Targeted Advertising Platforms: Data Sharing and Customer Poaching

TL;DR

This paper models targeted advertising platforms to incentivize merchants to share customer data despite poaching concerns, proposing a mechanism involving three interconnected markets to optimize revenue, engagement, and merchant surplus.

Contribution

It introduces a novel mechanism design for targeted advertising platforms that balances data sharing incentives with poaching concerns, applicable to broader market exchange scenarios.

Findings

Optimal mechanism involves three interconnected markets.

Sufficiently large platforms can implement the mechanism effectively.

Model applies to other market design settings like greenhouse gas credits.

Abstract

E-commerce platforms are rolling out ambitious targeted advertising initiatives that rely on merchants sharing customer data with each other via the platform. Yet current platform designs fail to address participating merchants' concerns about customer poaching. This paper proposes a model of designing targeted advertising platforms that incentivizes merchants to voluntarily share customer data despite poaching concerns. I characterize the optimal mechanism that maximizes a weighted sum of platform's revenues, customer engagement and merchants' surplus. In sufficiently large platforms, the optimal mechanism can be implemented through the design of three markets: $i)$ selling market, where merchants can sell all their data at a posted price $p$, $ii)$ exchange market, where merchants share all their data in exchange for high click-through rate (CTR) ads, and $iii)$ buying market, where high-value merchants buy high CTR ads at the full price. The model is broad in scope with applications in other market design settings like the greenhouse gas credit markets and reallocating public resources, and points toward new directions in combinatorial market exchange designs.