Parallel Experimentation and Competitive Interference on Online Advertising Platforms

Caio Waisman (), Navdeep S. Sahni (), Harikesh S. Nair () and Xiliang Lin ()
Additional contact information
Caio Waisman: Northwestern University Kellogg School of Management, Evanston, Illinois 60208
Navdeep S. Sahni: Stanford University, Stanford, California
Harikesh S. Nair: Google LLC, Mountain View, California 94043
Xiliang Lin: Google LLC, Mountain View, California 94043

Marketing Science, 2025, vol. 44, issue 2, 437-456

Abstract: This paper studies the measurement of advertising effects on online platforms when parallel experimentation occurs, that is, when multiple advertisers experiment concurrently. It provides a framework that makes precise how parallel experimentation affects the experiment’s value: while ignoring parallel experimentation yields an estimate of the average effect of advertising in place, which has limited value in decision making in an environment with variable advertising competition, accounting for parallel experimentation captures the actual uncertainty advertisers face due to competitive actions. It then implements an experimental design that enables the estimation of these effects on JD.com, a large e-commerce platform that is also a publisher of digital ads. Using traditional and kernel-based estimators, it shows that not accounting for competitive actions can result in the advertiser inaccurately estimating the advertising lift by a factor of two or higher, which can be consequential for decision making.

Keywords: experimentation; A/B/n testing; causal inference; digital advertising; e-commerce; platforms (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.2022.0194 (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:44:y:2025:i:2:p:437-456

Access Statistics for this article

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