Research On Tourism User Behavior Decision Based On Online Reservation User Data

With the rapid development of tourism and the popularization of internet applications,more and more travelers through the network platform to obtain tourism information,yudingxian tourism products,publish tourism comments,share their travel experience.According to the 37th China internet development report,by December 2015,the number of users online booking hotels,air tickets,train tickets,and even rental cars reached million,of which the proportion of hotels online booking hotels 40.5%,up 54.1%.The large online reservation user scale stores a large number of online reservation users' consumption behavior data,however,the analysis and extraction of these user data will have potential use value for online tourism enterprises.This paper takes the online hotel reservation user data of x network as the sample research object,and uses clustering analysis,interview,mathematical statistics and other methods.First,the k-means clustering analysis is used to cluster five different user groups,as high-end user group,in the high-end user group,middle end group,Low-end user group,low-end user group,and the sensitivity of different user groups to 8 variables has obvious characteristics.Secondly,five kinds of user groups based on clustering results,random selection samples use interview method,the user structure characteristics of samples and the user behavior analysis online reservation hotel,mining the main factors of purchasing motivation,reservation behavior preference,reservation experience,to understand the potential needs and the most sensitive needs.Finally,the characteristics and requirements of different user groups after clustering,especially the main Low-end user group,optimize the online product information optimization classification strategy and recommendation priority strategy,provide differentiated services and jingzhunshi classification marketing,meet the real needs of user group,attract more online users to purchase and improve the conversion.The subdivision and potential of online booking data is necessary for online tourism enterprises,and it has a certain feasibility,which provides a stronger theoretical and practical basis for the rapid development of online tourism enterprises,and has certain guiding significance in the strategic decision-making of online tourism enterprises.