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Ensemble learning from model based trees with application to differential price sensitivity assessment

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dc.contributor.authorMartín Arevalillo, Jorge
dc.date.accessioned2025-03-19T10:46:49Z
dc.date.available2025-03-19T10:46:49Z
dc.date.issued2021-05
dc.descriptionThe registered version of this article, first published in “Information Sciences, vol. 557, 2021", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.ins.2020.12.039 La versión registrada de este artículo, publicado por primera vez en “Information Sciences, vol. 557, 2021", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.ins.2020.12.039
dc.description.abstractThe assessment of price sensitivity is a relevant issue with important implications in decision making for revenue management. The issue has attracted interest among companies evolving towards the data-driven culture through the exploitation of their data sources. Thus, the design of pricing strategies that rely on analytics to identify groups of customers that exhibit differential price sensitivity has a great potential for revenue managers. This work proposes a data-driven approach, using ensemble learning from model based trees, to assess differential price sensitivity in a similar way as random forests algorithm does to assess variable importance. A differential price sensitivity score is defined and a ranking is obtained as a result so that the top ranked variables can be selected as candidate inputs for segmentation and differential price sensitivity group finding. Then optimal price allocation is carried out on the derived groups in order to compute the expected revenues which are compared with the revenues given by un-optimized prices and by optimal price allocation derived from the logit estimation of the bid response function. The proposed approach is validated in synthetic experiments and by application to the real business case of an auto lending company; the resulting revenues show its benefit.en
dc.description.versionversión original
dc.identifier.citationJorge M. Arevalillo, Ensemble learning from model based trees with application to differential price sensitivity assessment, Information Sciences, Volume 557, 2021, Pages 16-33, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2020.12.039
dc.identifier.doihttps://doi.org/10.1016/j.ins.2020.12.039
dc.identifier.issn0020-0255
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26322
dc.journal.titleInformation Sciences
dc.journal.volume557
dc.language.isoen
dc.page.final33
dc.page.initial16
dc.publisherELSEVIER
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsensemble learningen
dc.subject.keywordsdifferential price sensitivityen
dc.subject.keywordsmodel based recursive partitioningen
dc.subject.keywordsrevenue managementen
dc.titleEnsemble learning from model based trees with application to differential price sensitivity assessmenten
dc.typeartículoes
dc.typejournal articleen
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relation.isAuthorOfPublication.latestForDiscoveryea1c092a-eceb-49c8-abb7-d4d52f53930a
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