USING CUSTOMER ANALYTICS TO UNDERSTAND TOURIST PREFERENCES AND IMPROVE PERSONALISED TRAVEL EXPERIENCES ACROSS HOSPITALITY BUSINESSES

The rapid expansion of global tourism, increasing digitalization of hospitality services, and evolving consumer expectations have intensified the need for businesses to understand tourist preferences and deliver personalized travel experiences. Contemporary hospitality organizations generate substantial customer data through online reservations, digital interactions, loyalty programmes, reviews, and service transactions, creating opportunities for evidence-based decision-making and improved customer engagement. This study examines the application of customer analytics in identifying tourist preferences, predicting behavioural patterns, and enhancing service personalization across hospitality businesses. Particular attention is given to customer segmentation, predictive modelling, sentiment analysis, and recommendation systems for understanding travel motivations, accommodation choices, spending behaviour, and satisfaction drivers. Furthermore, the study explores how data-driven insights can support tailored service offerings, targeted marketing strategies, customer retention, and operational efficiency within hotels, resorts, and tourism service providers. It also considers challenges involving data privacy, algorithmic bias, technological integration, and changing consumer expectations. Ultimately, the study emphasizes the importance of responsible, adaptive, and customer-centred analytical frameworks in strengthening hospitality competitiveness, improving visitor satisfaction, and creating meaningful, personalized travel experiences across diverse tourism markets.