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.

中文翻译：

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在线市场的个性化：通过学习进行价格歧视的广义模型

本文构建了一个理论框架来模拟在线市场的个性化。在消费者需求水平不同的市场中，卖家提供多种产品组合和价格。相对于实体店，在线卖家可以使用定价算法来观察买家的在线行为并推断买家的类型。我通过贝叶斯学习构建了一个通用的价格歧视模型，其中卖家提供不同的产品捆绑，这些产品的大小和价格取决于消费者属于给定类型的后验概率。随着新信息的出现，贝叶斯学习允许卖家个性化产品菜单。我解释了这种策略与一级或二级价格歧视模型的不同之处，以及贝叶斯学习随着时间的推移如何影响均衡价值和福利。