Information, Bidding and Competition in
Sponsored Search
Jon Feldman
S. Muthukrishnan
Sponsored search from the advertiser perspective
• What is a search ad campaign?
• What are the goals of a search ad campaign?
• What information do we have to analyze and manage a
campaign?
• Given this information, how do we manage a campaign?
• How does competition affect a search ad campaign?
• Note: the word “auction” does not appear above...
Goals of this tutorial
• Detail the real experience of setting up and managing a
search ad campaign.
• Formulate general research questions inspired by these
details.
• Highlight examples of existing research.
• non-goal: complete survey of the Sponsored Search
literature
Users, advertisers, and the search engine
• Search engine determines which ads get shown, page
layout (and, of course, the search results).
• Search engine user determines which (if any) ad is
clicked, and whether to buy something.
...thus, even from the advertiser perspective, need to
reason about behavior of user and search engine.
Multiple perspectives
Algorithms
optimize stuff and prove bounds
What can you prove about
that heuristic?
Great proof... but is this
useful on real data?
Learning/IR/Stats
learn stuff from data and test it
Your mechanism is
biasing my data...!
Isn’t the Bayesean
assumption unsatisfying?
We can predict behavior
without always needing to
bound stuff.
Economics
model stuff to learn about the world
Your model ignores the nature of
the agents...!
Outline
First half (Jon):
• Parameters of a search campaign:
 Keywords, creatives, adgroups, match types, negative keywords,
geo, language, demographic, ad networks, delivery method,
scheduling, rotation, frequency capping, landing pages,
conversion tracking.
• Goals of a search campaign:
 Direct vs. branding
 Reaching the user
 Conversion attribution
• Information:
 Performance statistics, traffic estimation
Outline
Second half (Muthu):
• Bidding, campaign management
 Bidding strategy, optimization
 Keyword selection, bidding languages
• Competition
 Dynamics, vindictive strategies
Parameters of a search campaign
Ad Networks
• What is a good economic model for “outsourcing ads?”
“Competing Ad Auctions,” Ashlagi, Monderer, Tennenholtz, AAW 08
“Competing Keyword Auctions,” Liu, Chen, Whinston, AAW 08
Automatic bidding
• Much more on bidding types, strategy and algorithms
later.... (Muthu)
• How can an auctioneer successfully mix bidding types?
“General Auction Mechanism for Search Advertising,”
Aggarwal, M., Pal, Pal, WWW ‘09
Budget allocation
• How should a search engine allocate budget efficiently?
Pure online algorithms question:
``Adwords and Generalized Online Matching'', Mehta,
Saberi, Vazirani, Vazirani, JACM, 2007.
• What incentives do the resulting mechanism create?
“Multi-unit Auctions with budget limits,” Dobzinski, Lavi,
Nisan. FOCS 08.
Ad rotation
• Clear application of explore/exploit model.
• What are the implications of having the search engine
automate the learning, rather than the advertiser?
Frequency capping (not on search ads)
• Marketing rule of thumb: People should see your ad
between 3 and 7 times.
• Is this still true for online ads? For what formats is this a
useful rule?
• We are now armed with massive data; was this even true
to begin with?
Keywords, queries and broad match
“keyword” = the criteria entered by an advertiser
“query” = the data entered by the user
“broad match” = search engine determines if (and to what
degree) a keyword matches a query
• When does a keyword match a query?
• Are keywords the right language for advertisers to
express their preference for queries?
 Other alternatives?
Learning / NLP questions
• Can user intention be gleaned from queries?
• Can advertiser intention be gleaned from sets of
keywords, ad creatives, landing pages?
"Logistic Regression and Collaborative Filtering for Sponsored Search Term
Recommendation", Bartz, Murthi, Sebastian, AAW 06
“Keyword generation for search engine advertising using semantic similarity
between terms,” Abhishek, EC 07
Economic questions
• Is it a good idea to opt into broad match?
“Bid Optimization for Broad Match Ad Auctions,” Even-Dar, Mirrokni,
Mansour, M., Nadav.
“To Broad-Match or Not to Broad Match: An Auctioneer’s Dilemma?,” Singh,
Roychowdhury, AAW 08
• How do targeting restrictions affect efficiency of ad
placements?
“The Cost of Inexpressiveness in Advertisement Auctions,” Benisch, Sadeh,
Sandholm, AAW 08.
“The Cost of Conciseness in Sponsored Search Auctions,” Abrams, Ghosh,
Vee, WINE 07.
• How does competition express itself across the
keyword/query network?
Algorithmic questions
• How do match ads to queries (online) to maximize
efficiency?
“An Optimal Online Bipartite Matching Algorithm,” Karp, Vazirani, Vazirani,
STOC ’90.
“Optimize-and-Dispatch Architecture for Expressive Ad Auctions,” Parkes
and Sandholm, AAW 05.
“Offline Optimization for Online Ad Allocation,” F., Mehta, Mirrokni, M., AAW
09
Goals of a search campaign
Goals of advertising
• Direct vs. branding
“Direct” advertiser: selling something now
e.g.: online electronics retailer
“Branding” advertiser: building brand awareness
e.g.: national restaurant chain
Direct advertisers: easier to quantify goals
• Direct advertisers want sales
“conversion” = ad interaction that results in a sale
v = value(conversion) ≈ profit from sale
Goal: maximize v • #conversions - cost
$120.00
$100.00
....find point where
dCost / dConversions = v
cost
$80.00
$60.00
$40.00
$20.00
$0
5
10
15
conversions
20
25
Conversions via impressions
v = value(conversion) ≈ profit from sale
Risk-neutral model:
value(click) = v • Pr[conversion | click]
value(impression) = value(click) • Pr[click | impression]
Therefore:
value(impr.) = v • Pr[conversion | click] • Pr[click | impr.]
