Beyond Last-Click: An Optimal Mechanism for Ad Attribution

TL;DR

This paper introduces a new theoretically grounded mechanism, Peer-Validated Mechanism (PVM), for ad attribution that outperforms traditional Last-Click methods in accuracy and fairness, especially in homogeneous settings.

Contribution

The paper develops a novel DSIC mechanism for ad attribution, proving its optimality in homogeneous cases and demonstrating improved performance over existing heuristics.

Findings

PVM outperforms LCM in accuracy and fairness.

PVM is the optimal DSIC mechanism in homogeneous settings.

Theoretical bounds for PVM's accuracy are established.

Abstract

Accurate attribution for multiple platforms is critical for evaluating performance-based advertising. However, existing attribution methods rely heavily on the heuristic methods, e.g., Last-Click Mechanism (LCM) which always allocates the attribution to the platform with the latest report, lacking theoretical guarantees for attribution accuracy. In this work, we propose a novel theoretical model for the advertising attribution problem, in which we aim to design the optimal dominant strategy incentive compatible (DSIC) mechanisms and evaluate their performance. We first show that LCM is not DSIC and performs poorly in terms of accuracy and fairness. To address this limitation, we introduce the Peer-Validated Mechanism (PVM), a DSIC mechanism in which a platform's attribution depends solely on the reports of other platforms. We then examine the accuracy of PVM across both homogeneous and heterogeneous settings, and provide provable accuracy bounds for each case. Notably, we show that PVM is the optimal DSIC mechanism in the homogeneous setting. Finally, numerical experiments are conducted to show that PVM consistently outperforms LCM in terms of attribution accuracy and fairness.