Selling through online marketplaces with consumer profiling

Xinjie Xing, Hongfu Huang*, Carl Philip T. Hedenstierna

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number114022
JournalJournal of Business Research
Volume164
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Competition
  • Consumer profiling services
  • Data blocking
  • Pricing
  • Retail platforms

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