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Parallel Experimentation and Competitive Interference on Online Advertising Platforms

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Listed:
  • Caio Waisman
  • Navdeep S. Sahni
  • Harikesh S. Nair
  • Xiliang Lin

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, 2019. "Parallel Experimentation and Competitive Interference on Online Advertising Platforms," Papers 1903.11198, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:1903.11198
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    References listed on IDEAS

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    Cited by:

    1. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    2. Geng, Tong & Lin, Xiliang & Nair, Harikesh S. & Hao, Jun & Xiang, Bin & Fan, Shurui, 2020. "Comparison Lift: Bandit-Based Experimentation System for Online Advertising," Research Papers 3904, Stanford University, Graduate School of Business.
    3. Caio Waisman & Brett R. Gordon, 2023. "Multi-cell experiments for marginal treatment effect estimation of digital ads," Papers 2302.13857, arXiv.org, revised Jan 2024.
    4. Garrett Johnson & Julian Runge & Eric Seufert, 2022. "Privacy-Centric Digital Advertising: Implications for Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 9(1), pages 49-54, June.

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