Individualization in Online Markets: A Generalized Model of Price Discrimination through Learning

• Rasha Ahmed ^[1]
  1. [1] Trinity College

     Trinity College

     Town of Hartford, Estados Unidos

     
• Localización: Journal of Theoretical and Applied Electronic Commerce Research, ISSN-e 0718-1876, Vol. 18, Nº. 4, 2023, págs. 2077-2091
• Idioma: inglés
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• Resumen

  • This paper builds a theoretical framework to model individualization in online markets.

    In a market with consumers of varying levels of demand, a seller offers multiple product bundles and prices. Relative to brick-and-mortar stores, an online seller can use pricing algorithms that can observe a buyer’s online behavior and infer a buyer’s type. I build a generalized model of price discrimination with Bayesian learning where a seller offers different bundles of the product that are sized and priced contingent on the posterior probability that the consumer is of a given type. Bayesian learning allows the seller to individualize product menus over time as new information becomes available. I explain how this strategy differs from first- or second-degree price discrimination models and how Bayesian learning over time affects equilibrium values and welfare.