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dc.contributor.advisorZapata Ramírez, Sebastian
dc.coverage.spatialColombiaspa
dc.creatorRodríguez Blanco, Oliver Andrés
dc.date.accessioned2021-02-12T19:20:15Z
dc.date.available2021-02-12T19:20:15Z
dc.date.created2020
dc.identifier.urihttp://hdl.handle.net/20.500.12010/17259
dc.description.abstract—La iniciativa del proyecto es presentar como el aplicar modelos de Aprendizaje Automatico para identificar predictivamente ´ las instalaciones fallidas de dispositivos Puntos de venta o Point of sales por sus siglas en ingles en comercios nuevos, los cuales ´ se encontraron interesados en hacer parte de la red de Credibanco, pero por diferentes razones al momento de la visita de respectivo tecnico, no aceptaron dicha instalaci ´ on. ´ Este proyecto pretende aportar a uno de los principales objetivos estrategicos de CredibanCo, en lograr m ´ as participaci ´ on en el ´ mercado al instalar exitosamente una mayor cantidad de dispositivos POS a nivel nacional, adicionalmente se lograra disminuir el gasto ´ operativo al identificar los comercios que son potencialmente propensos a no aceptar la instalacion al momento de la visita, ya ´ que cada una de estas tiene un costo asociado, se instale o no el dispositivo. Se elaboraron modelos con el fin de prediccion efectiva de aquellos ´ comercios que no acepten la instalacion del dispositivo, para tal ´ fin se recurrio a diferentes fuentes de datos de la organizaci ´ on´ como radicaciones de solicitudes del area de servicio al cliente, ´ informacion transaccional, datos del tipo de hardware de los ´ dispositivos, datos demograficos de los comercios he informaci ´ on´ comercial. En el proceso se alcanzo a un x porcentaje de Precisi ´ on junto con ´ una Sensibilidad del x porcentaje, adicionalmente se generaron actividades de cara al proceso de visita para instalaciones mediante planes de retencion para as ´ ´ı garantizar que los comercios identificados por el modelo de ser posible instalacion fallida se ´ les ofrezca ofertas tentativas para garantizar dicha instalacion y ´ preferencia en los tiempos de agendamiento para la visita del tecnico ´ quien realizara la instalacion.spa
dc.format.extent14 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad de Bogotá Jorge Tadeo Lozanospa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.subjectAlgoritmospa
dc.subjectComerciospa
dc.titleIdentificación predictiva para instalaciones fallidas de datáfonos en comercios nuevosspa
dc.type.localTrabajo de grado de maestríaspa
dc.subject.lembDatáfonosspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAbierto (Texto Completo)spa
dc.subject.keywordMachine Learningspa
dc.identifier.repourlhttp://expeditio.utadeo.edu.cospa
dc.creator.degreeMagíster en Ingeniería y Analítica de Datosspa
dc.publisher.programMaestría en Ingeniería y Analítica de Datosspa
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dc.description.hashtag#Datafonosspa
dc.description.abstractenglishThe project initiative is to present how the application of Machine Learning models allowed to predictively identify failed installations of POS devices (Points of Sale or Point) in new businesses, which were interested in being part of the CredibanCo network. but for different reasons at the time of the visit of the respective technician, they did not accept said installation. This project objectives to contribute to one of the main strategic objectives of CredibanCo, to achieve a greater market share through the successful installation of a greater number of POS devices nationwide, additionally it will reduce operating expenses by identifying businesses potentially prone to not accept. installation at the time of the visit, since each of these has an associated cost, whether or not the device is installed. Models were developed in order to automatically find those patterns that would be complex for a human to identify them and allow an effective prediction of those businesses that do not accept the installation of the device, for this purpose different data sources of the organization were used, such as requests customer service, transactional information, device hardware type data, business demographics and commercial information. As a result of this project, an x percentage of precision was achieved together with a sensitivity of x percentage, additionally activities were generated for the process of visiting the facilities through retention plans in order to guarantee that the businesses identified by the model in what possible offers of failed tentative installation are offered to guarantee said installation and preference in scheduling times for the visit of the technician who will perform the installation.spa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa


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