Dynamic pricing strategies towards strategic consumers under demand learning

Meixian Tang, Junfeng Tian, Jinxia Kong, Zhenzhen Ren and Jinsong Tian

PLOS ONE, 2026, vol. 21, issue 1, 1-25

Abstract: This study considers a two-stage dynamic pricing framework with demand learning, where a seller sells a finite number of products to strategic consumers in an uncertain market. Based on Bayesian updating, we reveal the applicable conditions of demand learning for dynamic pricing with and without price guarantee. If a price guarantee is not offered in dynamic pricing, demand learning yields marginal benefits only when the initial estimate of prior market size is accurate or overestimated, and prior market size uncertainty is low. Under dynamic pricing with price guarantee, demand learning is effective when the initial estimate is inaccurate, particularly for a prior market size with high uncertainty. However, if the initial estimate is relatively certain, demand learning causes revenue loss by offering price guarantees. Moreover, we also explore the trade-off between dynamic pricing strategies when demand learning is adopted and propose managerial insights. For a product just entering the market and an overestimated market size, dynamic pricing with price guarantee and demand learning dominates. When the product is in the introduction period with an underestimated market size or the product is in the mature period, dynamic pricing without price guarantee and with demand learning is better for weak strategic consumers or under low inventory. Otherwise, dynamic pricing with price guarantee and demand learning is favorable in most cases.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0340105

DOI: 10.1371/journal.pone.0340105

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