Direct advertisers
val(impr.) = v • Pr[conv. | click] • Pr[click | impr.]
So generating value is (in principle) simple:
For each search query:
- bidders declare value(impression)
- SE runs an efficient, truthful auction.
...but:
...how do we learn those probabilities?
...who is in the best position to learn them?
...how do we elicit values on a per-query basis?
Bid for clicks, rank by impression value
• Current SE common practice:
- Bidders declare val(click) (= v • Pr[conv | click])
....bid declared on keyword level.
- SE estimates Pr[click | impr.],
ranks by val(impr.) = val(click) • Pr[click | impr.]
....ranking at query time.
price: min bid required to achieve rank
pay only on a click
• Implicit assumption: v, Pr[conversion | click] both
independent of query.
Learning click probabilities: user click models
...but ...how do users interact with ads?
“Separable” model:
- User looks at ad in position j with prob. p(j).
p(1) > p(2) > ... > p(k)
- If user looks at ad, user clicks on ad with prob. q(i).
• Ranking by b(i) q(i) maximizes efficiency
• Basis of most mech. design work in sponsored search
Learning click probabilities: user click models
...but ...how do users really interact with ads?
CS:
“Sponsored Search Auctions with Markovian
Users,” Aggarwal, F., M., Pal, WINE 08.
“A Cascade Model for Externalities in Sponsored
Search,” Kempe, Mahdian, WINE 08.
Learning / IR: “An Experimental Comparison of Click Position-Bias
Models,” Craswell, Zoeter, Taylor, Ramsey, WSDM 2008
“A User Browsing Model... ,” Dupret, Piwowarsky, SIGIR 08
“Click Chain Model in Web Search,” Guo, Liu, Kannan,
Minka, Taylor, Wang, Faloutsos, WWW 09
Econ:
“Position Auctions with Consumer Search,” Athey,
Ellison, working paper, 2007.
Learning click probabilities: user click models
“Position Auctions with Consumer Search,” Athey,
Ellison, working paper, 2007.
• User looking for something
• Will search down the sponsored links until
cognitive cost of looking > expected value from looking
SE: arrange ads to minimize user cost, maximize ad value
Advertiser: proper targeting, build user trust.
Learning and Incentives
“Dynamic Cost-Per-Action Mechanisms and Applications to
Online Advertising,” Nazerzadeh, Saberi, Vohra, WWW’08
• Repeated sales of clicks c1, c2, c3, ..., cn.
• Mechanism decides to give click i to advertiser j based on history.
• Advertiser j then learns how valuable the click was, reports v(i, j).
“Characterizing Truthful Multi-Armed Bandit mechanisms,”
Babaioff, Sharma, Slivkins, EC 09.
The Price of Truthfulness for Pay-Per-Click Auctions,”
Devanur, Kakade, EC 09
• If mechanism truthful, each sale must explore or exploit, not both.
Conversion Attribution
value(click) = v • Pr[conversion | click]
What role does the search ad click play in “generating” the
conversion?
“Integrated Multichannel Communication Strategies: Evaluating the
Return on Marketing objectives...,” Briggs, Krishnan, Borin, Journal of
Interactive Marketing, 2005.
Brand and Search
In what phases of this
process is (sponsored)
search useful?
Model still relevant?
“Internet Shopping Shoots Holes in the Purchase Funnel” J. Henry,
bnet.com, Sept. 2008.
Information
Always an aggregated view
• Each auction a small part of overall campaign
performance.
• Huge diversity of queries, competition, targeting features.
• Noise in every prediction
 The dynamics of GSP are a second-order concern.
Impressions, Clicks, Cost and Position
• Always see aggregated view over diverse set of queries
 How should an advertiser react to information?
$1.20
$1.00
cost
$0.80
?
$0.60
$0.40
?
$0.20
$5
10
15
20
25
conversions
$120.00
$100.00
$80.00
cost
have:
0
$60.00
$40.00
$20.00
$-
need:
0
5
10
15
conversions
20
25
Traffic Projections
Keyword-based estimates
• How can the SE predict the
effect of a bid change?
• Bid affects query mix, and
therefore conversion
probability and value.
Simulation-based estimates
Traffic predictions from click simulation
• If an advertiser had placed differently, what would have
happened?
click
Ad A
Ad C
click?
Ad B
Ad A
click?
Ad C
Ad B
Ad D
Ad D
Acting on traffic predictions
• How volatile is click traffic? Does volatility come from...
 competition?
 query (inventory) variance?
 changes to SE algorithms?
• Can traffic be separated effectively across keywords?
 keywords are related to each other
• Need simple, robust bidding strategies (stay tuned).
Web Analytics
Portfolio of auctions
Advertisers face a complex, noisy, dynamic environment.
- Important to target effectively, monitor performance.
- find signals in massive data sets
- Need fast, scalable, simple optimization strategies.
...go have some coffee.
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Information, Bidding Languages and Competition in