“Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness” by Garrett Johnson (Boston University), Randall Lewis (Netflix), & Elmar Nubbemeyer (Netflix)

To measure the effects of advertising, marketers must know how consumers would behave had they not seen the ads. The authors develop a methodology they call “Ghost Ads” which facilitates this comparison by identifying the control-group counterparts of the exposed consumers in a randomized experiment. Relative to Public Service Announcement (PSA) and Intent-to-Treat A/B tests, Ghost Ads can reduce the cost of experimentation, improve measurement precision, deliver the relevant strategic baseline, and work with modern ad platforms that optimize ad delivery in real-time. The authors have implemented their methodology at scale on the Google Display Network since 2015. Hundreds of advertisers have used Google’s experimentation platform to run over a thousand tests annually worth millions of dollars in ad spend.

Journal of Marketing Researchpaper:

Working paper version: