Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders

37 Pages Posted: 12 Aug 2017 Last revised: 10 Apr 2018

See all articles by Negin Golrezaei

Negin Golrezaei

Massachusetts Institute of Technology - MIT Sloan School of Management

Max Lin

Google Inc.

Vahab Mirrokni

Google Inc.

Hamid Nazerzadeh

University of Southern California - Marshall School of Business

Date Written: August 10, 2017

Abstract

Due to its simplicity and desirable incentive properties, the second price auction has been the prevalent auction format used by advertising exchanges. However, even with the optimized choice of reserve prices, this auction is not revenue optimal when the bidders are heterogeneous and their valuation distributions differ significantly. In order to optimize the revenue of advertising exchanges, we propose an auction format called the boosted second price auction, which assigns a boost value to each bidder. The auction favors bidders with higher boost values and allocates the item to the bidder with the highest boosted bid.

We propose a data-driven approach to optimize boost values using the previous bids of the bidders. Our analysis of auction data from a large online advertising exchange shows that our algorithm can improve revenue by up to 6%. Furthermore, we observe that the data-driven algorithm assigns higher boosts to advertisers with more stable bidding behavior. We show how this connects to Myerson’s optimal mechanism design framework for heterogeneous bidders and propose a boosted second price auction, where bid distributions with lower inverse hazard rates receive a higher boost. We also establish conditions that guarantee that these boosted auctions will increase revenue over the second price auctions and obtain a high fraction of the optimal revenue.

Keywords: Boosted Second-price Auctions, Online Advertising, Heterogeneity, Brand, Retargeting

Suggested Citation

Golrezaei, Negin and Lin, Max and Mirrokni, Vahab and Nazerzadeh, Hamid, Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders (August 10, 2017). Available at SSRN: https://ssrn.com/abstract=3016465 or http://dx.doi.org/10.2139/ssrn.3016465

Negin Golrezaei (Contact Author)

Massachusetts Institute of Technology - MIT Sloan School of Management ( email )

100 Main Street
Building E62-577
Cambridge, MA 02142
United States

Max Lin

Google Inc. ( email )

Vahab Mirrokni

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Hamid Nazerzadeh

University of Southern California - Marshall School of Business ( email )

Bridge Memorial Hall
Los Angeles, CA 90089
United States

HOME PAGE: http://www-bcf.usc.edu/~nazerzad/

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