Incrementality Bidding & Attribution
40 Pages Posted: 6 Mar 2018 Last revised: 12 Mar 2018
Date Written: February 27, 2018
The causal effect of showing an ad to a potential customer versus not, commonly referred to as “incrementality,” is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans the randomization, training, cross validation, scoring, and conversion attribution of advertising’s causal effects. Implementation of this approach is likely to secure a significant improvement in the return on investment of advertising.
Keywords: incrementality, ad effectiveness, machine learning, econometrics, real-time bidding, attribution, display advertising
JEL Classification: C22, C23, C26, C3, C52, M37, C93
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