Zooming In on Paid Search Ads--A Consumer-Level Model Calibrated on Aggregated Data

Oliver J. Rutz () and Michael Trusov ()
Additional contact information
Oliver J. Rutz: Foster School of Business, University of Washington, Seattle, Washington 98195
Michael Trusov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742

Marketing Science, 2011, vol. 30, issue 5, 789-800

Abstract: We develop a two-stage consumer-level model of paid search advertising response based on standard aggregated data provided to advertisers by major search engines such as Google or Bing. The proposed model uses behavioral primitives in accord with utility maximization and allows recovering parameters of the heterogeneity distribution in consumer preferences. The model is estimated on a novel paid search data set that includes information on the ad copy. To that end, we develop an original framework to analyze composition and design attributes of paid search ads. Our results allow us to correctly evaluate the effects of specific ad properties on ad performance, taking consumer heterogeneity into account. Another benefit of our approach is allowing recovery of preference correlation across the click-through and conversion stage. Based on the estimated correlation between price- and position-sensitivity, we propose a novel contextual targeting scheme in which a coupon is offered to a consumer depending on the position in which the paid search ad was displayed. Our analysis shows that total revenues from conversion can be increased using this targeting scheme while keeping cost constant.

Keywords: Internet; paid search advertising; aggregate data; choice modeling; Bayesian methods (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1110.0647 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:30:y:2011:i:5:p:789-800

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().