
Segmenta=ons:
Travel,
etc.
Webpage
User
21/05/15
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9

10. The
new
RTB
eco-­‐system
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Demand
Side
PlaKorm
(DSP)
Adver(ser Publisher
Supply
Side
PlaKorm
(SSP)
Ad
Exchange
-­‐15%
-­‐
20% -­‐15%
-­‐
20%
3rd
party
data
provider,
ad
serving,
ad
agency,
campaign
analy(cs,
-­‐10%
-­‐
30%
-­‐10
%
–
15%
£1.00 £0.15
-­‐
£0.50
10

11. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
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11

12. The
mechanism:
a
mixture
of
first
and
second
price
auc(ons
• A high soft floor price can make
it first price auction
(In RTB, floor prices are not
always disclosed before
auctions)
• In our dataset, 45% first price
auctions consumed 55%
budgets
• The complicated setting puts
advertisers in an unfavourable
position and could damage the
ad eco-system
21/05/15
Yandex
Talk
Shuai
Yuan,
Jun
Wang,
Real-­‐=me
Bidding
for
Online
Adver=sing:
Measurement
and
Analysis,
AdKDD’13
12

13. Auc(ons:
Winning
bids
against
(me
for
a
given
placement
• Winning bids peak at
8-10am
• The hourly average
series peaks around
6-8am every day
when there are less
impressions but more
bidders.
21/05/15
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13

14. Auc(ons:
The
distribu(on
of
number
of
bidders
and
impressions
against
(me
for
a
given
placement
• More bidders in the
morning, which may be
due to the mixture of
hour-of-day targeting and
no daily pacing setup
• A clear lag suggests the
unbalance of supply and
demand of the market in
certain hours.
The
plots
used
3
months
worth
of
data
sampled
from
a
single
placement.
21/05/15
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14

15. Conversions:
post-­‐view
&
post-­‐click
Daily periodic patterns for conv (left) and cvr (right) show that
Some pattern, e.g., in this case, people are less likely to convert during late night
The post-view conversions are NOT negligible
21/05/15
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15

16. Conversions:
User
target
frequency
distribu(on
The frequency against CVR plot from two different campaigns
Campaign 1 sets a frequency cap of 2-5 -> poor performance
Campaign 2 sets a frequency cap of 6-10 -> waste of budget
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16

17. The recency factor affects the CVR (left)
Campaign 1 sets a long recency cap -> waste of budget
Campaign 2 sets a short recency cap -> poor performance
The wide conversion window (right)
challenges attribution models
Conversions:
Recency
Distribu(on
21/05/15
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Talk
17

18. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
Yandex
Talk
18

19. Op(mal
Bidder
in
DSP
Weinan
Zhang,
Shuai
Yuan,
Jun
Wang,
Op=mal
Real-­‐Time
Bidding
for
Display
Adver=sing,
KDD’14
19

20. Op(mal
Bidder:
Problem
Defini(on
Bid
Engine
Bid
Request
Bid
Price
Input:
bid
request
include
Cookie
informa=on
(anonymous
profile),
website
category
&
page,
user
terminal,
loca=on
etc
Output:
bid
price
Considera(ons:
Historic
data,
CRM
(first
party
data),
DMP
(3rd
party
data
from
Data
Management
Plaiorm)
What
is
the
op(mal
bidder
given
a
budget
constraint?
e.g.,
Maximise
Subject
to
the
budget
constraint
20

21. Op(mal
Bidder
Formula(on
• Func=onal
op=misa=on
problem
• Solu=on:
Calculus
of
varia=ons
CTR
winning
func=on
bidding
func=on
budget
Est.
volume
21

22. Op(mal
Bidding
Strategy
Solu(on
22

23. Op(mal
bidding
strategy
compared
with
a
linear
solu(on
Slight
increase
at
low
bids
is
more
effec=ve
Thus
reduce
the
bids
at
high
CTR
or
CVR
23

24. Overall
Performance
–
Op(mising
Clicks
24

25. 25
hlp://contest.ipinyou.com/session-­‐three-­‐online-­‐leaderboard.html

26. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
Yandex
Talk
26

27. DSP
as
an
Intermediary
in
RTB
• Different
pricing
schemes:
CPM/CPC/CPA
RTB
Intermediary
Ad agent
Trading Desk
DSP
Publishers
Ad
inventories
Advertisers
Sell
ad
inventory
Sell
ad
inventory
Buy
ad
inventory
Buy
ad
inventory
CPA
CPM
Weinan
Zhang
and
Jun
Wang,
Sta=s=cal
Arbitrage
Mining
for
Display
Adver=sing,
accepted
KDD’15
27

28. DSP
as
an
intermediary
in
RTB
• Sta=s=cal
arbitrage
opportunity
occurs
when
(CPM)
cost
per
conversion
<
(CPA)
payoff
per
conversion
28

29. Sta(s(cal
Arbitrage
Mining
• Op=mising
net
profit
by
tuning
bidding
func=on
and
campaign
volume
alloca=on
Total
cost
constraint
Risk
control
E-­‐Step
M-­‐Step
Total
arbitrage
net
profit
• Solve
it
in
an
EM
fashion
29

30. EM
Solu(ons
• M-­‐Step:
Fix
v
and
tune
b()
• E-­‐Step:
Fix
b()
and
tune
v
Poriolio
margin
variance
Poriolio
margin
mean
Total
arbitrage
net
profit
Total
cost
constraint
Net
profit
margin
on
each
campaign
30

31. Mul(ple
Campaign
Arbitrage
Results
31

32. Dynamic
PorKolio
Op(misa(on
32

33. Online
A/B
Test
on
BigTree™
Mobile
DSP
• 23
hours,
13-­‐14
Feb.
2015,
with
$60
budget
each
33

34. Summary
• Introduc=on
to
Real-­‐Time
Bidding
(RTB)
• RTB
exchange
mechanisms:
measurements
and
analysis
• Buy
side:
– Bidding
op=misa=on
– Sta=s=cal
Arbitrage
Mining
(SAM)
• Sell
side:
– Forward
market
(Waterfall
op=misa=on)
– Ad
op=ons
pricing
21/05/15
Yandex
Talk
34

35. (RTB)
Ads
prices
are
vola(le
35
●
●
●
●
●
●
●
● ● ●
●
●
●
●
● ●
●
●
●
●
●
●
● ●
50
60
70
80
90
100
0 5 10 15 20
Hour
CPM
campaign
● 1
2
●
●
● ● ● ●
●
●
●
●
●
●
● ● ● ● ● ●
●
●
●
●
●
●
0.1
0.2
0.3
0.4
0 5 10 15 20
Hour
AWR
campaign
● 1
2
● ●
●
● ●
●
●
●
●
●
●
●
● ●
●
●
● ● ●
●
●
●
●
●
25
50
75
0 5 10 15 20
Hour
eCPC
campaign
● 1
2
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0.000
0.005
0.010
0 5 10 15 20
Hour
CTR
campaign
● 1
2
Figure 1: The instability of CPM (cost per mille), AWR (auction
D
Bi
A
Figur
ad exc
21/05/15
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36. Hedge
the
price
risk
• Need
Ad’s
Futures
Contract
and
Risk-­‐
reduc=on
Capabili=es
– Technologies
are
constrained
mainly
to
“spots”
markets,
i.e.,
any
transac=on
where
delivery
takes
place
right
away
(in
Real-­‐=me
Adver=sing
and
Sponsored
Search)
– No
principled
technologies
to
support
efficient
forward
pricing
&risk
management
mechanisms
36
21/05/15
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37. Advertiser
Demand Side
Platform
(DSP)
RTB / Spot
Exchange
Supply Side
Platform
(SSP)
Publisher
3rd party data providers, ad serving,
ad agency, ad networks, campaign
analytics
-10% to -30%
An
analogy
with
financial
markets
37
Forward/Futures/
Options Exchange
Solution 1:
Combine RTB with Forward Market, which pre-sell inventories in advance with a fixed price
Solution 2:
If we got Futures Exchange or provide Option Contracts, advertisers could lock in the campaign
cost and Publishers could lock in a profit in the future
21/05/15
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Talk

38. Publisher
ad
serving
“waterfall”
(programma(c
direct)
21/05/15
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Illustra=on
from
Sitescout
38

39. Solu(on
1:
RTB
combined
with
Forward
Programma(c
Guaranteed/Direct
Market
39
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B Chen et al., A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising. Best
Paper in ADKDD 2014

40. Op(misa(on
objec(ve
40
21/05/15
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B Chen et al., A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising. Best
Paper in ADKDD 2014

41. Op(mised
pricing
scheme
41
21/05/15
Yandex
Talk
B Chen et al., A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising. Best
Paper in ADKDD 2014

