Exploring Segmentation in eTourism: Clustering User Characteristics in Hotel Booking Situations Using k-Means

In the dynamic field of eTourism, personalization and user segmentation are paramount for enhancing user experience and driving digital platform success. This paper addresses the gap in eTourism research related to understanding consumer behavior through an external lens, due to the limited access to proprietary data from Online Travel Agencies (OTAs). We employ Adaptive Choice-Based Conjoint (ACBC) analysis and k-means clustering on data from a survey (n = 801) based on 346 hotel listings on Booking.com, focusing on Vienna. Attributes such as star category, price, review valence, volume of reviews, scarcity indicators, sustainability cues, and city center proximity were examined to identify consumer preferences. Five distinct consumer clusters were revealed: Cost-Conscious Eco-Bookers, Green-Urban Deal Hunters, Social-Proof Assurance Seekers, Budget-Only Focused Minimalists, and Luxury-Quality Connoisseurs. These clusters vary in their prioritization of hotel attributes and demographics, demonstrating the diverse decision-making criteria within the eTourism market. This paper proposes a foundation for classifying user groups on booking platforms, enabling OTAs and hoteliers to tailor offerings to nuanced consumer segments, thus improving user experiences and potentially increasing conversion rates. The findings offer actionable insights into OTA personalization strategies and contribute to the scientific understanding of consumer behavior in the digital tourism landscape.