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

ilustraciones, diagramas, tablas

Autores:: Mosquera González, Davinson

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_96a70fbf4f7435ec775404bf1d52bc61
oai_identifier_str	oai:repositorio.unal.edu.co:unal/81631
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
dc.title.translated.eng.fl_str_mv	Method for customer segmentation incorporating the prediction of the customer's monetary value as a segmentation variable
title	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
spellingShingle	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación 000 - Ciencias de la computación, información y obras generales::001 - Conocimiento 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 650 - Gerencia y servicios auxiliares::659 - Publicidad y relaciones públicas Internet marketing Comercio electrónico Mercadeo por internet Electronic commerce Segmentación de clientes Valor monetario Aprendizaje de máquinas Modelos paramétricos Customer segmentation Customer lifetime value (CLV) Machine learning Parametric models
title_short	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
title_full	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
title_fullStr	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
title_full_unstemmed	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
title_sort	Método para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentación
dc.creator.fl_str_mv	Mosquera González, Davinson
dc.contributor.advisor.none.fl_str_mv	Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv	Mosquera González, Davinson
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::001 - Conocimiento 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 650 - Gerencia y servicios auxiliares::659 - Publicidad y relaciones públicas
topic	000 - Ciencias de la computación, información y obras generales::001 - Conocimiento 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 650 - Gerencia y servicios auxiliares::659 - Publicidad y relaciones públicas Internet marketing Comercio electrónico Mercadeo por internet Electronic commerce Segmentación de clientes Valor monetario Aprendizaje de máquinas Modelos paramétricos Customer segmentation Customer lifetime value (CLV) Machine learning Parametric models
dc.subject.armarc.none.fl_str_mv	Internet marketing
dc.subject.lemb.none.fl_str_mv	Comercio electrónico Mercadeo por internet Electronic commerce
dc.subject.proposal.spa.fl_str_mv	Segmentación de clientes Valor monetario Aprendizaje de máquinas Modelos paramétricos
dc.subject.proposal.eng.fl_str_mv	Customer segmentation Customer lifetime value (CLV) Machine learning Parametric models
description	ilustraciones, diagramas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-24T20:12:14Z
dc.date.available.none.fl_str_mv	2022-06-24T20:12:14Z
dc.date.issued.none.fl_str_mv	2022-06
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81631
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81631 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Alipour, S. P. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes, 45(7), 1129–1157. https://doi.org/10.1108/K-07-2015-0180 Ardanuy, J. (2012). Breve introducción a la bibliometría. In Universitat de Barcelona (pp. 1–25). https://doi.org/10.1038/nmat3485 Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007 Bauer, J., & Jannach, D. (2021). Improved Customer Lifetime Value Prediction with Sequence-To-Sequence Learning and Feature-Based Models. ACM Transactions on Knowledge Discovery from Data, 1(1). https://doi.org/10.1145/3441444 Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. SIGMOD Rec., 29(2), 93–104. https://doi.org/10.1145/335191.335388 Cadavid, L., Awad, G., & Franco, C. (2012). Análisis bibliométrico del campo modelado de difusión de innovaciones. Estudios Gerenciales, 28(EE), 213–236. http://www.scielo.org.co/pdf/eg/v28nspe/v28nspea12.pdf Camps, D. (2008). Limitaciones de los indicadores bibliométricos en la evaluación de la actividad científica biomédica. In Colombia Médica (Vol. 39, pp. 74–79). scieloco. Cancer Research UK. (2022). Cancer Research UK Together we will beat cancer. https://www.cancerresearchuk.org/ Channa, H. S. (2019). Customer lifetime value: An ensemble model approach. Advances in Intelligent Systems and Computing, 808, 353–363. https://doi.org/10.1007/978-981-13-1402-5_27 Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013 Chen, D. (2019). Online Retail II Data Set. