Análisis de agrupamiento de clientes en reservas de hotel
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oct 5, 2026
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https://doi.org/10.36677/elperiplo.v0i51.27801
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Jaime Josué Morales Morales
http://orcid.org/0000-0001-7723-4137
José Rosario Lara Salazar
http://orcid.org/0000-0002-7174-4854
Arturo Yee Rendón
http://orcid.org/0000-0002-9052-6588
http://orcid.org/0000-0001-7723-4137
José Rosario Lara Salazar
http://orcid.org/0000-0002-7174-4854
Arturo Yee Rendón
http://orcid.org/0000-0002-9052-6588
Resumen
El presente estudio tiene como objetivo realizar un análisis de agrupamiento de clientes de un servicio hotelero mediante técnicas de aprendizaje no supervisado, con el fin de identificar perfiles diferenciados de comportamiento y mejorar las estrategias de atención y fidelización. Para ello, se utilizó el algoritmo k-medias para agrupar a los clientes a partir de variables numéricas y categóricas relacionadas con el proceso de reserva, el tipo de cliente y las características de la estancia. El número óptimo de clústeres (grupos de perfiles) se determinó mediante el método del codo, identificándose cinco grupos claramente diferenciados, los cuales fueron validados e interpretados mediante análisis estadísticos y visualización con t-SNE. Cada clúster refleja un perfil distintivo, como familias, parejas espontáneas o parejas de alta demanda, que presenta patrones específicos en la anticipación de la reserva, la duración de la estancia, la composición del grupo y el canal de adquisición. Los resultados obtenidos proporcionan una base sólida para el diseño de estrategias comerciales personalizadas y la optimización operativa en el sector hotelero.
Palabras clave:
Segmentación de clientes, análisis de clústeres, aprendizaje no supervisado, algoritmo k-medias, sector hotelero.
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Article Details
Como citar
MORALES MORALES, Jaime Josué; LARA SALAZAR, José Rosario; YEE RENDÓN, Arturo.
Análisis de agrupamiento de clientes en reservas de hotel.
El Periplo Sustentable, [S.l.], n. 51, p. 88 - 108, oct. 2026.
ISSN 1870-9036.
Disponible en: <https://rperiplo.uaemex.mx/article/view/27801>. Fecha de acceso: 05 oct. 2026
doi: https://doi.org/10.36677/elperiplo.v0i51.27801.
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Artículos de investigación
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Citas
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Bagirov, A. M., Taheri, S., & Ugon, J. (2016). Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems. Pattern Recognition, 53, 12-24. https://doi.org/10.1016/j.patcog.2015.11.011
Bandyapadhyay, S., Fomin, F. V., Golovach, P. A., Lochet, W., Purohit, N., & Simonov, K. (2023). How to find a good explanation for clustering? Artificial Intelligence, 322, 103948. https://doi.org/10.1016/j.artint.2023.103948
Bondzi-Simpson, A., & Ayeh, J. K. (2019). Assessing hotel readiness to offer local cuisines: a clustering approach. International Journal of Contemporary Hospitality Management, 31(2), 998-1020. https://doi.org/10.1108/IJCHM-12-2017-0820
Bulchand-Gidumal, J. (2022). Impact of Artificial Intelligence in Travel, Tourism, and Hospitality. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken, Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-48652-5_110
Chen, J., Xu, Y., Yu, P., & Zhang, J. (2023). A reinforcement learning approach for hotel revenue management with evidence from field experiments. Journal of Operation Management, 69(7), 1176–1201. https://doi.org/10.1002/joom.1246
Cherenkov, E., Benga, V., Lee, M., Nandwani, N., Raguin, K., Sueur, M. C., & Sun, G. (2024). From Machine Learning Algorithms to Superior Customer Experience: Business Implications of Machine Learning-Driven Data Analytics in the Hospitality Industry. Journal of Smart Tourism, 4(2), 5-14. https://doi.org/10.52255/smarttourism.2024.4.2.2
de Souza, A., Moro, S., & Pereira, R. (2024). Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company. Applied Sciences, 14(1), 78. https://doi.org/10.3390/app14010078
