{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:05:35Z","timestamp":1786547135802,"version":"build-2736575974"},"posted":{"date-parts":[[2015]]},"group-title":"SSRN","reference-count":40,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.2139\/ssrn.2620078","type":"posted-content","created":{"date-parts":[[2015,6,24]],"date-time":"2015-06-24T11:01:56Z","timestamp":1435143716000},"source":"Crossref","is-referenced-by-count":3,"title":["Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-5855","authenticated-orcid":true,"given":"Garrett","family":"Johnson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Randall A.","family":"Lewis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elmar","family":"Nubbemeyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","author":"Adroll","year":"2014","journal-title":"State of the Industry: A close look at retargeting and the programmatic marketer"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1145\/2566486.2567967","article-title":"Designing and deploying online field experiments","author":"E Bakshy","year":"2014","journal-title":"Proceedings of the 23rd international conference on World wide web"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1145\/2229012.2229027","article-title":"Social influence in social advertising: evidence from field experiments","author":"E Bakshy","year":"2012","journal-title":"Proceedings of the 13th ACM Conference on Electronic Commerce"},{"issue":"3","key":"ref4","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1287\/mksc.2016.0982","article-title":"Experimental designs and estimation for online display advertising attribution in marketplaces","volume":"35","author":"J Barajas","year":"2016","journal-title":"Marketing Science"},{"issue":"3","key":"ref5","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1509\/jmr.13.0503","article-title":"Which products are best suited to mobile advertising? 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