🤖 AI Summary
This paper addresses the data-sharing reluctance of merchants in targeted advertising due to concerns over “customer hijacking.” To resolve this, we propose a tri-market coordination mechanism comprising a sales market (merchants sell data), an exchange market (peer-to-peer data swapping), and a purchase market (platform acquires data). Grounded in mechanism design theory, our framework models platform revenue, user engagement, and merchant welfare as a weighted optimization objective, integrating click-through-rate–driven dynamic pricing and formal data property rights assignment to mitigate trust barriers in data sharing. Unlike single-market designs, our approach achieves, for the first time under competitive settings, Pareto improvements in both data incentive compatibility and advertising allocation efficiency. Empirical evaluation on a large-scale platform demonstrates significant gains: +32% merchant participation rate and +19% advertising conversion rate. The work provides a scalable theoretical framework and practical paradigm for multi-sided data market design.
📝 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.