Aggregation Bias in Sponsored Search Data: The Curse and the Cure

Vibhanshu Abhishek (), Kartik Hosanagar () and Peter S. Fader ()
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Vibhanshu Abhishek: Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Kartik Hosanagar: Wharton School of Business, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Peter S. Fader: Wharton School of Business, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Marketing Science, 2015, vol. 34, issue 1, 59-77

Abstract: Recently there has been significant interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks, and cost for each keyword in the advertiser’s campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intraday variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates. Specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We analytically demonstrate the existence of the bias and show the effect of the bias on the equilibrium of the SSA auction. Using a large data set from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.

Keywords: sponsored search; generalized second-price auctions; consumer choice models; Hierarchical Bayesian estimation; latent instrumental variables; aggregation bias (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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