Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness

Abstract

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. The authors show that, relative to public service announcement 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 also describe a variant, “predicted ghost ad” methodology, which is compatible with online display advertising platforms; their implementation records more than 100 million predicted ghost ads per day. The authors demonstrate the methodology with an online retailer's display retargeting campaign. They show novel evidence that retargeting can work: the ads lifted website visits by 17.2% and purchases by 10.5%. Compared with intent-to-treat and public service announcement experiments, advertisers can measure ad lift just as precisely while spending at least an order of magnitude less.