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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1632415/full |
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| author | Juan Schrader Lloy Pinedo Franz Vargas Karla Martell José Seijas-Díaz Roger Rengifo-Amasifen Rosa Cueto-Orbe Cinthya Torres-Silva |
| author_facet | Juan Schrader Lloy Pinedo Franz Vargas Karla Martell José Seijas-Díaz Roger Rengifo-Amasifen Rosa Cueto-Orbe Cinthya Torres-Silva |
| author_sort | Juan Schrader |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5f86c03caee042bba0e207dcdb79131a |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-5f86c03caee042bba0e207dcdb79131a2025-08-20T03:57:31ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.16324151632415Application of artificial intelligence techniques for the profiling of visitors to tourist destinationsJuan Schrader0Lloy Pinedo1Franz Vargas2Karla Martell3José Seijas-Díaz4Roger Rengifo-Amasifen5Rosa Cueto-Orbe6Cinthya Torres-Silva7Grupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Transformación Digital Empresarial, Facultad de Ingeniería y Negocios, Universidad Privada Norbert Wiener, Lima, PeruGrupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Innovación Turística y Comercio Exterior, Facultad de Ciencias Económicas, Administrativas y Contables, Universidad Nacional Autónoma de Alto Amazonas, Yurimaguas, PeruGrupo de Investigación Gestión ATEC, Facultad de Ciencias Económicas, Universidad Nacional de San Martín, Tarapoto, PeruTourism 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1632415/fullartificial intelligencesegmentationclusteringtouristsAgglomerative ClusteringDBSCAN |
| spellingShingle | Juan Schrader Lloy Pinedo Franz Vargas Karla Martell José Seijas-Díaz Roger Rengifo-Amasifen Rosa Cueto-Orbe Cinthya Torres-Silva Application of artificial intelligence techniques for the profiling of visitors to tourist destinations Frontiers in Artificial Intelligence artificial intelligence segmentation clustering tourists Agglomerative Clustering DBSCAN |
| title | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| title_full | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| title_fullStr | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| title_full_unstemmed | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| title_short | Application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| title_sort | application of artificial intelligence techniques for the profiling of visitors to tourist destinations |
| topic | artificial intelligence segmentation clustering tourists Agglomerative Clustering DBSCAN |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1632415/full |
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