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Online+Retail+II Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17 Chen, P. P., Guitart, A., Del Río, A. F., & Periáñez, A. (2019). Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2134–2140. https://doi.org/10.1109/BigData.2018.8622151 Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2018). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences, 1–7. https://doi.org/10.1016/j.jksuci.2018.09.004 Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. TQM Journal, 32(4), 869–896. https://doi.org/10.1108/TQM-10-2019-0243 Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284. https://doi.org/10.1287/mksc.1040.0098 Forliano, C., De Bernardi, P., & Yahiaoui, D. (2021). Entrepreneurial universities: A bibliometric analysis within the business and management domains. Technological Forecasting and Social Change, 165, 1–16. https://doi.org/10.1016/j.techfore.2020.120522 Guadarrama, T., & Rosales, E. M. (2015). Marketing relacional: valor, satisfacción, lealtad y retención del cliente. Análisis y reflexión teórica. Ciencia y Sociedad, 40(2), 307–340. Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48(5), 1275–1287. https://doi.org/10.1007/s10489-017-1105-y Hall, C. M. (2011). Publish and perish? Bibliometric analysis, journal ranking and the assessment of research quality in tourism. Tourism Management, 32(1), 16–27. https://doi.org/10.1016/J.TOURMAN.2010.07.001 Heldt, R., Silveira, C. S., & Luce, F. B. (2021). Predicting customer value per product: From RFM to RFM/P. Journal of Business Research, 127(March 2019), 444–453. https://doi.org/10.1016/j.jbusres.2019.05.001 Herrera-Franco, G., Montalván-Burbano, N., Carrión-Mero, P., Apolo-Masache, B., & Jaya-Montalvo, M. (2020). Research trends in geotourism: A bibliometric analysis using the scopus database. Geosciences (Switzerland), 10(10), 1–29. https://doi.org/10.3390/geosciences10100379 Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 1–5. https://doi.org/10.1073/pnas.0507655102 Hurwitz, J., & Kirsch, D. (2018). Machine Learning for dummies (IBM Limite). Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2018). Modeling and application of customer lifetime value in online retail. Informatics, 5(1), 1–22. https://doi.org/10.3390/informatics5010002 Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2019). Comparative analysis of selected probabilistic customer lifetime value models in online shopping. Journal of Business Economics and Management, 20(3), 398–423. https://doi.org/10.3846/jbem.2019.9597 Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018). Customer Segmentation using K-means Clustering. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 135–139. https://doi.org/10.1109/CTEMS.2018.8769171 Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678 Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. 2008 Eighth IEEE International Conference on Data Mining, 413–422. https://doi.org/10.1109/ICDM.2008.17 Nita, A. (2019). Empowering impact assessments knowledge and international research collaboration - A bibliometric analysis of Environmental Impact Assessment Review journal. Environmental Impact Assessment Review, 78(March), 106283. https://doi.org/10.1016/j.eiar.2019.106283 Oblander, E. S., Gupta, S., Mela, C. F., Winer, R. S., & Lehmann, D. R. (2020). The past, present, and future of customer management. Marketing Letters, 31(2–3), 125–136. https://doi.org/10.1007/s11002-020-09525-9 Pareto, V. (1896). Cours d’économie politique. Université de Lausanne. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. http://jmlr.org/papers/v12/pedregosa11a.html Peralta, M. J., Frías, M., & Gregorio, O. (2015). Criterios, clasificaciones y tendencias de los indicadores bibliométricos en la evaluación de la ciencia. Revista Cubana de Información En Ciencias de La Salud, 26(3), 290–309. http://scielo.sld.cu Platzer, M. (2021). Customer Base Analysis with BTYDplus (pp. 1–33). https://cran.r-project.org/web/packages/BTYDplus/vignettes/BTYDplus-HowTo.pdf Rathi, T., & Ravi, V. (2017). Customer Lifetime Value Measurement using Machine Learning Techniques. In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 3013–3022). https://doi.org/10.4018/978-1-5225-1759-7.ch124 Roy-García, I., Rivas-Ruiz, R., Pérez-Rodríguez, M., & Palacios-Cruz, L. (2019). Correlation: Not all correlation entails causality. Revista Alergia Mexico, 66(3), 354–360. https://doi.org/10.29262/ram.v66i3.651 Rueda, G., Gerdsri, P., & Kocaoglu, D. F. (2007). Bibliometrics and Social Network Analysis of the Nanotechnology Field. PICMET ’07 - 2007 Portland International Conference on Management of Engineering & Technology, 2905–2911. https://doi.org/10.1109/PICMET.2007.4349633 Sifa, R., Runge, J., Bauckhage, C., & Klapper, D. (2018). Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE. Hawaii International Conference on System Sciences, 923–932. Simeone, O. (2018). A Brief Introduction to Machine Learning for Engineers. Foundations and Trends® in Signal Processing, 12(3–4), 200–431. https://doi.org/10.1561/2000000102 Sodhi, P., Awasthi, N., & Sharma, V. (2019). Introduction to Machine Learning and Its Basic Application in Python. SSRN Electronic Journal, 1354–1375. https://doi.org/10.2139/ssrn.3323796 Srivastava, R. (2017). Identification of customer clusters using RFM model: a case of diverse purchaser classification. International Journal of Information, Business and Management, 9(4), 201–208. Tsai, T. Y., Lin, C. T., & Prasad, M. (2019). An Intelligent Customer Churn Prediction and Response Framework. Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 928–935. https://doi.org/10.1109/ISKE47853.2019.9170380 Valérie, D., & Pierre, A. G. (2010). Bibliometric idicators: Quality masurements of sientific publication. Radiology, 255(2), 342–351. https://doi.org/10.1148/radiol.09090626 Velasco, B., Eiros Bouza, J. M., Pinilla, J. M., & San Román, J. A. (2012). La utilización de los indicadores bibliométricos para evaluar la actividad investigadora. Aula Abierta, 40(2), 75–84. http://dialnet.unirioja.es/servlet/articulo?codigo=3920967&info=resumen&idioma=ENG Villa, E., Ruiz, L., Valencia, A., & Picón, E. (2018). Electronic commerce: factors involved in its adoption from a bibliometric analysis. Journal of Theoretical and Applied Electronic Commerce Research, 13(1), 39–70. https://doi.org/10.4067/S0718-18762018000100104 Win, T. T., & Bo, K. S. (2020). Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm. Proceedings of the 4th International Conference on Advanced Information Technologies, ICAIT 2020, 236–241. https://doi.org/10.1109/ICAIT51105.2020.9261792 Yoseph, F., & Heikkila, M. (2019). Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method. Proceedings - International Conference on Machine Learning and Data Engineering, ICMLDE 2018, 77–82. https://doi.org/10.1109/iCMLDE.2018.00029
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	94 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/81631/3/1152704626.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/81631/4/license.txt https://repositorio.unal.edu.co/bitstream/unal/81631/5/1152704626.2022.pdf.jpg
bitstream.checksum.fl_str_mv	8d94589be716724f5937d621af1c6e44 8153f7789df02f0a4c9e079953658ab2 13a40942b813c3c9ea1f75f4c48223d0
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814090002942918656
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Branch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15600Mosquera González, Davinsondd976c35e95c4ed41ee46d075dd6c60a6002022-06-24T20:12:14Z2022-06-24T20:12:14Z2022-06https://repositorio.unal.edu.co/handle/unal/81631Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasLa 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)This 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.MaestríaMagíster en Ingeniería - AnalíticaInvestigación CuantitativaÁrea Curricular de Ingeniería de Sistemas e Informática94 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::001 - Conocimiento000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores650 - Gerencia y servicios auxiliares::659 - Publicidad y relaciones públicasInternet marketingComercio electrónicoMercadeo por internetElectronic commerceSegmentación de clientesValor monetarioAprendizaje de máquinasModelos paramétricosCustomer segmentationCustomer lifetime value (CLV)Machine learningParametric modelsMétodo para la segmentación de clientes incorporando la predicción del valor monetario del cliente como una variable de segmentaciónMethod for customer segmentation incorporating the prediction of the customer's monetary value as a segmentation variableTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlipour, S. P. