Segmentation, Incentives and Privacy

TL;DR

This paper introduces a novel prior-free segmentation model that ensures incentive compatibility, privacy, and efficient market segmentation without relying on a common prior, addressing a key gap in online advertising research.

Contribution

It proposes the first segmentation mechanism that simultaneously guarantees incentive compatibility, privacy, and effectiveness without assuming a shared prior.

Findings

First prior-free segmentation mechanism addressing incentives and privacy

Ensures incentive compatibility in data-driven segmentation

Achieves efficient market segmentation without common prior

Abstract

Data driven segmentation is the powerhouse behind the success of online advertising. Various underlying challenges for successful segmentation have been studied by the academic community, with one notable exception - consumers incentives have been typically ignored. This lacuna is troubling as consumers have much control over the data being collected. Missing or manipulated data could lead to inferior segmentation. The current work proposes a model of prior-free segmentation, inspired by models of facility location, and to the best of our knowledge provides the first segmentation mechanism that addresses incentive compatibility, efficient market segmentation and privacy in the absence of a common prior.