Attribution Modeling Increases Efficiency of Bidding in Display Advertising

TL;DR

This paper demonstrates that integrating attribution modeling into bidding strategies enhances efficiency in display advertising by better estimating conversion probabilities and adjusting bids accordingly.

Contribution

It introduces a novel approach that incorporates attribution modeling directly into the bidding process, improving performance over standard methods.

Findings

Improved bidding efficiency demonstrated on real traffic data.

Effective offline and online performance gains shown.

Attribution-aware bidding outperforms traditional methods.

Abstract

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising. Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.