Fuzzy Clustering Analysis of Hotel Online Booking Marketing—A Case of eLong Net

Abstract

In order to improve the network for hotel marketing quality of e-commerce and meet customers’ preferences, we, in the paper, propose a classification of hotel marketing quality on the basis of fuzzy clustering. The information of different types of 1579 hotels in Guangzhou was manually collected from the eLong net, including room characteristics, customer reviews, hotel service information, and reservation platform recommendation. Based on the indicator screening method to information sensitivity, indicators are constructed for 14 precision marketing evaluations. These 14 marketing evaluation indicators, affecting the marketing quality of different types of hotels, are refined through factor analysis into several key hotel marketing quality indicators. The extracted key indicators are clustered through a fuzzy C-means clustering method; based on the clustering results, the precise marketing differences are compared within different types of hotels. After that, by applying the entropy value method we identify the key factor affecting marketing quality in user ratings. Thus, for hotel precision marketing, it is suggested that an online booking marketing platform should be reconstructed with more friendship, stickiness, and valuableness as well.