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Bayesian strategies for dynamic pricing in e‐commerce

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  • Eric Cope

Abstract

E‐commerce platforms afford retailers unprecedented visibility into customer purchase behavior and provide an environment in which prices can be updated quickly and cheaply in response to changing market conditions. This study investigates dynamic pricing strategies for maximizing revenue in an Internet retail channel by actively learning customers' demand response to price. A general methodology is proposed for dynamically pricing information goods, as well as other nonperishable products for which inventory levels are not an essential consideration in pricing. A Bayesian model of demand uncertainty involving the Dirichlet distribution or a mixture of such distributions as a prior captures a wide range of beliefs about customer demand. We provide both analytic formulas and efficient approximation methods for updating these prior distributions after sales data have been observed. We then investigate several strategies for sequential pricing based on index functions that consider both the potential revenue and the information value of selecting prices. These strategies require a manageable amount of computation, are robust to many types of prior misspecification, and yield high revenues compared to static pricing and passive learning approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Suggested Citation

  • Eric Cope, 2007. "Bayesian strategies for dynamic pricing in e‐commerce," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(3), pages 265-281, April.
    • Handle: RePEc:wly:navres:v:54:y:2007:i:3:p:265-281
    DOI: 10.1002/nav.20204
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    Cited by:

    1. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    3. Yuqing Zhang & Neil Walton, 2019. "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches," Papers 1907.05381, arXiv.org.
    4. Katy S. Azoury & Julia Miyaoka, 2014. "Sequential learning versus no learning in Bayesian regression models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(7), pages 532-548, October.
    5. Gu, Wei & Luo, Jing & Yu, Xiaoru & Zhang, Wenqing & Li, Baixun, 2023. "Dynamic decisions between sellers and consumers in online second-hand trading platforms: Evidence from C2C transactions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    6. John Aloysius & Cary Deck & Amy Farmer, 2012. "A Comparison of Bundling and Sequential Pricing in Competitive Markets: Experimental Evidence," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 19(1), pages 25-51, February.
    7. Arnoud V. den Boer, 2014. "Dynamic Pricing with Multiple Products and Partially Specified Demand Distribution," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 863-888, August.
    8. Arnoud V. den Boer & Bert Zwart, 2014. "Simultaneously Learning and Optimizing Using Controlled Variance Pricing," Management Science, INFORMS, vol. 60(3), pages 770-783, March.

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