Personalization, Consumer Search, and Algorithmic Pricing

Liying Qiu (), Yan Huang (), Param Vir Singh () and Kannan Srinivasan ()
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Liying Qiu: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Yan Huang: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Param Vir Singh: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Kannan Srinivasan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Marketing Science, 2025, vol. 44, issue 6, 1278-1298

Abstract: Our study investigates the impact of product ranking systems on artificial intelligence (AI)-powered pricing algorithms. Specifically, we examine the effects of “personalized” and “unpersonalized” ranking systems on algorithmic pricing outcomes and consumer welfare. Our analysis reveals that personalized ranking systems, which rank products in decreasing order of consumer’s utilities, may encourage higher prices charged by pricing algorithms, especially when consumers search for products sequentially on a third-party platform. This is because personalized ranking significantly reduces the ranking-mediated price elasticity of demand and thus incentives to lower prices. Conversely, unpersonalized ranking systems lead to significantly lower prices and greater consumer welfare. These findings suggest that even in the absence of price discrimination, personalization may not necessarily benefit consumers because pricing algorithms can undermine consumer welfare through higher prices. Thus, our study highlights the crucial role of ranking systems in shaping algorithmic pricing behaviors and consumer welfare.

Keywords: algorithmic pricing; digital platforms; marketing; personalization; ranking systems; reinforcement learning; sequential search (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:6:p:1278-1298

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