Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising

L. Elisa Celis (), Gregory Lewis, Markus Mobius () and Hamid Nazerzadeh ()
Additional contact information
L. Elisa Celis: Department of Computer Science, University of Washington
Hamid Nazerzadeh: Marshall School of Business, University of Southern California

No 11-21, Working Papers from NET Institute

Abstract: Increasingly sophisticated tracking technology offers publishers the ability to offer targeted advertisements to advertisers. Such targeting enhances advertising efficiency by improving the match quality between advertisers and users, but also thins the market of interested advertisers. Using bidding data from Microsoft's Ad Exchange (AdECN) platform, we show that there is often a substantial gap between the highest and second highest willingness to pay. This motivates our new BIN-TAC mechanism, which is effective in extracting revenue when such a gap exists. Bidders can ``buy-it-now'', or alternatively ``take-a-chance'' in an auction, where the top d > 1 bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of distributions, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve. For the AdECN data, we use structural methods to estimate counterfactual revenues, and find that our BIN-TAC mechanism improves revenue by 11% relative to an optimal second-price auction.

Keywords: Advertising; Auctions; Mechanism Design (search for similar items in EconPapers)
JEL-codes: D44 L86 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2011-09, Revised 2011-11
New Economics Papers: this item is included in nep-cta, nep-ind and nep-mkt
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Related works:
Working Paper: Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising (2012)
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