1 Towards a Generic Bidding Standard for Online Advertising Sihem Amer-Yahia Sebastien Lahaie David Pennock Yahoo! Research

Research Advertising Then and Now Then: Think real estate Phone calls Manual negotiation “Half doesn’t work” Now: Think Wall Street Computer learns what ad is best Computer mediates ad sales: Auction Computer measures which ads work Advertisers buy contextual events: User i views/clicks/converts on page j at time t automation automation automation

Research Today and Tomorrow Today: Multiple competing “black box” networks Proprietary data and interfaces Silo-ed ad types (display, search, contextual, affiliate), largely historical artifacts Tomorrow (hopefully): Open standard bidding language For advertisers and publishers Portable among ad networks Unified ad types

Research 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

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

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

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

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

Yahoo! Research 9 Ad Serving Engine Ad campaigns in XML Advertisers Ad processing platform Ad inventory database compiler common data structure Standard Data Access Layer Get inventory Update budget Update impressions Pricing, allocation, spend management algorithms Ad NetworksPublishers

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:

Yahoo! Research Nesting Tree structureForest (additive) Google, Microsoft, Yahoo! offer this

Yahoo! Research Nesting In general, nodes further annotated with volume constraints

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

Yahoo! Research 14 Display Advertising sale to young adults Buy one get one free Other dimensions:

Yahoo! Research 15 Display Advertising sale to young adults Buy one get one free

Yahoo! Research 16 Sponsored Search Cheap Flights to San Francisco Save Up to 65% On Flights to CA Plus $10 Instant Discount "flight promotion“ "New York flight“ "San Francisco flight“ "direct flight"

Yahoo! Research 17 Contextual Advertising male sneaker sale Buy one get one free "promotion“ "sneakers“ "sports“ "running shoes“ "basketball"

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 => Polytime See: “An Expressive Auction Design for Online Display Advertising”, Lahaie et al., AAAI-2008 –Budget constraints only => Polytime See Vazirani

Yahoo! Research 19 Agenda Implement the language compiler Design the API Implement pricing and allocation algorithm using the data access API