Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces

Joel Barajas (), Ram Akella (), Marius Holtan () and Aaron Flores ()
Additional contact information
Joel Barajas: University of California, Santa Cruz, California 95064
Ram Akella: School of Information, University of California, Berkeley, California 94720; and University of California, Santa Cruz, California 95064
Marius Holtan: AOL Research, Palo Alto, California 94306
Aaron Flores: AOL Research, Palo Alto, California 94306

Marketing Science, 2016, vol. 35, issue 3, 465-483

Abstract: Online Display Advertising’s importance as a marketing channel is partially due to its ability to attribute conversions to campaigns. Current industry practice to measure ad effectiveness is to run randomized experiments using placebo ads, assuming external validity for future exposures. We identify two different effects, i.e., a strategic effect of the campaign presence in marketplaces, and a selection effect due to user targeting; these are confounded in current practices. We propose two novel randomized designs to: (1) estimate the overall campaign attribution without placebo ads, (2) disaggregate the campaign presence and ad effects. Using the Potential Outcomes Causal Model, we address the selection effect by estimating the probability of selecting influenceable users. We show the ex-ante value of continuing evaluation to enhance the user selection for ad exposure mid-flight. We analyze two performance-based (CPA) and one Cost-Per-Impression (CPM) campaigns with 20 million users each. We estimate a negative CPM campaign presence effect due to cross product spillovers. Experimental evidence suggests that CPA campaigns incentivize selection of converting users regardless of the ad, up to 96% more than CPM campaigns, thus challenging the standard practice of targeting most likely converting users.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0982 .

Keywords: online advertising; experimental design; user targeting; field experiments; Bayesian estimation; econometrics (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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