User Behavior Analysis And Marketing Strategy Study Of Online Travel Websites

As the continuous expansion of internet users and online travel markets, there also comes the increasingly fierce competition for online travel agencies. Now they all have the same target, which is to strengthen the strategic differentiation capacity to ensure sustainable development replying on their completive advantages. Before online travel agencies start to improve their online marketing campaigns or operations, they will need to invest a lot of resources to online user behavior analysis. With the rapid development of online travel industry, online travel agencies are requiring more advanced technologies or methods for user behavior analysis when they have enough user behavior information collected. So, this article will take a study in user behavior strategy analysis for the online hotel booking industry, and give ideas on online hotel marketing strategy based on the segmented users.Structural arrangement of the article include the background introduction on topic choice, the significance of the research, the process of the research, and then the main ideas out of the research, that is how we can make use of the website data and know why the users visit the site. How to give users better service, who of the users we are losing, and why we are losing them, how to carry out user behavior analysis and improve user experience accordingly, how to provide reasonable product recommendation and marketing strategies according to past user transactions are all what introduced in this article. Also, this article will give ideas on the importance and significance for online travel agencies in China by introducing the development and current status of the online travel industry.This article will take an online travel agency as the research object and combine the characteristics of online booking of hotel business to give user portrait by user segmentation model and user research, and have empirical research for user classification through cluster analysis. Then through cluster analysis by SPSS tool for their user bookings, the users are classified into5groups which include high-end business users, main-stream business users, high-end leisure users, main-stream leisure users and users making reservations for others. And then, we randomly select different users from the5groups and collect information through user interviews for consumer motivation, business travel habits, online hotel booking habits, as well as the factors affecting the users to choose online travel agencies when they want to book a hotel. This information has helped to rich the characteristics of various user groups, and also helped to prove that the user behavior segmentation mentioned in this article is effective. Based on the characteristics of different user groups, different marketing campaigns and differentiated service strategies are also suggested for different user groups aimed at improving the website conversion rate.Finally, it is the conclusion of the article. This article points out the innovation on practice and research methods, as well as its application effectiveness and weakness of the research.