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Modelo predictivo de churn involuntario para una empresa de telecomunicaciones en Colombia
| dc.contributor.advisor | Cruz Castro, Daniel Leonardo | |
| dc.creator | Reina Rodríguez, David Alejandro | |
| dc.creator | Reina Rodríguez, Gustavo Adolfo | |
| dc.creator | Becerra Salcedo, Fabian Andrés | |
| dc.creator.degree | Magíster en Business Analytics | |
| dc.date.accessioned | 2026-07-17T13:06:33Z | |
| dc.date.available | 2026-07-17T13:06:33Z | |
| dc.date.created | 2026-07-14 | |
| dc.description | Este trabajo desarrolla un modelo predictivo de churn involuntario para una empresa de telecomunicaciones en Colombia, utilizando más de 5 millones de registros históricos y variables relacionadas con facturación, mora, consumo y comportamiento del cliente. Tras comparar varios algoritmos, XGBoost fue el modelo seleccionado por su mejor desempeño (AUC = 0,939), permitiendo identificar de forma anticipada a los clientes con mayor riesgo de abandono por falta de pago y facilitando la priorización de estrategias de retención y gestión de cartera. | |
| dc.description.abstract | This thesis develops a predictive model for involuntary churn in a Colombian telecommunications company using more than 5 million historical records and variables related to billing, payment delinquency, network usage, and customer behavior. After evaluating several machine learning algorithms, XGBoost was selected as the best-performing model (AUC = 0.939), enabling the early identification of customers at high risk of involuntary churn and supporting more effective retention and debt management strategies. | |
| dc.format.extent | 110 pp | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.48713/10336_48049 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/48049 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad del Rosario | |
| dc.publisher.department | Escuela de Administración | |
| dc.publisher.department | Escuela de Ingeniería, Ciencia y Tecnología | |
| dc.publisher.program | Maestría en Business Analytics | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.accesRights | info:eu-repo/semantics/closedAccess | |
| dc.rights.acceso | Bloqueado (Texto referencial) | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source.bibliographicCitation | Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2024). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 11(1), 1–24. https://doi.org/10.1186/s40537-024-00925-6 | |
| dc.source.bibliographicCitation | Asociación Nacional de Empresarios de Colombia. (2024). Informe sectorial de telecomunicaciones en Colombia 2024. ANDI. https://www.andi.com.co | |
| dc.source.bibliographicCitation | Bayram, F., Ahmed, B. S., & Kassler, A. (2022). From concept drift to model degradation: An overview on performance-aware drift detectors. Knowledge-Based Systems, 245, 108632. https://doi.org/10.1016/j.knosys.2022.108632 | |
| dc.source.bibliographicCitation | Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 | |
| dc.source.bibliographicCitation | Comisión de Regulación de Comunicaciones. (2025a). Reporte de industria de los servicios postales y de telecomunicaciones – Tercer trimestre de 2025. CRC. https://www.crcom.gov.co | |
| dc.source.bibliographicCitation | Comisión de Regulación de Comunicaciones. (2025b). Data Flash 2025-011: Portabilidad numérica móvil. CRC. https://www.postdata.gov.co/sites/default/files/dataflash/Data_Flash_2025_011_PNM_V2.pdf | |
| dc.source.bibliographicCitation | Deloitte. (2023). Global telecommunications outlook 2023: Connecting the dots between strategy and execution. Deloitte Insights. https://www2.deloitte.com | |
| dc.source.bibliographicCitation | Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2021). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5 | |
| dc.source.bibliographicCitation | GSMA Intelligence. (2024). The mobile economy Latin America 2024. GSMA. https://www.gsma.com/mobileeconomy | |
| dc.source.bibliographicCitation | Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: A machine learning approach. Computing, 104(2), 271–294. https://doi.org/10.1007/s00607-021-00908-y | |
| dc.source.bibliographicCitation | Langer, B. (2025). Understanding data & analytics maturity: A systematic review of maturity model composition. Schmalenbach Journal of Business Research, 77(2), 205–227. https://doi.org/10.1007/s41471-024-00205-2 | |
| dc.source.bibliographicCitation | Makokha, A., Obote, K., Muchiri, H., & Senagi, K. (2024). Predicting customer churn in the telecommunications industry using machine learning techniques. American Journal of Networks and Communications, 13(1), 10–26. https://doi.org/10.11648/j.ajnc.20241301.12 | |
| dc.source.bibliographicCitation | Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607 | |
| dc.source.bibliographicCitation | Ministerio de Tecnologías de la Información y las Comunicaciones. (2023). Boletín trimestral de las TIC: cifras del sector de tecnologías de la información y las comunicaciones. MinTIC. https://www.mintic.gov.co | |
| dc.source.bibliographicCitation | Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1–9. https://doi.org/10.1016/j.simpat.2015.03.003 | |
| dc.source.bibliographicCitation | Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, 100342. https://doi.org/10.1016/j.rico.2023.100342 | |
| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject | Cartera | |
| dc.subject | Facturación | |
| dc.subject | Retención | |
| dc.subject.keyword | Churn | |
| dc.subject.keyword | XGBoost | |
| dc.subject.keyword | Billing | |
| dc.subject.keyword | Involuntary churn | |
| dc.title | Modelo predictivo de churn involuntario para una empresa de telecomunicaciones en Colombia | |
| dc.title.TranslatedTitle | Predictive Model of Involuntary Customer Churn for a Telecommunications Company in Colombia | |
| dc.type | masterThesis | |
| dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | |
| dc.type.spa | Tesis de maestría | |
| local.department.report | Escuela de Administración | |
| local.department.report | Escuela de Ciencias e Ingeniería | |
| local.regiones | Bogotá |
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