Eibl, S., Fina, R., & Auinger, A. (2024). Exploring Segmentation in eTourism: Clustering User Characteristics in Hotel Booking Situations Using k-Means. In F. S. Nah, HCI en empresas, gobiernos y organizaciones. (Vol. 14720, pp. 157–175). https://doi.org/10.1007/978-3-031-61315-9_11
Fränti, P., & Sieranoja, S. (2019). How much can k-means be improved by using better initialization and repeats? Pattern Recognition, 93, 95-112. https://doi.org/10.1016/j.patcog.2019.04.014
Golalipour, K., Akbari, E., Hamidi, S. S., Lee, M., & Enayatifar, R. (2021). From clustering to clustering ensemble selection: A review. Engineering Applications of Artificial Intelligence, 104, 104388. https://doi.org/10.1016/j.engappai.2021.104388
Herrera, A., Arroyo, Á., Jiménez, A., & Herrero, Á. (2024). Forecasting hotel cancellations through machine learning. Expert Systems, 41(9), e13608. https://doi.org/10.1111/exsy.13608
Kaya, K., Yilmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, 59(1), 102816. https://doi.org/10.1016/j.ipm.2021.102816
Kumar, A., Mahato, B., Kujur, A., Pandey, M., Kumar, B., & Alam, S. (2024). Predicting Cancellations of Hotel Reservations with Unsupervised Machine Learning Methods. 2024 International BIT Conference (BITCON), (pp. 1-6). Dhanbad, India,. https://doi.org/10.1109/BITCON63716.2024.10985602.
Maaten, L. V., & Hinton, G. E. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579-2605.
Oliveira, F. B., Silva-Filho, M. W., Barbosa, G. A., Freitas, J. P., Penna, C., & Miranda, P. B. (2025). Machine Learning and Time Series Analysis to Forecast Hotel Room Prices. In A. V. Paes, Intelligent Systems (Vol. 15414). Springer, Cham. https://doi.org/10.1007/978-3-031-79035-5_25
Oyewole, G. J., & Thopil, G. A. (2023). Data clustering: application and trends. Artificial Intelligence Review, 56, 6439–6475. https://doi.org/10.1007/s10462-022-10325-y
Parvez, M. O. (2021). Use of machine learning technology for tourist and organizational services: high-tech innovation in the hospitality industry. Journal of Tourism Futures, 7(2), 240-244. https://doi.org/10.1108/JTF-09-2019-0083
Puh, K., & Bagic´, B. (2023). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188-1204. https://doi.org/10.1108/JHTI-02-2022-0078
Ramzán, B., Bajwa, I. S., Jamil, N., Amin, R. U., Ramzan, S., Mirza, F., & Sarwar, N. (2019). An Intelligent Data Analysis for Recommendation Systems Using Machine Learning. Scientific Programming, 1-20. https://doi.org/10.1155/2019/5941096
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
Sánchez-Medina, A. J., & C-Sánchez, E. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546. https://doi.org/10.1016/j.ijhm.2020.102546
Sánchez-Rivero, M., Rodríguez-Rangel, M. C., & Ricci-Risquete, A. (2023). K-Means segmentation of tourism accommodation based on the active use of websites: Its application to an emerging destination (extremadura, Spain). Journal of Vacation Marketing, 29(4), 654-669. https://doi.org/10.1177/13567667221117303
Schubert, E. (2023). Stop using the elbow criterion for k-means and how to choose the number of clusters instead. ACM SIGKDD Exploration Newsletter, 25(1), 36-42. https://doi.org/10.1145/3606274.3606278
Shu, Z., Carrasco, G. R., García-Miguel, J. P., & Sánchez-Montañés, M. (2023). Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: The case of TripAdvisor. Expert Systems with Applications, 213, 118922. https://doi.org/10.1016/j.eswa.2022.118922
van Leeuwen, R., & Koole, G. (2022). Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning. Machine Learning with Applications, 10, 100414. https://doi.org/10.1016/j.mlwa.2022.100414
Vargas-Calderón, V., Moros, O. A., Castro, N. G., & Camargo, J. E. (2021). Machine learning for assessing quality of service in the hospitality sector based on customer reviews. Information Technology & Tourism, 23(3), 351-379. https://doi.org/10.1007/s40558-021-00207-4
Viverit, L., Yoonjoung, H. C., Nobre, P. L., & Tiana, G. (2023). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111, 103455. https://doi.org/10.1016/j.ijhm.2023.103455
