Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method

[EN] For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to bus...

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Detalles Bibliográficos
Autores: Barrera, Felipe, Segura Maroto, Marina, Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/200858
Acceso en línea:https://riunet.upv.es/handle/10251/200858
Access Level:acceso abierto
Palabra clave:Multiple criteria analysis
Supply chain management
Customer relationship management
RFM
GLNF sorting
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking MethodBarrera, FelipeSegura Maroto, MarinaMaroto Álvarez, Mª Concepción|||0000-0001-8512-3197Multiple criteria analysisSupply chain managementCustomer relationship managementRFMGLNF sortingPROMETHEEESTADISTICA E INVESTIGACION OPERATIVA[EN] For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.ElsevierFacultad de Administración y Dirección de EmpresasDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadCentro de Gestión de la Calidad y del CambioRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-03-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/200858reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2008582026-06-13T07:49:27Z
dc.title.none.fl_str_mv Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
title Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
spellingShingle Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
Barrera, Felipe
Multiple criteria analysis
Supply chain management
Customer relationship management
RFM
GLNF sorting
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
title_short Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
title_full Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
title_fullStr Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
title_full_unstemmed Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
title_sort Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method
dc.creator.none.fl_str_mv Barrera, Felipe
Segura Maroto, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author Barrera, Felipe
author_facet Barrera, Felipe
Segura Maroto, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author_role author
author2 Segura Maroto, Marina
Maroto Álvarez, Mª Concepción|||0000-0001-8512-3197
author2_role author
author
dc.contributor.none.fl_str_mv Facultad de Administración y Dirección de Empresas
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Centro de Gestión de la Calidad y del Cambio
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Multiple criteria analysis
Supply chain management
Customer relationship management
RFM
GLNF sorting
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
topic Multiple criteria analysis
Supply chain management
Customer relationship management
RFM
GLNF sorting
PROMETHEE
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-03-15
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/200858
url https://riunet.upv.es/handle/10251/200858
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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