Application of artificial intelligence techniques for the profiling of visitors to tourist destinations

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. © 2025 Elsevier B.V., All rights reserved.