Vives, A., & Ostrovskaya, L. (2023). Exploring the impact of hotel attributes and services on price and revenue: A hedonic pricing model approach with a focus on internal and external segmentation and repeat customers. Tourism Economics, 30(4), 813-843. https://doi.org/10.1177/1354816623120037
Yildirim, M. E., Murat, K., & Ibrahim, F. (2022). A Case Study: Unsupervised Approach for Tourist Profile Analysis by K-means Clustering in Turkey. Journal of Internet Computing and Services, 23(1), 11-17. https://doi.org/10.7472/JKSII.2022.23.1.11
Zhang, X., & Yu, Q. (2017). Hotel reviews sentiment analysis based on word vector clustering. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). Beijing, China. https://doi.org/10.1109/CIAPP.2017.8167219
Ampountolas, A. (2025). Predicting hotel booking cancellations: a comprehensive machine learning approach. Journal of Revenue and Pricing Management, 24, 539–550. https://doi.org/10.1057/s41272-025-00532-x
Ampountolas, A., & Legg, M. (2025). A comprehensive approach to enhancing short-term hotel cancellation forecasts through dynamic machine learning models. Tourism Economics, 32(2). https://doi.org/10.1177/13548166251318768
Antonio, N., de Almeida, A., & Nunes, L. (2019). Hotel booking demand datasets. Data in Brief, 22, 41-49. https://doi.org/10.1016/j.dib.2018.11.126
Bagirov, A. M., Taheri, S., & Ugon, J. (2016). Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems. Pattern Recognition, 53, 12-24. https://doi.org/10.1016/j.patcog.2015.11.011
Bandyapadhyay, S., Fomin, F. V., Golovach, P. A., Lochet, W., Purohit, N., & Simonov, K. (2023). How to find a good explanation for clustering? Artificial Intelligence, 322, 103948. https://doi.org/10.1016/j.artint.2023.103948
Bondzi-Simpson, A., & Ayeh, J. K. (2019). Assessing hotel readiness to offer local cuisines: a clustering approach. International Journal of Contemporary Hospitality Management, 31(2), 998-1020. https://doi.org/10.1108/IJCHM-12-2017-0820
Bulchand-Gidumal, J. (2022). Impact of Artificial Intelligence in Travel, Tourism, and Hospitality. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken, Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-48652-5_110
Chen, J., Xu, Y., Yu, P., & Zhang, J. (2023). A reinforcement learning approach for hotel revenue management with evidence from field experiments. Journal of Operation Management, 69(7), 1176–1201. https://doi.org/10.1002/joom.1246
Cherenkov, E., Benga, V., Lee, M., Nandwani, N., Raguin, K., Sueur, M. C., & Sun, G. (2024). From Machine Learning Algorithms to Superior Customer Experience: Business Implications of Machine Learning-Driven Data Analytics in the Hospitality Industry. Journal of Smart Tourism, 4(2), 5-14. https://doi.org/10.52255/smarttourism.2024.4.2.2
de Souza, A., Moro, S., & Pereira, R. (2024). Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company. Applied Sciences, 14(1), 78. https://doi.org/10.3390/app14010078
Eibl, S., Fina, R., & Auinger, A. (2024). Exploring Segmentation in eTourism: Clustering User Characteristics in Hotel Booking Situations Using k-Means. In F. S. Nah, HCI en empresas, gobiernos y organizaciones. (Vol. 14720, pp. 157–175). https://doi.org/10.1007/978-3-031-61315-9_11
Fränti, P., & Sieranoja, S. (2019). How much can k-means be improved by using better initialization and repeats? Pattern Recognition, 93, 95-112. https://doi.org/10.1016/j.patcog.2019.04.014
Golalipour, K., Akbari, E., Hamidi, S. S., Lee, M., & Enayatifar, R. (2021). From clustering to clustering ensemble selection: A review. Engineering Applications of Artificial Intelligence, 104, 104388. https://doi.org/10.1016/j.engappai.2021.104388
Herrera, A., Arroyo, Á., Jiménez, A., & Herrero, Á. (2024). Forecasting hotel cancellations through machine learning. Expert Systems, 41(9), e13608. https://doi.org/10.1111/exsy.13608
Kaya, K., Yilmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, 59(1), 102816. https://doi.org/10.1016/j.ipm.2021.102816
Kumar, A., Mahato, B., Kujur, A., Pandey, M., Kumar, B., & Alam, S. (2024). Predicting Cancellations of Hotel Reservations with Unsupervised Machine Learning Methods. 2024 International BIT Conference (BITCON), (pp. 1-6). Dhanbad, India,. https://doi.org/10.1109/BITCON63716.2024.10985602.
