Incrementality Bidding & Attribution

Author: Randall A. Lewis, Netflix, Inc. 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 randomization, training, cross validation, scoring, and conversion attribution in a causal model of advertising’s effects. Thanks to this method, Netflix has benefited by identifying many cases where traditional models were either overspending or underspending, leading to a significant improvement in the return on investment of advertising. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/