This study explores the challenges of segmentation in the smart tourism era, where advanced technologies such as big data, artificial intelligence, and the Internet of Things are transforming the tourism industry. The purpose of this research is to analyze how traditional segmentation methods—based largely on socio-demographic criteria—are becoming insufficient in the face of increasingly dynamic, real-time data provided by smart tourism platforms. Using data-driven segmentation from two large Eurobarometer databases, the study applies a two-step cluster analysis to profile tourists based on their preferences, past experiences, use of technology, and destination-specific offers. The findings suggest that traditional segmentation based on age is no longer reliable, as tourist behaviors are now influenced more significantly by their interaction with technology and prior experiences. This paper contributes to the field by providing a refined understanding of how smart technologies can support the identification of more dynamic and actionable tourist segments. These insights offer practical applications for tourism and hospitality firms, enabling them to design more effective marketing strategies and personalized experiences that cater to the evolving expectations of modern tourists.