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Internet Advertising Auctions

Internet Advertising Auctions

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Internet Advertising Auctions

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  1. Internet Advertising Auctions David Pennock, Yahoo! Research - New York Contributed slides:K.Asdemir, H.Bhargava, J.Feng, S.Lahaie, M.Schwarz

  2. Advertising Then and Now • Then: Think real estatePhone callsManual negotiation“Half doesn’t work” • Now: Think Wall StreetAutomation, automation, automationAdvertisers buy contextual attention: User i on page j at time tComputer learns what ad is bestComputer mediates ad sales: Auction!Computer measures which ads work

  3. Advertising Then & Now: Video

  4. Auctions Machine learning Optimization Sales Economics &Computer Science Statistics &Computer Science Operations Research Computer Science Marketing Advertising: NowTools Disciplines

  5. search “las vegas travel”, Yahoo! “las vegas travel” auction Sponsored search auctions Space next to search results is sold at auction

  6. Ad exchanges

  7. Outline • Motivation: Industry facts & figures • Introduction to sponsored search • Brief and biased history • Allocation and pricing: Google vs old Yahoo! • Incentives and equilibrium • Ad exchanges • Selected survey of research • Prediction markets

  8. eBay 216 million/month Google / Yahoo! 11 billion/month (US) Auctions Applications

  9. eBay Google Auctions Applications

  10. eBay Google Auctions Applications

  11. Newsweek June 17, 2002“The United States of EBAY” • In 2001: 170 million transactions worth $9.3 billion in 18,000 categories “that together cover virtually the entire universe of human artifacts—Ferraris, Plymouths and Yugos; desk, floor, wall and ceiling lamps; 11 different varieties of pockets watches; contemporary Barbies, vintage Barbies, and replica Barbies.” • “Since everything that transpires on Ebay is recorded, and most of it is public, the site constitutes a gold mine of data on American tastes and preoccupations.”

  12. “The United States of Search” • 11 billion searches/month • 50% of web users search every day • 13% of traffic to commercial sites • 40% of product searches • $8.7 billion 2007 US ad revenue (41% of $21.2 billion US online ads; 2% of all US ads) • Still ~20% annual growth after years of nearly doubling • Search data: Covers nearly everything that people think about: intensions, desires, diversions, interests, buying habits, ...

  13. Online ad industry revenue

  14. Introduction tosponsored search What is it? Brief and biased history Allocation and pricing: Google vs Yahoo! Incentives and equilibrium

  15. search “las vegas travel”, Yahoo! “las vegas travel” auction Sponsored search auctions Space next to search results is sold at auction

  16. Sponsored search auctions • Search engines auction off space next to search results, e.g. “digital camera” • Higher bidders get higher placement on screen • Advertisers pay per click: Only pay when users click through to their site; don’t pay for uncliked view (“impression”)

  17. Sponsored search auctions • Sponsored search auctions are dynamic and continuous: In principle a new “auction” clears for each new search query • Prices can change minute to minute;React to external effects, cyclical & non-cyc • “flowers” before Valentines Day • Fantasy football • People browse during day, buy in evening • Vioxx

  18. Example price volatility: Vioxx

  19. Sponsored search today • 2007: ~ $10 billion industry • ‘06~$8.5B ‘05~$7B ‘04~$4B ‘03~$2.5B ‘02~$1B • $8.7 billion 2007 US ad revenue (41% of US online ads; 2% of all US ads) • Resurgence in web search, web advertising • Online advertising spending still trailing consumer movement online • For many businesses, substitute for eBay • Like eBay, mini economy of 3rd party products & services: SEO, SEM

  20. Sponsored SearchA Brief & Biased History • Idealab  (no relation to • Crazy (terrible?) idea, meant to combat search spam • Search engine “destination” that ranks results based on who is willing to pay the most • With algorithmic SEs out there, who would use it? • GoTo   Yahoo! Search Marketing • Team w/ algorithmic SE’s, provide “sponsored results” • Key: For commercial topics (“LV travel”, “digital camera”) actively searched for, people don’t mind (like?) it • Editorial control, “invisible hand” keep results relevant • Enter Google • Innovative, nimble, fast, effective • Licensed Overture patent (one reason for Y!s ~5% stake in G)

  21. Thanks: S. Lahaie Sponsored SearchA Brief & Biased History • Overture introduced the first design in 1997: first price, rank by bid • Google then began running slot auctions in 2000: second price, rank by revenue (bid * CTR) • In 2002, Overture (at this point acquired by Yahoo!) then switched to second-price. Still uses rank by bid; Moving toward rank by revenue

