Selling through online marketplaces with consumer profiling

Retail platforms obtain consumers' individual preferences by gathering vast amounts of data and can deliver such information to online retailers to support their pricing activities; this is called consumer-profiling services (CPS). We develop a game-theoretic model to study how a retail platform should provide CPS in light of retailers' competition and consumers' data-blocking activities. We show that exclusively providing data to high-quality retailers results in a net benefit for the platform and retailers. Low-quality retailers benefit from refusing the CPS provided by the platform to avoid head-to-head competition. In addition, we find that consumers' data blocking can benefit both the platform and retailers when the data-blocking cost is moderate, which is counterintuitive. We also find that data blocking always hurts consumer surplus and social welfare. To test the robustness of the main model, three extensions are discussed: sequential pricing, asymmetric production costs, and positive service fees.

Automated summary

Retail platforms provide consumer-profiling services (CPS), which gather consumers' preferences to support pricing activities of online retailers. A game-theoretic model is developed to investigate how a platform should offer CPS considering retailers' competition and consumers' data blocking. The study reveals that exclusively sharing data with high-quality retailers benefits both the platform and retailers. Surprisingly, data blocking by consumers can benefit the platform and retailers when the cost is moderate. However, data blocking always negatively affects consumer surplus and social welfare. Three extensions are examined to test the robustness of the main model: sequential pricing, asymmetric production costs, and positive service fees.