Incrementality Bidding and Attribution

• URL: http://arxiv.org/abs/2208.12809v1
• Date: Thu, 25 Aug 2022 18:33:08 GMT
• Title: Incrementality Bidding and Attribution
• Authors: Randall Lewis and Jeffrey Wong
• Abstract summary: In digital advertising three major puzzle pieces are central to quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. We propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution.
• Score: 0.4511923587827302
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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