Financial methods in online advertising
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The document discusses the challenges and financial methods in online advertising, particularly the discrepancies in ad matching to users and web pages. It explores the potential for futures contracts and unified ad exchanges to stabilize pricing and reduce risks for advertisers and publishers. Additionally, it highlights the introduction of option contracts for better revenue management in sponsored search advertising.
![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).](https://image.slidesharecdn.com/iatp13-financialmethodsincomputationaladvertising-130806031709-phpapp01/85/Financial-methods-in-online-advertising-16-320.jpg)
![Multi-Keyword Multi-Click
Advertisement Option Contract
for Sponsored Search
[B.Chen
et
al.
2013]](https://image.slidesharecdn.com/iatp13-financialmethodsincomputationaladvertising-130806031709-phpapp01/85/Financial-methods-in-online-advertising-29-320.jpg)
![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](https://image.slidesharecdn.com/iatp13-financialmethodsincomputationaladvertising-130806031709-phpapp01/85/Financial-methods-in-online-advertising-31-320.jpg)
![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](https://image.slidesharecdn.com/iatp13-financialmethodsincomputationaladvertising-130806031709-phpapp01/85/Financial-methods-in-online-advertising-35-320.jpg)
Financial methods in online advertising
- 1. Financial Methods in Online Adver3sing Dr. Jun Wang UCL ACM SIGIR IATP13
- 2. Acknowledgements Bowei Chen, Mathema3cal Finance Shuai Yuan, Computer Science
- 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
- 29. Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search [B.Chen et al. 2013]
- 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.
- 40. The test of Arbitrage
- 41. 4141
- 42. 4242 § n=1, analyNcal proof + numerical valuaNon § n>=2, numerical valuaNon 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 Effects of Ad Options on Search Engine’s Revenue
- 43. 4343 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 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’.
- 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
- 46. Thanks for your aRen3on