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Using survival prediction techniques to learn consumer-specific reservation price distributions

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Jin, Ping, Haider, Humza, Greiner, Russell, Wei, Sarah and Häubl, Gerald (2021) Using survival prediction techniques to learn consumer-specific reservation price distributions. PLOS ONE, 16 (4). e0249182. doi:10.1371/journal.pone.0249182

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Official URL: https://doi.org/10.1371/journal.pone.0249182

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Abstract

A consumer’s “reservation price” (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, including personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able to predict these values, based on each consumer’s specific information, using a model learned from earlier consumer transactions. Here, we view each such (non)transaction as a censored observation, which motivates us to use techniques from survival analysis/prediction, to produce models that can generate a consumer-specific RP distribution, based on features of each new consumer. To validate this framework of RP, we run experiments on realistic data, with four survival prediction methods. These models performed very well (under three different criteria) on the task of estimating consumer-specific RP distributions, which shows that our RP framework can be effective.

Item Type: Journal Article
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HF Commerce
H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Warwick Business School > Marketing Group
Faculty of Social Sciences > Warwick Business School
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Consumers' preferences, Consumers' preferences -- Mathematical models, Consumers' preferences -- Prices, Consumer behavior, Willingness to pay -- Mathematical models, Prices
Journal or Publication Title: PLOS ONE
Publisher: Public Library of Science
ISSN: 1932-6203
Official Date: 29 April 2021
Dates:
DateEvent
29 April 2021Published
12 March 2021Accepted
Volume: 16
Number: 4
Article Number: e0249182
DOI: 10.1371/journal.pone.0249182
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDUniversity of Albertahttp://dx.doi.org/10.13039/501100000190
RGPIN-2019-04927[NSERC] Natural Sciences and Engineering Research Council of Canadahttp://dx.doi.org/10.13039/501100000038
UNSPECIFIEDAlberta Machine Intelligence Institutehttp://dx.doi.org/10.13039/100013373
Insight Grant Program Social Sciences and Humanities Research Council of Canadahttp://dx.doi.org/10.13039/501100000155
Contributors:
ContributionNameContributor ID
UNSPECIFIEDHutson, Alan D.UNSPECIFIED

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