فهرست مطالب امیر ارضی سلطان

Nowadays, the tourism industry is one of the cornerstones of any sustainable economy, in a manner that it assigns about 10 percent of the global economy. The purpose of this article is to study and analyze the profile information of users of one of the most popular social tourism websites called Tripdvisor, by utilizing data mining techniques. For this purpose, the database of the tripadvisor website was considered and the profile information of the users who at least had commented on one of the Iranian hotels was extracted. After that all the information of user profiles were evaluated by feature selection technique and the information that had the most impact on the clustering were identified. In the following, by calculating the Davies–Bouldin index, the optimal number of clusters was obtained and it was three, and the users were divided into three clusters. Each cluster had unique characteristics that were named pioneers, aristocratic friends travel, and low-travel users. Then, based on the characteristics of each of these three clusters and their users' profile information by using association rules, each cluster was attempted to be further identified. Finally, proportional with the analyzed features, some solutions and approaches were provided to increase the participation of these users in the website and targeted promoting programs for each of these users' cluster.

This research aims at associating two groups of demographic and transaction related factors and furthermore, proposes customer churn factor as another influential factor in customer value analysis. To this end, at first, customers' transaction data in a real local ISP in a four year period are utilized for segmentation purpose using k-means method. Regarding service nature of the case, customers' behavior has been considered in terms of customer satisfaction factors. The results of Exploratory Factor Analysis (EFA) indicate that three factors of service quality, expectation confirmation and post-purchasing satisfaction are influential factors. Then, association rules using GRI algorithm are exploited in order to investigate among customers' behaviors and propose appropriate strategies and action plans for each customer segment. Finally, segmentation results are associated with physiographic variables and existence of a significant difference among identified segments is more investigated using one way ANOVA test. The results clarified that expectation confirmation and post- purchasing satisfaction as hygienic factors and service quality as an incentive factor influence customer loyalty.