Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación

dc.contributorBranch Bedoya, John Willian
dc.creatorMosquera González, Davinson
dc.date.accessioned2022-06-24T20:12:14Z
dc.date.accessioned2022-09-21T18:19:03Z
dc.date.available2022-06-24T20:12:14Z
dc.date.available2022-09-21T18:19:03Z
dc.date.created2022-06-24T20:12:14Z
dc.date.issued2022-06
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81631
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3407459
dc.description.abstractLa presente tesis de investigación, tiene como objetivo proponer un método para la segmentación de clientes, incorporando la predicción del valor monetario del cliente como una variable de segmentación, para tal fin, se propone una metodología cuantitativa, en la que los datos a utilizar corresponden a las transacciones de una tienda en línea de regalos para toda ocasión de Reino Unido, denominada “Online Retail II”, que consta de un total de 5.833 clientes y 1.067.371 registros; a partir de los cuales se realiza un proceso de caracterización de los datos, seguido de la predicción del valor monetario de cada cliente utilizando técnicas estadísticas y de aprendizaje de máquinas, que posteriormente se incluye como variable en el proceso de segmentación. Finalmente, se hace un comparativo entre los resultados de segmentar clientes sin incorporar la predicción del valor monetario y la segmentación de clientes incorporando la predicción del valor monetario; con lo que se concluye que el método propuesto, utilizando el algoritmo de Vecinos más cercanos para la predicción del valor monetario del cliente, al incorporarlo en la segmentación de clientes, logra un desempeño económico entre 10% y 20% mejor que segmentar sin incorporar esta variable. (Texto tomado de la fuente)
dc.description.abstractThis research thesis aims to propose a method for customer segmentation, incorporating the prediction of the customer's monetary value as a segmentation variable, for this purpose, a quantitative methodology is proposed, in which the data to be used correspond to the transactions of an online all-occasion gifts store in the United Kingdom, called “Online Retail II”, consisting of a total of 5.833 customers and 1.067.371 registrations; from which a data characterization process is carried out, followed by the prediction of the monetary value of each client using statistical and machine learning techniques, which is later included as a variable in the segmentation process. Finally, a comparison is made between the results of segmenting customers without incorporating the prediction of the monetary value and the customer segmentation incorporating the prediction of the monetary value; with which it is concluded that the proposed method, using the Nearest Neighbors algorithm for the prediction of the monetary value of the client, when incorporating it into the client segmentation, achieves an economic performance between 10% and 20% better than segmenting without incorporating this variable.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Maestría en Ingeniería - Analítica
dc.publisherDepartamento de la Computación y la Decisión
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleMétodo para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
dc.typeTesis


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