Empirical Price Modeling for Sponsored Search

We present a characterization of empirical price data from sponsored search auctions. We show that simple models drawing bid values independently from a fixed distribution can be tuned to match empirical data on average, but still fail to account for deviations observed in individual auctions. Hypothesizing that these deviations are due to strategic bidding, we define measures of “jamming” behavior and show that actual auctions exhibit significantly more jamming than predicted by such models. Correspondingly, removing the jamming bids from observed auction data yields a much closer fit. We demonstrate that this characterization is a revealing tool for analysis, using model parameter values and measures of jamming to summarize the effects of query modifers on a set of keyword auctions.

• Type: Conference
• Year: 2007
• Where: WINE
• Authors: Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael Kearns
• Keywords: Economy, Empirical Price Data, Models Drawing Bid, Search Auctions, WINE 2007