Incrementality Bidding and Attribution
This addresses the challenge of measuring advertising effectiveness for marketers and advertisers, but it appears incremental as it builds on existing machine learning and causal econometrics foundations.
The paper tackles the problem of quantifying the causal effect of digital advertising (incrementality) by proposing a unified methodology for bidding and attribution, which is expected to significantly improve return on investment.
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.