2019/03/27 by Caio Waisman, Waisman, Caio, Navdeep S. Sahni +5
Business, Management and Accounting · Decision Sciences · Social Sciences · #Applications (stat.AP) #Auction Theory and Applications #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Media Influence and Politics
paper · pdf · doi:10.48550/arxiv.1903.11198
openalex publication_date 2019/03/27 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
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.