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes, 45(7), 1129–1157. https://doi.org/10.1108/K-07-2015-0180Ardanuy, J. (2012). Breve introducción a la bibliometría. In Universitat de Barcelona (pp. 1–25). https://doi.org/10.1038/nmat3485Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007Bauer, J., & Jannach, D. (2021). Improved Customer Lifetime Value Prediction with Sequence-To-Sequence Learning and Feature-Based Models. ACM Transactions on Knowledge Discovery from Data, 1(1). https://doi.org/10.1145/3441444Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. SIGMOD Rec., 29(2), 93–104. https://doi.org/10.1145/335191.335388Cadavid, L., Awad, G., & Franco, C. (2012). Análisis bibliométrico del campo modelado de difusión de innovaciones. Estudios Gerenciales, 28(EE), 213–236. http://www.scielo.org.co/pdf/eg/v28nspe/v28nspea12.pdfCamps, D. (2008). Limitaciones de los indicadores bibliométricos en la evaluación de la actividad científica biomédica. In Colombia Médica (Vol. 39, pp. 74–79). scieloco.Cancer Research UK. (2022). Cancer Research UK Together we will beat cancer. https://www.cancerresearchuk.org/Channa, H. S. (2019). Customer lifetime value: An ensemble model approach. Advances in Intelligent Systems and Computing, 808, 353–363. https://doi.org/10.1007/978-981-13-1402-5_27Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013Chen, D. (2019). Online Retail II Data Set. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Online+Retail+IIChen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17Chen, P. P., Guitart, A., Del Río, A. F., & Periáñez, A. (2019). Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2134–2140. https://doi.org/10.1109/BigData.2018.8622151Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2018). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences, 1–7. https://doi.org/10.1016/j.jksuci.2018.09.004Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. TQM Journal, 32(4), 869–896. https://doi.org/10.1108/TQM-10-2019-0243Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284. https://doi.org/10.1287/mksc.1040.0098Forliano, C., De Bernardi, P., & Yahiaoui, D. (2021). Entrepreneurial universities: A bibliometric analysis within the business and management domains. Technological Forecasting and Social Change, 165, 1–16. https://doi.org/10.1016/j.techfore.2020.120522Guadarrama, T., & Rosales, E. M. (2015). Marketing relacional: valor, satisfacción, lealtad y retención del cliente. Análisis y reflexión teórica. Ciencia y Sociedad, 40(2), 307–340.Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48(5), 1275–1287. https://doi.org/10.1007/s10489-017-1105-yHall, C. M. (2011). Publish and perish? Bibliometric analysis, journal ranking and the assessment of research quality in tourism. Tourism Management, 32(1), 16–27. https://doi.org/10.1016/J.TOURMAN.2010.07.001Heldt, R., Silveira, C. S., & Luce, F. B. (2021). Predicting customer value per product: From RFM to RFM/P. Journal of Business Research, 127(March 2019), 444–453. https://doi.org/10.1016/j.jbusres.2019.05.001Herrera-Franco, G., Montalván-Burbano, N., Carrión-Mero, P., Apolo-Masache, B., & Jaya-Montalvo, M. (2020). Research trends in geotourism: A bibliometric analysis using the scopus database. Geosciences (Switzerland), 10(10), 1–29. https://doi.org/10.3390/geosciences10100379Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 1–5. https://doi.org/10.1073/pnas.0507655102Hurwitz, J., & Kirsch, D. (2018). Machine Learning for dummies (IBM Limite).Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2018). Modeling and application of customer lifetime value in online retail. Informatics, 5(1), 1–22. https://doi.org/10.3390/informatics5010002Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M. (2019). Comparative analysis of selected probabilistic customer lifetime value models in online shopping. Journal of Business Economics and Management, 20(3), 398–423. https://doi.org/10.3846/jbem.2019.9597Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018). Customer Segmentation using K-means Clustering. 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 135–139. https://doi.org/10.1109/CTEMS.2018.8769171Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. 