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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Frontiers in artificial intelligence Jg. 8; S. 1632415
Hauptverfasser: Schrader, Juan, Pinedo, Lloy, Vargas, Franz, Martell, Karla, Seijas-Díaz, José, Rengifo-Amasifen, Roger, Cueto-Orbe, Rosa, Torres-Silva, Cinthya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 04.08.2025
Schlagworte:
Agglomerative Clustering
Artificial Intelligence
clustering
DBSCAN
segmentation
tourists
Agglomerative Clustering
HDBSCAN
K-means
DBSCAN
segmentation
clustering
tourists
artificial intelligence
ISSN:2624-8212, 2624-8212
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Erfan Babaee Tirkolaee, University of Istinye, Türkiye
Reviewed by: Magdalena Graczyk-Kucharska, Poznan University of Life Sciences, Poland
Małgorzata Spychała, Poznań University of Technology, Poland
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2025.1632415