Incrementality Bidding & Attribution

40 Pages Posted: 6 Mar 2018 Last revised: 12 Mar 2018

Date Written: February 27, 2018

Abstract

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

Suggested Citation

Lewis, Randall A. and Wong, Jeffrey, Incrementality Bidding & Attribution (February 27, 2018). Available at SSRN: https://ssrn.com/abstract=3129350 or http://dx.doi.org/10.2139/ssrn.3129350

Randall A. Lewis (Contact Author)

Netflix ( email )

Los Gatos, CA
United States
312-RA-LEWIS (Phone)

HOME PAGE: http://www.econinformatics.com/

Jeffrey Wong

Netflix ( email )

Los Gatos, CA
United States

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