Incrementality Bidding and Attribution

TL;DR

This paper introduces a unified, machine learning-based methodology for measuring advertising incrementality by integrating ad bidding, attribution, and experimentation, aiming to improve advertising ROI.

Contribution

It presents a novel computational model that combines bidding, attribution, and causal inference, advancing the accuracy and efficiency of measuring advertising effectiveness.

Findings

Unified model improves causal attribution accuracy

Enhances ROI through better incrementality measurement

Integrates multiple advertising processes into a single framework

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.