  22. Sponsored SearchA Brief & Biased History • In the beginning: • Exact match, rank by bid, pay per click, human editors • Mechanism simple, easy to understand, worked, somewhat ad hoc • Today & tomorrow: • “AI” match, rank by expected revenue (Google), pay per click/impression/conversion, auto editorial, contextual (AdSense, YPN), local, 2nd price (proxy bid), 3rd party optimizers, budgeting optimization, exploration exploitation, fraud, collusion, more attributes and expressiveness, more automation, personalization/targeting, better understanding (economists, computer scientists)

  23. Sponsored Search ResearchA Brief & Biased History • Circa 2004 • Weber & Zeng, A model of search intermediaries and paid referrals • Bhargava & Feng, Preferential placement in Internet search engines • Feng, Bhargava, & PennockImplementing sponsored search in web search engines: Computational evaluation of alternative mechanisms • Feng, Optimal allocation mechanisms when bidders’ ranking for objects is common • Asdemir, Internet advertising pricing models • Asdemir, A theory of bidding in search phrase auctions: Can bidding wars be collusive? • Mehta, Saberi, Vazirani, & VaziranAdWords and generalized on-line matching • Key papers, survey, and ongoing research workshop series • Edelman, Ostrovsky, and Schwarz, Internet Advertising and the Generalized Second Price Auction, 2005 • Varian, Position Auctions, 2006 • Lahaie, Pennock, Saberi, Vohra, Sponsored Search, Chapter 28 in Algorithmic Game Theory, Cambridge University Press, 2007 • 1st-3nd Workshops on Sponsored Search Auctions4th Workshop on Ad Auctions -- Chicago Julu 8-9, 2008

  24. Allocation and pricing • Allocation • Yahoo!: Rank by decreasing bid • Google: Rank by decreasing bid * E[CTR] (Rank by decreasing “revenue”) • Pricing • Pay “next price”: Min price to keep you in current position

  25. Yahoo Allocation: Bid Ranking search “las vegas travel”, Yahoo! “las vegas travel” auction pays $2.95per click pays $2.94 pays $1.02 ... bidder ipays bidi+1+.01

  26. Google Allocation: $ Ranking “las vegas travel” auction x E[CTR] = E[RPS] x E[CTR] = E[RPS] x E[CTR] = E[RPS] x E[CTR] = E[RPS] x E[CTR] = E[RPS]

  27. TripReservations Expedia LVGravityZone etc... Google Allocation: $ Ranking search “las vegas travel”, Google “las vegas travel” auction pays 3.01*.1/.2+.01 = 1.51per click x .1 = .301 x .2 = .588 pays 2.93*.1/.1+.01 = 2.94 x .1 = .293 pays bidi+1*CTRi+1/CTRi+.01 x E[CTR] = E[RPS] x E[CTR] = E[RPS]

  28. Aside: Second price auction(Vickrey auction) • All buyers submit their bids privately • buyer with the highest bid wins;pays the price of the second highest bid Only pays $120  $150 $120 $90 $50

  29. Incentive Compatibility(Truthfulness) • Telling the truth is optimal in second-price (Vickrey) auction • Suppose your value for the item is $100;if you win, your net gain (loss) is $100 - price • If you bid more than $100: • you increase your chances of winning at price >$100 • you do not improve your chance of winning for < $100 • If you bid less than $100: • you reduce your chances of winning at price < $100 • there is no effect on the price you pay if you do win • Dominant optimal strategy: bid $100 • Key: the price you pay is out of your control • Vickrey’s Nobel Prize due in large part to this result

  30. Vickrey-Clark-Groves (VCG) • Generalization of 2nd price auction • Works for arbitrary number of goods, including allowing combination bids • Auction procedure: • Collect bids • Allocate goods to maximize total reported value (goods go to those who claim to value them most) • Payments: Each bidder pays her externality;Pays: (sum of everyone else’s value without bidder) - (sum of everyone else’s value with bidder) • Incentive compatible (truthful)

  31. Is Google pricing = VCG? Well, not really … Put Nobel Prize-winning theories to work. Google’s unique auction model uses Nobel Prize-winning economic theory to eliminate the winner’s curse – that feeling that you’ve paid too much. While the auction model lets advertisers bid on keywords, the AdWords™ Discounter makes sure that they only pay what they need in order to stay ahead of their nearest competitor.

