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dc.contributor.advisorGarcía Cardona, Alejandra
dc.contributor.authorMira Orozco, Lina María
dc.coverage.spatialLat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.coverage.spatialLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.date.accessioned2021-06-01T15:42:33Z
dc.date.available2021-06-01T15:42:33Z
dc.date.created2020-12-15
dc.identifier.otherT 0070 2020
dc.identifier.urihttp://hdl.handle.net/11407/6378
dc.descriptionEste trabajo del MBA presenta un diseño de la estrategia de gestión de cobranza a través de Big Data Analytics, en empresas de Venta por Catálogo. La metodología empleada para lograr el objetivo del trabajo consiste en una revisión sistemática de literatura y un análisis cualitativo, con el fin de identificar, describir, profundizar y finalmente divulgar la estrategia. Es así como el método de investigación se empleó de la siguiente forma: (1) Planear el protocolo de revisión; (2) Identificación y clasificación de literatura orientada al objeto de estudio; (3) Descripción de literatura de la evolución del objeto de estudio, y (4) finalmente, la entrega de resultados. Como resultado se diseña la estrategia de gestión de cobranza a través de Big Data Analytics en empresas de Venta por Catálogo, en tres categorías enmarcadas en el ciclo de vigencia del crédito: (1) Otorgamiento del crédito, (2) Seguimiento al Comportamiento del uso del crédito y (3) Recuperación del crédito. Asimismo, el trabajo es un buen ejemplo de cómo emplear estas estrategias en empresas orientadas al desarrollo del canal comercial, lo cual asegura crecimiento, pero al mismo tiempo protege la estructura financiera de la empresa, pues permite segmentar los perfiles de los clientes, y genera estrategias customizadas de acuerdo con el riesgo, de tal forma que minimice la probabilidad de pérdida de la empresa. (la probabilidad de pérdidas que podría tener la empresa).
dc.description.abstractThe present study of MBA seeks to design a collection management strategy using Big Data Analytics implemented in Direct Selling Companies. The methodology used to achieve the main objective is based on a systematic literature review and a qualitative analysis, in order to identify, describe, deepen and communicate the final strategy. The research method was applied in four core activities: (1) Planning the review protocol ; (2) Identification and classification of literature approach to the object of study; (3) Literature description of the evolution of the study and finally (4) Results. As a result, the strategy design model applied in Collections management through Big Data Analytics in Direct Selling companies, the model design was divided into three categories focused on the credit cycle (1) Credit Granting, (2) Credit Use Behavior and (3) Credit Recovery. Likewise, this study is a good example of how to use these strategies in companies oriented to the development of the commercial channel, which ensure growth, but at the same time protect the financial structure of the company, by segmenting customer profiles, generating personalized strategies according to the risk, thus minimizing the possibility of loss of the company.
dc.format.extentp. 1-47
dc.format.mediumElectrónico
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad de Medellínspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0
dc.subjectGestión de cobranza
dc.subjectRiesgo
dc.subjectProbabilidad de incumplimiento
dc.subjectProbabilidad de pago
dc.subjectHerramientas de analítica
dc.subjectBig data analytics
dc.subjectMachine learning
dc.subjectVenta por catálogo
dc.titleDiseño de una estrategia para la gestión de cobranza, a través de Big Data Analytics en empresas de venta por catálogo
dc.rights.accessrightsinfo:eurepo/semantics/openAccess
dc.publisher.programMaestría en Administración - MBA
dc.subject.lembAdministración de créditos
dc.subject.lembAdministración de ventas
dc.subject.lembCobro de cuentas
dc.subject.lembCuentas por cobrar
dc.subject.lembMinería de datos
dc.subject.lembRiesgo (Finanzas)
dc.subject.keywordCollection management
dc.subject.keywordRisk
dc.subject.keywordProbability of default
dc.subject.keywordProbability of payment
dc.subject.keywordAnalytical tools
dc.subject.keywordBig data analysis
dc.subject.keywordMachine learning
dc.subject.keywordDirect selling
dc.relation.citationstartpage1
dc.relation.citationendpage47
dc.audienceComunidad Universidad de Medellín
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativas
dc.publisher.placeMedellín
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
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dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International
dc.type.localTesis de Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.description.degreenameMagíster en Administración MBA
dc.description.degreelevelMaestría
dc.publisher.grantorUniversidad de Medellín


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