42. An Ad Option is a contract in which the option publisher grants the advertiser
the right but not the obligation to enter into a transaction either buy or sell an
underlying ad slot at a specified price on or before a specified date.
The specified pre-agreed price is called strike price and the specified date is
called expiration date. The option seller grants this right in exchange for a
certain amount of money at the current time is called option price.
Solu(on
2:
Ad
Op(on
Contract
J Wang and B Chen, Selling futures online advertising slots via option contracts, WWW 2012.
21/05/15
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43. Ad
op(ons:
Benefits
Advertisers
§ secure impressions delivery
§ reduce uncertainty in auctions
§ cap cost
Publishers
§ sell the inventory in advance
§ have a more stable and predictable revenue over a long-
term period
§ increase advertisers’ loyalty
21/05/15
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44. Submits a request of
guaranteed ad delivery for
the keywords ‘MSc Web
Science’, ‘MSc Big Data
Analytics’ and ‘Data
Mining’ for the future 3
month term [0, T], where T
= 0.25.
Ad
op(ons
contd.
Sells a list of ad keywords via a multi-keyword
multi-click option
t = T
Timeline
search engineonline advertiser
t = 0
Pays £5 upfront option
price to obtain the option.
B Chen et al., Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search, ACM
TOIST,
2015
multi-keyword multi-click option (3 month
term)
upfront
fee
(m = 100)
keywords list fixed CPCs
£5
‘MSc Web Science’ £1.80
‘MSc Big Data
Analytics’
£6.25
‘Data Mining’ £8.67
44
21/05/15
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45. Exercising
the
op(on
t = T
Timeline
Exercises 100 clicks of ‘MSc
Web Science’ via option.
t = 0
Pays £1.80 to the search
engine for each click until
the requested 100 clicks are
fully clicked by Internet
users.
t = t1
Reserves an ad slot of the keyword ‘MSc
Web Science’ for the advertiser for 100
clicks until all the 100 clicks are fully
clicked by Internet users..
t = t1c
45
search engineonline advertiser
21/05/15
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46. Not
exercising
the
op(on
t = T
Timeline
If the advertiser thinks the
fixed CPC £8.67 of the
keyword ‘Data Mining’ is
expensive, he/she can
attend keyword auctions to
bid for the keyword as other
bidders, say £8.
t = 0
Pays the GSP-based CPC
for each click if winning the
bid.
t = …
Selects the winning bidder for the keyword
‘Data Mining’ according to the GSP-based
auction model.
46
search engineonline advertiser
21/05/15
Yandex
Talk

47. Risk
Hedge
when
Ad
Op(ons
and
RTB
spot
are
combined
0.1552% 16.6955% -2.1550%
1
2
3
4
0.1 0.2 0.3 0.4 0.5
Revenue std
Revenuemean
(b)
0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
47
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B Chen and J Wang, A Lattice Framework for Pricing Display Ad Options with the Stochastic Volatility Underlying Model,
Technical Report, 2014

48. An empirical example of search engine's revenue for the keyword equity loans
4 4.5 5
0
0.1
0.2
0.3
0.4
0.5
0.6
Fixed CPC
Revenueofsearchengine
(a) GBM
Reve difference
Option price
Exp spot CPC
4 4.5 5
0
0.1
0.2
0.3
0.4
0.5
0.6
Fixed CPC
Revenueofsearchengine
(b) CEV
Reve difference
Option price
Exp spot CPC
4 4.5 5
0
0.1
0.2
0.3
0.4
0.5
0.6
Fixed CPC
Revenueofsearchengine
(c) MRD
Reve difference
Option price
Exp spot CPC
Revenueofsearchengine
4.5 5
Fixed CPC
(c) MRD
Reve difference
Option price
Exp spot CPC
4 4.5 5
0
0.1
0.2
0.3
0.4
0.5
0.6
Fixed CPC
Revenueofsearchengine
(d) CIR
Reve difference
Option price
Exp spot CPC
4 4.5 5
0
0.1
0.2
0.3
0.4
0.5
0.6
Fixed CPC
Revenueofsearchengine
(e) HWV
Reve difference
Option price
Exp spot CPC
Fig. 8. Empirical example of search engine’s revenue for the keyword ‘equity loans’.
Next, Figure 9 illustrates the case where we have 2 candidate keywords: ‘iphoneB Chen et al. Multi-Keyword Multi-Click Option Contracts for Sponsored Search Advertising, ACM
TOIST,
2015
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49. Acknowledgements
• Thanks
to
my
PhD
students
Weinan
Zhang,
Shuai
Yuan,
Bowei
Chen,
Xiaoxue
Zhao…
21/05/15
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49

50. For
more
informa(on,
please
refer
to
1. Shuai
Yuan,
Jun
Wang,
Real-­‐=me
Bidding
for
Online
Adver=sing:
Measurement
and
Analysis,
AdKDD’13
2. Weinan
Zhang,
Shuai
Yuan,
Jun
Wang,
Op=mal
Real-­‐Time
Bidding
for
Display
Adver=sing,
KDD’14
3. Bowei
Chen,
Shuai
Yuan,
and
Jun
Wang.
A
dynamic
pricing
model
for
unifying
programma=c
guarantee
and
real-­‐=me
bidding
in
display
adver=sing,
Best
Paper
Award,
ADKDD’14,
4. Weinan
Zhang
and
Jun
Wang,
Sta=s=cal
Arbitrage
Mining
for
Display
Adver=sing,
accepted
KDD’15
5. Shuai
Yuan,
Jun
Wang,
Bowei
Chen,
An
Empirical
Study
of
Reserve
Price
Op=misa=on
in
Real-­‐Time
Bidding,
CIKM’14
6. Bowei
Chen,
Jun
Wang,
A
latce
framework
for
pricing
display
ad
op=ons
with
stochas=c
vola=lity
underlying
models,
under
submission,
2015
7. Bowei
Chen,
Jun
Wang,
Ingemar
Cox,
and
Mohan
Kankanhalli,
Mul=-­‐
Keyword
Mul=-­‐Click
Op=on
Contracts
for
Sponsored
Search
Adver=sing,
ACM
TOIST,
2015
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50

51. Thanks
for
your
acen(on
51