Maaten, L. V., & Hinton, G. E. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579-2605.
Oliveira, F. B., Silva-Filho, M. W., Barbosa, G. A., Freitas, J. P., Penna, C., & Miranda, P. B. (2025). Machine Learning and Time Series Analysis to Forecast Hotel Room Prices. In A. V. Paes, Intelligent Systems (Vol. 15414). Springer, Cham. https://doi.org/10.1007/978-3-031-79035-5_25
Oyewole, G. J., & Thopil, G. A. (2023). Data clustering: application and trends. Artificial Intelligence Review, 56, 6439–6475. https://doi.org/10.1007/s10462-022-10325-y
Parvez, M. O. (2021). Use of machine learning technology for tourist and organizational services: high-tech innovation in the hospitality industry. Journal of Tourism Futures, 7(2), 240-244. https://doi.org/10.1108/JTF-09-2019-0083
Puh, K., & Bagic´, B. (2023). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188-1204. https://doi.org/10.1108/JHTI-02-2022-0078
Ramzán, B., Bajwa, I. S., Jamil, N., Amin, R. U., Ramzan, S., Mirza, F., & Sarwar, N. (2019). An Intelligent Data Analysis for Recommendation Systems Using Machine Learning. Scientific Programming, 1-20. https://doi.org/10.1155/2019/5941096
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
Sánchez-Medina, A. J., & C-Sánchez, E. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546. https://doi.org/10.1016/j.ijhm.2020.102546
Sánchez-Rivero, M., Rodríguez-Rangel, M. C., & Ricci-Risquete, A. (2023). K-Means segmentation of tourism accommodation based on the active use of websites: Its application to an emerging destination (extremadura, Spain). Journal of Vacation Marketing, 29(4), 654-669. https://doi.org/10.1177/13567667221117303
Schubert, E. (2023). Stop using the elbow criterion for k-means and how to choose the number of clusters instead. ACM SIGKDD Exploration Newsletter, 25(1), 36-42. https://doi.org/10.1145/3606274.3606278
Shu, Z., Carrasco, G. R., García-Miguel, J. P., & Sánchez-Montañés, M. (2023). Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: The case of TripAdvisor. Expert Systems with Applications, 213, 118922. https://doi.org/10.1016/j.eswa.2022.118922
van Leeuwen, R., & Koole, G. (2022). Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning. Machine Learning with Applications, 10, 100414. https://doi.org/10.1016/j.mlwa.2022.100414
Vargas-Calderón, V., Moros, O. A., Castro, N. G., & Camargo, J. E. (2021). Machine learning for assessing quality of service in the hospitality sector based on customer reviews. Information Technology & Tourism, 23(3), 351-379. https://doi.org/10.1007/s40558-021-00207-4
Viverit, L., Yoonjoung, H. C., Nobre, P. L., & Tiana, G. (2023). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111, 103455. https://doi.org/10.1016/j.ijhm.2023.103455
Vives, A., & Ostrovskaya, L. (2023). Exploring the impact of hotel attributes and services on price and revenue: A hedonic pricing model approach with a focus on internal and external segmentation and repeat customers. Tourism Economics, 30(4), 813-843. https://doi.org/10.1177/1354816623120037
Yildirim, M. E., Murat, K., & Ibrahim, F. (2022). A Case Study: Unsupervised Approach for Tourist Profile Analysis by K-means Clustering in Turkey. Journal of Internet Computing and Services, 23(1), 11-17. https://doi.org/10.7472/JKSII.2022.23.1.11
Zhang, X., & Yu, Q. (2017). Hotel reviews sentiment analysis based on word vector clustering. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). Beijing, China. https://doi.org/10.1109/CIAPP.2017.8167219