article

Dynamic Pricing with a Prior on Market Response

Authors:

Vivek F. Farias,

Benjamin Van RoyAuthors Info & Claims

Operations Research, Volume 58, Issue 1

Pages 16 - 29

https://doi.org/10.1287/opre.1090.0729

Published: 01 January 2010 Publication History

Abstract

We study a problem of dynamic pricing faced by a vendor with limited inventory, uncertain about demand, and aiming to maximize expected discounted revenue over an infinite time horizon. The vendor learns from purchase data, so his strategy must take into account the impact of price on both revenue and future observations. We focus on a model in which customers arrive according to a Poisson process of uncertain rate, each with an independent, identically distributed reservation price. Upon arrival, a customer purchases a unit of inventory if and only if his reservation price equals or exceeds the vendor's prevailing price.

We propose a simple heuristic approach to pricing in this context, which we refer to as decay balancing. Computational results demonstrate that decay balancing offers significant revenue gains over recently studied certainty equivalent and greedy heuristics. We also establish that changes in inventory and uncertainty in the arrival rate bear appropriate directional impacts on decay balancing prices in contrast to these alternatives, and we derive worst-case bounds on performance loss. We extend the three aforementioned heuristics to address a model involving multiple customer segments and stores, and provide experimental results demonstrating similar relative merits in this context.

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Cited By

Feng ZDawande MJanakiraman GQi A(2024)Technical Note—Dynamic Pricing and Learning with DiscountingOperations Research10.1287/opre.2023.247772:2(481-492)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/opre.2023.2477
Keskin NLi M(2024)Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition ProbabilitiesOperations Research10.1287/opre.2022.031672:3(885-902)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1287/opre.2022.0316
Drent CDrent MArts J(2024)Condition-Based Production for Stochastically Deteriorating SystemsManufacturing & Service Operations Management10.1287/msom.2022.047326:3(1137-1156)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1287/msom.2022.0473
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Dynamic Pricing with a Prior on Market Response
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cover image Operations Research

Operations Research Volume 58, Issue 1

January 2010

255 pages

ISSN:0030-364X

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INFORMS

Linthicum, MD, United States

Publication History

Published: 01 January 2010

Accepted: 01 March 2009

Received: 01 March 2007

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Cited By

Feng ZDawande MJanakiraman GQi A(2024)Technical Note—Dynamic Pricing and Learning with DiscountingOperations Research10.1287/opre.2023.247772:2(481-492)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/opre.2023.2477
Keskin NLi M(2024)Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition ProbabilitiesOperations Research10.1287/opre.2022.031672:3(885-902)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1287/opre.2022.0316
Drent CDrent MArts J(2024)Condition-Based Production for Stochastically Deteriorating SystemsManufacturing & Service Operations Management10.1287/msom.2022.047326:3(1137-1156)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1287/msom.2022.0473
Li XZheng Z(2024)Dynamic Pricing with External Information and Inventory ConstraintManagement Science10.1287/mnsc.2023.496370:9(5985-6001)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/mnsc.2023.4963
Xiao WZhang SHuang ZYu YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Multi-Scenario Pricing for Hotel Revenue ManagementProceedings of the ACM Web Conference 202410.1145/3589334.3645350(3986-3994)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645350
Chen XMiao SWang Y(2023)Differential Privacy in Personalized Pricing with Nonparametric Demand ModelsOperations Research10.1287/opre.2022.234771:2(581-602)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1287/opre.2022.2347
Chen XWang Y(2023)Robust Dynamic Pricing with Demand Learning in the Presence of Outlier CustomersOperations Research10.1287/opre.2022.228071:4(1362-1386)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1287/opre.2022.2280
Balseiro SGolrezaei NMahdian MMirrokni VSchneider J(2023)Contextual Bandits with Cross-LearningMathematics of Operations Research10.1287/moor.2022.131348:3(1607-1629)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1287/moor.2022.1313
Gur YMacnamara GMorgenstern ISaban D(2023)Information Disclosure and Promotion Policy Design for PlatformsManagement Science10.1287/mnsc.2023.467769:10(5883-5903)Online publication date: 2-Feb-2023
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Goyal VPerivier NKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Dynamic pricing and assortment under a contextual MNL demandProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600520(3461-3474)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600520
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