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Segmentación de clientes en empresa comercializadora de equipos biomédicos: estrategias de marketing basadas en clusterización

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dc.contributor.advisorGarcía García, Diego Armando
dc.contributor.author Ojeda Espitia, Diego Enrique
dc.contributor.authorVargas Alvis, Fabian Andrés
dc.contributor.authorNúñez Vargas, Lady Johanna
dc.contributor.authorRíos Sánchez, Víctor Abraham
dc.contributor.researchgroupCiencias básicas
dc.creator.id1033700486
dc.creator.id1024509752
dc.creator.id1014237313
dc.creator.id1002344169
dc.date.accessioned2025-06-03T13:50:04Z
dc.date.issued2025-05-17
dc.description.abstractEl este documento aborda la problemática de segmentación de clientes en una empresa de venta de equipos biomédicos en Colombia cuyas estrategias de marketing y perfilamiento de clientes carece de un respaldo analítico robusto que le permita generar planes personalizados para aumentar la venta y la rentabilidad del negocio. Lo anterior, sumado al incremento de la competencia en el sector, dado que para 2022 se presentó un incremento de 8% de las importaciones de este tipo de productos. Por lo cual, el objetivo será desarrollar un modelo de clusterización que soporte la toma de decisiones del negocio.spa
dc.description.abstractThis document addresses the problem of customer segmentation in a biomedical equipment sales company in Colombia, whose marketing and customer profiling strategies lack robust analytical support that would allow it to generate customized plans to increase sales and business profitability. The above, added to the increase in competition in the sector, given that for 2022 there was an 8% increase in imports of this type of product. Therefore, the objective will be to develop a clustering model that supports business decision-making.eng
dc.description.degreelevelEspecializaciónspa
dc.description.degreenameEspecialista en Machine Learningspa
dc.description.researchareaEstadística aplicada y ciencia de datos
dc.description.tableofcontentsCAPÍTULO 1 .................................................................................................................................. 6 1. PLANTEAMIENTO DEL PROBLEMA................................................................................ 6 1.1 Antecedentes del Problema ............................................................................................... 6 1.2 Descripción del problema. ................................................................................................. 7 2. PREGUNTA DE INVESTIGACIÓN ..................................................................................... 8 3. OBJETIVOS............................................................................................................................ 8 Objetivo general ...................................................................................................................... 8 Objetivos específicos ............................................................................................................... 8 4. CONVENIENCIA DE LA INVESTIGACIÓN ...................................................................... 9 5. JUSTIFICACIÓN.................................................................................................................... 9 6. MARCO TEORICO .............................................................................................................. 10 Modelos de Clusterización y Segmentación para el Comportamiento de Compra de los Clientes. ................................................................................................................................. 11 6.1. Método K-means ......................................................................................................... 11 6.2. Método K-Medoids ..................................................................................................... 12 6.3. Análisis ABC .............................................................................................................. 13 6.4. Customer Lifetime Value (CLV) ................................................................................ 14 6.5. Clustering Jerárquico .................................................................................................. 16 6.6. DBSCAN .................................................................................................................... 17 6.7. Modelo RFM ............................................................................................................... 18 CAPÍTULO 2 ................................................................................................................................ 19 DISCUSIÓN CRÍTICA DE LOS MODELOS Y JUSTIFICACIÓN DE LA ELECCIÓN DEL MODELO A IMPLEMENTAR. ............................................................................................... 19 7. MARCO INSTITUCIONAL ................................................................................................ 21 CAPÍTULO 3 ................................................................................................................................ 22 8. METODOLOGÍA ................................................................................................................. 22 Primer nivel ........................................................................................................................... 22 Enfoque de la investigación ............................................................................................... 22 Alcance de la investigación ............................................................................................... 23 Diseño de la investigación ................................................................................................. 23 Pseudocódigo ..................................................................................................................... 35 CAPÍTULO 4 ................................................................................................................................ 37 9. ANÁLISIS Y DISCUSIÓN DE RESULTADOS ................................................................. 37 Desarrollo de los Modelos de Segmentación ........................................................................ 37 Filtrado y Análisis Exploratorio de Datos ............................................................................. 37 Modelo RFM – Enfoque Cuantiles ........................................................................................ 44 Modelo RFM – Enfoque Clústeres ........................................................................................ 48 Normalización de datos ......................................................................................................... 49 Número óptimo de clústeres .................................................................................................. 50 Desarrollo del Algoritmo K-means ....................................................................................... 51 Análisis de Resultados ........................................................................................................... 54 Elección de Enfoque .............................................................................................................. 56 Validación y Adaptabilidad del Modelo ................................................................................ 57 Estrategias de Marketing Recomendadas .............................................................................. 58 CAPITULO 5 ................................................................................................................................ 59 10. CONCLUSIONES .............................................................................................................. 59 REFERENCIAS ............................................................................................................................ 61spa
dc.formatpdf
dc.format.extent66 páginas
dc.format.mediumRecurso electrónicospa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad Eanspa
dc.identifier.reponamereponame:Repositorio Institucional Biblioteca Digital Minervaspa
dc.identifier.repourlrepourl:https://repository.universidadean.edu.co/
dc.identifier.urihttps://hdl.handle.net/10882/14813
dc.language.isospa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.programEspecialización en Machine Learningspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.lembEvaluación de proyectosspa
dc.subject.lembAnálisis de mercadeospa
dc.subject.lembAgrupación de productosspa
dc.subject.lembComportamiento del consumidorspa
dc.subject.lembPruebas de mercadospa
dc.subject.proposalAgrupamientospa
dc.subject.proposalSegmentación de clientesspa
dc.subject.proposalEquipos biomédicosspa
dc.subject.proposalMercadeospa
dc.subject.proposalClusteringeng
dc.subject.proposalCustomer segmentationeng
dc.subject.proposalBiomedical equipmenteng
dc.subject.proposalMarketingeng
dc.titleSegmentación de clientes en empresa comercializadora de equipos biomédicos: estrategias de marketing basadas en clusterizaciónspa
dc.titleCustomer segmentation in biomedical equipment trading company: marketing strategies based on clusteringeng
dc.typeTrabajo de grado - Especializaciónspa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.otherTrabajo de grado - Especialización
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
person.affiliation.nameEspecialización en Machine Learning
person.affiliation.nameEspecialización en Machine Learning
person.affiliation.nameEspecialización en Machine Learning
person.affiliation.nameEspecialización en Machine Learning

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