H Hotel Online Booking Customer Behavior Analysis And Marketing Research

With the development of the domestic economy and the tourism industry, the hotel industry is becoming increasingly fierce competition. Online reservation booking major way as a modern tourism, hotel business sales constitute an important factor. To ensure the competitiveness of enterprises, optimize marketing strategies, invest a lot of resources to analyze customer behavior online booking, user information clustering analysis and data mining has become the focus of attention of the hotel management industry. At this stage, the data size of hotel employees in daily operations and customer information management process to deal with the exponential growth, TB, PB, EB and even the amount of data is no longer uncommon, this is the clustering analysis and data mining work of the new challenge. Using the traditional stand-alone data processing mode single treatment, not only takes a lot of time and human resources, and because of the limited stand-alone processing power will eventually lead to the growing amount of information exceeds the time data processing companies acceptable range.Against this background, the hotel management systems need to build an efficient information handling, massive customer information data collection and collation, research customer booking behavior. In general, there are two ways. The first is to improve the single processing efficiency and increase the internal memory. This way the effect is obvious, can meet the needs of enterprise information processing in a short time, but the acquisition of the update and the storage unit of the device usually takes large costs. Therefore, this idea of distributed parallel processing using the inexpensive minicomputers, or microcomputers constitute a processing cluster, the K-means algorithm is applied to a distributed system, with a view of H hotel customers booking behavior analysis and research.The paper is divided into six chapters. Firstly, elaborated on the research background, significance, research status. Secondly, this paper introduces related to the relevant theories and concepts. The Third chapter introduce the H hotel background to analyses the current situation of the online booking behavior and its customers, puts forward the necessity to improve the management of H hotel online booking. Chapters IV and V for the full text of the core chapters, followed by the hotel’s customer information for H data collection, data preprocessing, variable selection, cluster analysis process, and finally the results obtained are discussed. Chapter V behavioral event interview explore factors affecting the classification of Chapter IV of the different categories of users booking behavior. Chapter VI, according to the analysis results for the fourth and fifth chapters of the proposed customer segments based marketing strategies, including personalized service strategy, the new promotional model, new product strategies. Finally, the paper summarizes the shortcomings of the system design process exists, and on related issues were discussed.