Big Data and Artificial Intelligence In Tourism: Enhancing Customer Experience And Market Insights
Abstract
Big Data and Artificial Intelligence (AI) are transforming the tourism industry by enhancing customer experience and providing deep market insights. Leveraging these technologies enables personalized recommendations, demand forecasting, and efficient resource management. However, existing methods often fail to accurately analyze dynamic tourist behaviors due to data complexity, lack of real-time processing, and ineffective segmentation techniques. To address these challenges, this study proposes the Analyzing Tourist Behavior using Clustering Algorithms (ATB-CA) framework, which utilizes machine learning-based clustering techniques such as K-Means, DBSCAN, and hierarchical clustering. These methods effectively segment tourists based on preferences, spending patterns, and travel behaviors, enabling businesses to tailor services accordingly. The proposed ATB-CA framework enhances decision-making by integrating real-time data analytics, predictive modeling, and customer profiling to improve marketing strategies and optimize tourism services. By implementing this approach, tourism stakeholders can gain a deeper understanding of market trends and customer needs. Findings indicate that the ATB-CA framework significantly improves customer satisfaction by offering more personalized experiences while boosting revenue generation through precise market segmentation. The study highlights the effectiveness of AI-driven clustering techniques in refining tourism strategies, ultimately contributing to a more dynamic and customer-centric industry.