Towards a Generic Bidding Standard for Online Advertising
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Towards a Generic Bidding Standard for Online Advertising
Sihem Amer-Yahia
Sebastien Lahaie
David Pennock
Yahoo! Research
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ResearchResearch
Advertising Then and Now• Then: Think real estate
Phone callsManual negotiation“Half doesn’t work”
• Now: Think Wall StreetComputer learns what ad is bestComputer mediates ad sales: AuctionComputer measures which ads workAdvertisers buy contextual events:
User i views/clicks/convertson page j at time t
automation automation automation
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ResearchResearch
Today and Tomorrow• Today:
Multiple competing “black box” networksProprietary data and interfacesSilo-ed ad types (display, search, contextual, affiliate), largely historical artifacts
• Tomorrow (hopefully):Open standard bidding languageFor advertisers and publishersPortable among ad networksUnified ad types
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ResearchResearch
The Pitch
• An open standard for advertisers (& publishers) to describe their campaign goals that• Is expressive -- lets advertisers state
different value for different (bundles of) contextual events
• Is extensible -- doomed to fail otherwise• Is efficient -- is amenable to tractable
allocation and pricing algorithms
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Yahoo! Research 5
Why?
• Advertisers benefit most– Portability, choice– Flexibility, targeting
• Small publishers/networks & ad agencies: Reduced startup/switching costs
• Technology providers: New markets for analytics, campaign management tools
• Users: Better ads, maybe• Academics: Opens up black boxes
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Yahoo! Research 6
Why? Because.
• Open standards are flourishing despite non-obvious economics– Open source, wikinomics– APIs, widgets, mashups– Microformats– End of DRM– DomainKeys– OpenX.com– Openness one of Yahoo!’s three “Big Bets”, and
the only economically non-obvious one
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Yahoo! Research 7
Why Not?
• Largest networks & ad agencies may lose their “lock in”, have little incentive to join
• Sounds good in theory -- is a common standard even possible in practice? Microformats hard enough -- an ad language is massively more complex
• Online ad space evolving too fast -- not ready for standards yet
• Some features of current networks (e.g., real-time bidding based on “pixeled” users) inevitably will not be part of the standard
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Yahoo! Research 8
Language design goals
• Expressive
– DG1: Advertisers should be able to specify the type of campaign they want to instigate
– DG2: Advertisers should be able to specify their willingness to pay for individual contextual events
– DG3: Advertisers should be able to specify volume constraints over bundles of contextual events
– DG4: Advertisers should be able to specify budget constraints for their campaigns
– DG5: Advertisers should be able to specify contingency upon which payment will be made
– DG10: Publishers should be able to formally state the semantics used to interpret instances of the language
• Extensible
– DG6: The language should be extensible with respect to new targeting dimensions
– DG7: The language should be extensible with respect to new bidding mechanisms
• Efficient
– DG8: The language should allow for static checking of unrecognized or forbidden targeting dimensions, forbidden bids, and forbidden volume constraints
– DG9: The language should allow for efficient allocation and pricing algorithms
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Yahoo! Research 9
Ad Serving Engine
Ad campaigns in XML
Advertisers
Ad processing platform
Ad inventory database
compilercommon data structure
Standard Data Access Layer
Get inventoryUpdate budgetUpdate impressions
Pricing, allocation, spend managementalgorithms
Ad NetworksPublishers
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Yahoo! Research 10
Language definition and semantics
• A campaign is associated to an advertiser and is described by:
• adv is an advertiser id type is the campaign type (SS,DA,CA) id is the campaign identifier Budget is the advertiser's budget expr is an expression identifying an event (signal)
• Each expr is of the form:
)(),(),(exp 332211 valattvalattvalattr =≤≥=
r)words,udget, keyreative, bription, c, id, desc(adv, type exp
)(),(),25( northpositionCAlocationage ==≥
)r, . . . , r(p, bids, kexpexp 1
),, (type,valbid maxmin=
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Yahoo! Research
Nesting
• Tree structure • Forest (additive)
• Google, Microsoft, Yahoo! offer this
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
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Yahoo! Research
Nesting
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
In general, nodes further annotated with volume constraints
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Yahoo! Research 13
Language syntax: XML
• Flexible syntax which allows optional elements (needed for dimensions and bids)
• Tree structure facilitates nesting contextual events (signals) and helps to target more specific population segments
• Wide availability of XML tools (e.g., parsers and query engines) facilitates data processing
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Yahoo! Research 14
Display Advertising
<campaign type=“DA" adv="156" id="adv156-1" budget="$1000">
<description>sale to young adults</description>
<creative>Buy one get one free</creative>
<expr>
<dimension name="age" op= "=" value="[25-34]"/>
<bids> <cpm val="0.4" min="0" max="500"/> </bids>
</expr>
</campaign>
Other dimensions:
<dimension name="location“ op="=“ values="{CA,FL,WI,IL,TX}"/>
<dimension name="time" op="=" value="{am,pm}"/>
<dimension name="day" op="=" value="[Fri-Sun]"/>
<dimension name="position" op="=" value="south"/>
<creative>http://www.nike-camp.com/newSneakers.jpg</>
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Yahoo! Research 15
Display Advertising
<campaign type=“DA" adv="156" id="adv156-8" budget="$9000">
<description>sale to young adults</>
<creative>Buy one get one free</>
<expr>
<dimension name="age" op= "=" values="[25-35]"/>
<bids>
<cpm val="0.3" min="20K" max="100K"/>
<cpc val="0.4" min="200"/>
<cpa val="0.6"/>
</bids>
<expr>
<dimension name="location" op="=" values="CA"/>
<bids> <cpm val="0.3" max="50K"/> </bids>
</expr>
<expr>
<dimension name="location“ op="=“ values="{NY,NJ,PA}"/>
<bids> <cpc val="0.4" min="50"/> </bids>
</expr>
</expr>
</campaign>
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Yahoo! Research 16
Sponsored Search
<campaign type="SS" adv="5438" id="adv5438-78“ budget="$1000">
<description>Cheap Flights to San Francisco</>
<creative>Save Up to 65% On Flights to CA Plus $10 Instant Discount</>
<keywords>"flight promotion“ "New York flight“ "San Francisco flight“ "direct flight"</>
<expr>
<dimension name="location" op="=" val="east-coast"/>
<bids>
<cpc val="0.2"/>
<cpa val="0.5"/>
</bids>
</expr>
</campaign>
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Yahoo! Research 17
Contextual Advertising
<campaign type="CA" adv="156" id="adv156-1 "budget="$1500">
<description>male sneaker sale</>
<creative>Buy one get one free</>
<keywords>"promotion“ "sneakers“ "sports“ "running shoes“ "basketball"</>
<expr>
<dimension name="gender" op="=" values="male"/>
<bids>
<cpc val="0.4"/>
<cpa val="0.6" max="300"/>
</bids>
<expr>
<dimension name="position" op="=" values="north">
<bids> <cpm val="0.2"/> </bids>
</expr>
</expr>
</campaign>
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Yahoo! Research
Efficiency
• Static checking for logical errors
• Allocation depends on network interpretation: How to convert values on individual contextual events to values on bundles?– Greedy online allocation
(sponsored search, ad exchange model)
– Volume constraints only => PolytimeSee: “An Expressive Auction Design for Online Display Advertising”, Lahaie et al., AAAI-2008
– Budget constraints only => PolytimeSee Vazirani
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Yahoo! Research 19
Agenda
• Implement the language compiler
• Design the API
• Implement pricing and allocation algorithm using the data access API