2008 Eighth IEEE International Conference on Data Mining, 413–422. https://doi.org/10.1109/ICDM.2008.17Nita, A. (2019). Empowering impact assessments knowledge and international research collaboration - A bibliometric analysis of Environmental Impact Assessment Review journal. Environmental Impact Assessment Review, 78(March), 106283. https://doi.org/10.1016/j.eiar.2019.106283Oblander, E. S., Gupta, S., Mela, C. F., Winer, R. S., & Lehmann, D. R. (2020). The past, present, and future of customer management. Marketing Letters, 31(2–3), 125–136. https://doi.org/10.1007/s11002-020-09525-9Pareto, V. (1896). Cours d’économie politique. Université de Lausanne.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. http://jmlr.org/papers/v12/pedregosa11a.htmlPeralta, M. J., Frías, M., & Gregorio, O. (2015). Criterios, clasificaciones y tendencias de los indicadores bibliométricos en la evaluación de la ciencia. Revista Cubana de Información En Ciencias de La Salud, 26(3), 290–309. http://scielo.sld.cuPlatzer, M. (2021). Customer Base Analysis with BTYDplus (pp. 1–33). https://cran.r-project.org/web/packages/BTYDplus/vignettes/BTYDplus-HowTo.pdfRathi, T., & Ravi, V. (2017). Customer Lifetime Value Measurement using Machine Learning Techniques. In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 3013–3022). https://doi.org/10.4018/978-1-5225-1759-7.ch124Roy-García, I., Rivas-Ruiz, R., Pérez-Rodríguez, M., & Palacios-Cruz, L. (2019). Correlation: Not all correlation entails causality. Revista Alergia Mexico, 66(3), 354–360. https://doi.org/10.29262/ram.v66i3.651Rueda, G., Gerdsri, P., & Kocaoglu, D. F. (2007). Bibliometrics and Social Network Analysis of the Nanotechnology Field. PICMET ’07 - 2007 Portland International Conference on Management of Engineering & Technology, 2905–2911. https://doi.org/10.1109/PICMET.2007.4349633Sifa, R., Runge, J., Bauckhage, C., & Klapper, D. (2018). Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE. Hawaii International Conference on System Sciences, 923–932.Simeone, O. (2018). A Brief Introduction to Machine Learning for Engineers. Foundations and Trends® in Signal Processing, 12(3–4), 200–431. https://doi.org/10.1561/2000000102Sodhi, P., Awasthi, N., & Sharma, V. (2019). Introduction to Machine Learning and Its Basic Application in Python. SSRN Electronic Journal, 1354–1375. https://doi.org/10.2139/ssrn.3323796Srivastava, R. (2017). Identification of customer clusters using RFM model: a case of diverse purchaser classification. International Journal of Information, Business and Management, 9(4), 201–208.Tsai, T. Y., Lin, C. T., & Prasad, M. (2019). An Intelligent Customer Churn Prediction and Response Framework. Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, 928–935. https://doi.org/10.1109/ISKE47853.2019.9170380Valérie, D., & Pierre, A. G. (2010). Bibliometric idicators: Quality masurements of sientific publication. Radiology, 255(2), 342–351. https://doi.org/10.1148/radiol.09090626Velasco, B., Eiros Bouza, J. M., Pinilla, J. M., & San Román, J. A. (2012). La utilización de los indicadores bibliométricos para evaluar la actividad investigadora. Aula Abierta, 40(2), 75–84. http://dialnet.unirioja.es/servlet/articulo?codigo=3920967&info=resumen&idioma=ENGVilla, E., Ruiz, L., Valencia, A., & Picón, E. (2018). Electronic commerce: factors involved in its adoption from a bibliometric analysis. Journal of Theoretical and Applied Electronic Commerce Research, 13(1), 39–70. https://doi.org/10.4067/S0718-18762018000100104Win, T. T., & Bo, K. S. (2020). Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm. Proceedings of the 4th International Conference on Advanced Information Technologies, ICAIT 2020, 236–241. https://doi.org/10.1109/ICAIT51105.2020.9261792Yoseph, F., & Heikkila, M. (2019). Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method. Proceedings - International Conference on Machine Learning and Data Engineering, ICMLDE 2018, 77–82. https://doi.org/10.1109/iCMLDE.2018.00029InvestigadoresPúblico generalORIGINAL1152704626.2022.pdf1152704626.2022.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf2272384https://repositorio.unal.edu.co/bitstream/unal/81631/3/1152704626.2022.pdf8d94589be716724f5937d621af1c6e44MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81631/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1152704626.2022.pdf.jpg1152704626.2022.pdf.jpgGenerated Thumbnailimage/jpeg5124https://repositorio.unal.edu.co/bitstream/unal/81631/5/1152704626.2022.pdf.jpg13a40942b813c3c9ea1f75f4c48223d0MD55unal/81631oai:repositorio.unal.edu.co:unal/816312024-08-06 23:10:44.737Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

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

Publicaciones similares