Application of artificial intelligence techniques for the profiling of visitors to tourist destinations
Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategie...
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| Veröffentlicht in: | Frontiers in artificial intelligence Jg. 8; S. 1632415 |
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04.08.2025
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| Abstract | Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. The research followed the CRISP-DM methodology for data analysis, based on surveys administered to 882 visitors. The data were processed using the clustering algorithms K-Means, DBSCAN, HDBSCAN, and Agglomerative, with Principal Component Analysis applied beforehand for dimensionality reduction. The results showed that the Agglomerative Clustering model achieved the best performance in internal validation metrics, allowing for the identification of five distinct visitor profiles. These segments provide valuable insights for the design of more inclusive and personalized tourism products. In conclusion, the study demonstrates the value of machine learning as a tool for tourism segmentation, offering empirical evidence that can strengthen the management of emerging destinations such as Alto Amazonas. The practical contribution of this study lies in providing strategic information that enables destination managers to tailor services and experiences to the characteristics of each segment, thereby optimizing visitor satisfaction and strengthening the destination’s competitiveness. |
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| AbstractList | Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. The research followed the CRISP-DM methodology for data analysis, based on surveys administered to 882 visitors. The data were processed using the clustering algorithms K-Means, DBSCAN, HDBSCAN, and Agglomerative, with Principal Component Analysis applied beforehand for dimensionality reduction. The results showed that the Agglomerative Clustering model achieved the best performance in internal validation metrics, allowing for the identification of five distinct visitor profiles. These segments provide valuable insights for the design of more inclusive and personalized tourism products. In conclusion, the study demonstrates the value of machine learning as a tool for tourism segmentation, offering empirical evidence that can strengthen the management of emerging destinations such as Alto Amazonas. The practical contribution of this study lies in providing strategic information that enables destination managers to tailor services and experiences to the characteristics of each segment, thereby optimizing visitor satisfaction and strengthening the destination's competitiveness.Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. The research followed the CRISP-DM methodology for data analysis, based on surveys administered to 882 visitors. The data were processed using the clustering algorithms K-Means, DBSCAN, HDBSCAN, and Agglomerative, with Principal Component Analysis applied beforehand for dimensionality reduction. The results showed that the Agglomerative Clustering model achieved the best performance in internal validation metrics, allowing for the identification of five distinct visitor profiles. These segments provide valuable insights for the design of more inclusive and personalized tourism products. In conclusion, the study demonstrates the value of machine learning as a tool for tourism segmentation, offering empirical evidence that can strengthen the management of emerging destinations such as Alto Amazonas. The practical contribution of this study lies in providing strategic information that enables destination managers to tailor services and experiences to the characteristics of each segment, thereby optimizing visitor satisfaction and strengthening the destination's competitiveness. Tourism in Peru represents an opportunity for local development; however, there is limited understanding of visitor profiles. The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. The research followed the CRISP-DM methodology for data analysis, based on surveys administered to 882 visitors. The data were processed using the clustering algorithms K-Means, DBSCAN, HDBSCAN, and Agglomerative, with Principal Component Analysis applied beforehand for dimensionality reduction. The results showed that the Agglomerative Clustering model achieved the best performance in internal validation metrics, allowing for the identification of five distinct visitor profiles. These segments provide valuable insights for the design of more inclusive and personalized tourism products. In conclusion, the study demonstrates the value of machine learning as a tool for tourism segmentation, offering empirical evidence that can strengthen the management of emerging destinations such as Alto Amazonas. The practical contribution of this study lies in providing strategic information that enables destination managers to tailor services and experiences to the characteristics of each segment, thereby optimizing visitor satisfaction and strengthening the destination's competitiveness. |
| Author | Vargas, Franz Torres-Silva, Cinthya Martell, Karla Seijas-Díaz, José Schrader, Juan Pinedo, Lloy Cueto-Orbe, Rosa Rengifo-Amasifen, Roger |
| AuthorAffiliation | 1 Grupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas , Yurimaguas , Peru 2 Grupo de Investigación Transformación Digital Empresarial, Facultad de Ingeniería y Negocios, Universidad Privada Norbert Wiener , Lima , Peru 3 Grupo de Investigación Gestión ATEC, Facultad de Ciencias Económicas, Universidad Nacional de San Martín , Tarapoto , Peru |
| AuthorAffiliation_xml | – name: 2 Grupo de Investigación Transformación Digital Empresarial, Facultad de Ingeniería y Negocios, Universidad Privada Norbert Wiener , Lima , Peru – name: 1 Grupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas , Yurimaguas , Peru – name: 3 Grupo de Investigación Gestión ATEC, Facultad de Ciencias Económicas, Universidad Nacional de San Martín , Tarapoto , Peru |
| Author_xml | – sequence: 1 givenname: Juan surname: Schrader fullname: Schrader, Juan – sequence: 2 givenname: Lloy surname: Pinedo fullname: Pinedo, Lloy – sequence: 3 givenname: Franz surname: Vargas fullname: Vargas, Franz – sequence: 4 givenname: Karla surname: Martell fullname: Martell, Karla – sequence: 5 givenname: José surname: Seijas-Díaz fullname: Seijas-Díaz, José – sequence: 6 givenname: Roger surname: Rengifo-Amasifen fullname: Rengifo-Amasifen, Roger – sequence: 7 givenname: Rosa surname: Cueto-Orbe fullname: Cueto-Orbe, Rosa – sequence: 8 givenname: Cinthya surname: Torres-Silva fullname: Torres-Silva, Cinthya |
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| Copyright | Copyright © 2025 Schrader, Pinedo, Vargas, Martell, Seijas-Díaz, Rengifo-Amasifen, Cueto-Orbe and Torres-Silva. Copyright © 2025 Schrader, Pinedo, Vargas, Martell, Seijas-Díaz, Rengifo-Amasifen, Cueto-Orbe and Torres-Silva. 2025 Schrader, Pinedo, Vargas, Martell, Seijas-Díaz, Rengifo-Amasifen, Cueto-Orbe and Torres-Silva |
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| Keywords | Agglomerative Clustering HDBSCAN K-means DBSCAN segmentation clustering tourists artificial intelligence |
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| License | Copyright © 2025 Schrader, Pinedo, Vargas, Martell, Seijas-Díaz, Rengifo-Amasifen, Cueto-Orbe and Torres-Silva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Title | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
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