Behavior-Based Algorithmic Pricing

Antoine Dubus ()
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Antoine Dubus: ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]

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Abstract: This article studies the impact of algorithmic pricing on market competition when firms collect data to charge personalized prices to their past customers. Pricing algorithms offer to each firm a rich set of pricing strategies combining first and third-degree price discrimination: they can choose for each of their past customers whether to charge them personalized or homogeneous prices. The optimal targeting strategy of each firm consists in charging personalized prices to past customers with the highest willingness to pay and a homogeneous price to the remaining consumers, including past customers with a low valuation on whom a firm has information. This targeting strategy maximizes rent extraction while softening competition between firms compared to classical models where firms target all past customers. In turn, price-undercutting and poaching practices are not sustainable with behavior-based algorithmic pricing, resulting in greater industry profits.

Keywords: Algorithmic Pricing; Data Collection; Behavior-based price discrimination (search for similar items in EconPapers)
Date: 2023-09-25
New Economics Papers: this item is included in nep-com, nep-ind, nep-mic and nep-reg
Note: View the original document on HAL open archive server: https://hal.science/hal-03269586v4
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