Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness

Abstract

Abstract To measure the effects of advertising, marketers must know how consumers would behave had they not seen the ads. We develop a methodology we call `Ghost Ads,' which facilitates this comparison by identifying the control-group counterparts of the exposed consumers in a randomized experiment. We show that, 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. We also describe a variant `Predicted Ghost Ad' methodology that is compatible with online display advertising platforms; our implementation records more than 100 million predicted ghost ads per day. We demonstrate the methodology with an online retailer's display retargeting campaign. We show novel evidence that retargeting can work as the ads lifted website visits by 17.2% and purchases by 10.5%. Compared to Intent-to-Treat or PSA experiments, advertisers can measure ad lift just as precisely while spending at least an order of magnitude less.

Keywords: advertising effectiveness, field experiments, digital advertising

JEL Classification: M37, C93