  32. VCG pricing • (sum of everyone else’s value w/o bidder) - (sum of everyone else’s value with bidder) • CTRi = advi * posi (key “separability” assumption) • pricei = 1/advi*(∑j<ibidj*CTRj + ∑j>ibidj*advj*posj-1 -∑j≠ibidj*CTRj ) = 1/advi*(∑j>ibidj*advj*posj-1 - ∑j>ibidj*CTRj ) • Notes • For truthful Y! ranking set advi = 1. But Y! ranking technically not VCG because not efficient allocation. • Last position may require special handling

  33. Next-price equilibrium • Next-price auction: Not truthful: no dominant strategy • What are Nash equilibrium strategies? There are many! • Which Nash equilibrium seems “focal” ? • Locally envy-free equilibrium[Edelman, Ostrovsky, Schwarz 2005]Symmetric equilibrium[Varian 2006]Fixed point where bidders don’t want to move  or  • Bidders first choose the optimal position for them: position i • Within range of bids that land them in position i, bidder chooses point of indifference between staying in current position and swapping up with bidder in position i-1 • Pure strategy (symmetric) Nash equilibrium • Intuitive: Squeeze bidder above, but not enough to risk “punishment” from bidder above

  34. Next-price equilibrium • Recursive solution:posi-1*advi*bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1 posi-1*advi • Nomenclature:Next price = “generalized second price” (GSP)

  35. Ad exchanges Right Media Expressiveness

  36. Online Advertising Evolution • Direct: Publishers sell owned & operated (O&O) inventory • Ad networks: Big publishers place ads on affiliate sites, share revenueAOL, Google, Yahoo!, Microsoft • Ad exchanges: Match buy orders from advertisers with sell orders from publishers and ad networksKey distinction: exchange does not “own” inventory

  37. Advertisers Publishers Netflix MySpace Vonage Demand Six Apart … Looksmart Monster Inventory … Exchange Networks CPX Tribal … [Source: Ryan Christensen] Exchange Basics

  38. [Source: Ryan Christensen] Right Media Publisher Experience • Publisher can select / reject specific advertisers • Green = linked network • Light Blue = direct advertiser • Publishers can traffic their own deals by clicking “Add Advertiser” The publisher can approve creative from each advertiser

  39. [Source: Ryan Christensen] Right Media Advertiser Experience • Advertisers can set targets for CPM, CPC and CPA campaigns • Set budgets and frequency caps • Locate publishers, upload creative and traffic campaigns

  40. Expressiveness • “I’ll pay 10% more for Males 18-35” • “I’ll pay $0.05 per impression, $0.25 per click, and $5.25 per conversion” • “I’ll pay 50% more for exclusive display, or w/o Acme” • “My marginal value per click is decreasing/increasing” • “Never/Always show me next to Acme”“Never/Always show me on adult sites”“Show me when is 1st algo search result” • “I need at least 10K impressions, or none” • “Spread out my exposure over the month” • “I want three exposures per user, at least one in the evening” Design parameters: Advertiser needs/wants,computational/cognitive complexity, revenue

  41. Expressiveness Example • Competition constraints b xCTR = RPS 3 x .05 = .15 1 x .05 = .05

  42. Expressiveness Example monopoly bid • Competition constraints b xCTR = RPS 4 x .07 = .28

  43. Expressiveness: Design • Multi-attribute bidding

  44. Expressiveness: Less is More • Pay per conversion: Advertisers pay for user actions (sales, sign ups, extended browsing, ...) • Network sends traffic • Advertisers rate users/types 0-100Pay in proportion • Network learns, optimizes traffic, repeat • Fraud: Short-term gain only: If advertisers lie, they stop getting traffic

  45. Expressiveness: Less is More • “I’m a dry cleaner in Somerset, New Jersey with $100/month. Advertise for me.” • Can advertisers trust network to optimize?

  46. Stats/ML/OptEngine Stats/ML/OptEngine Stats/ML/OptEngine Stats/ML/OptEngine Stats/ML/OptEngine Coming Convergence:ML and Mechanism Design Mechanism(Rules) e.g. Auction,Exchange, ...

  47. ML Inner Loop • Optimal allocation (ad-user match) depends on: bid, E[clicks], E[sales], relevance, ad, advertiser, user, context (page, history), ... • Expectations must be learned • Learning in dynamic setting requires exploration/exploitation tradeoff • Mechanism design must factor all this in! Nontrivial.

  48. Selected Survey ofInternet Advertising Research

  49. Source: S. Lahaie An Analysis of Alternative Slot Auction Designs for Sponsored Search • Sebastien Lahaie, Harvard University* • *work partially conducted at Yahoo! Research • ACM Conference on Electronic Commerce, 2006

  50. Source: S. Lahaie Objective • Initiate a systematic study of Yahoo! and Google slot auctions designs. • Look at both “short-run” incomplete information case, and “long-run” complete information case.