Big Data-Driven User Behavior Analysis and Experience Iteration Strategies for Hotel Supplier Portals

Abstract

Amidst the increasingly fierce competition in the hotel industry, user experience has emerged as a pivotal factor in enhancing supplier cooperation satisfaction and platform competitiveness. The rapid development of big data technology has provided robust support for user behavior analysis and experience optimization in hotel supplier portals. Drawing on the author’s practical experience in the hotel sector, this paper delves into big data-driven user behavior analysis methods and their application in experience iteration for hotel supplier portals. By analyzing user behavior data, this study proposes personalization-based recommendation, interface optimization, and function optimization strategies underpinned by A/B testing, and demonstrates their effectiveness through real-world cases. The findings indicate that the implementation of big data analysis and iteration strategies can significantly enhance user experience in hotel supplier portals, thereby improving supplier cooperation satisfaction and platform operational efficiency. This research not only offers theoretical support for the informatization construction of the hotel industry but also provides references for user experience optimization in other industries.