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Parallel Experimentation and Competitive Interference on Online Advertising Platforms

Author

Listed:
  • 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)

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.

Suggested Citation

  • Caio Waisman & Navdeep S. Sahni & Harikesh S. Nair & Xiliang Lin, 2025. "Parallel Experimentation and Competitive Interference on Online Advertising Platforms," Marketing Science, INFORMS, vol. 44(2), pages 437-456, March.
    • Handle: RePEc:inm:ormksc:v:44:y:2025:i:2:p:437-456
    DOI: 10.1287/mksc.2022.0194
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