Fathian, Mohammad, Azhdari, Ehsan. (1396). Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining. , 9(3), 549-570. doi: 10.22059/jitm.2017.61437
Mohammad Fathian; Ehsan Azhdari. "Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining". , 9, 3, 1396, 549-570. doi: 10.22059/jitm.2017.61437
Fathian, Mohammad, Azhdari, Ehsan. (1396). 'Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining', , 9(3), pp. 549-570. doi: 10.22059/jitm.2017.61437
Fathian, Mohammad, Azhdari, Ehsan. Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining. , 1396; 9(3): 549-570. doi: 10.22059/jitm.2017.61437

Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining

Journal of Information Technology Management
مقاله 7، دوره 9، شماره 3، 2017، صفحه 549-570    اصل مقاله (548.73 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2017.61437
نویسندگان
Mohammad Fathian* 1؛ Ehsan Azhdari2
1Prof. of System Engineering, Iran University of Science and Technology, Tehran, Iran
2MSc. Student in Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده
One of the most important issues in Customer Relationship Management is customer segmentation and product offer based on their needs. In practice, Customer’s behavior will change over the time by changes in technology, increase in the number of new customers and new competitors, and product variety. Traditional segmentation models that are static over time cannot predict these changes in customer’s behavior and ignore them. This challenge is especially critical in Telecommunication with high churn rates. In this research, we have used temporal fuzzy clustering to detect significant changes in customers' behavior for a telecom company during a 10-month period. The aim of this study is to find factors that affect structural and gradual changes in clustering model. In addition, we have suggested a method based on Frechet distance to extract similar patterns in customer’s usage behavior. Provided that combining the temporal clustering with trajectory analysis is an effective way to recognize customers’ behavior among the clusters, the results showed that there are seven distinct customer behavior patterns two of which lead to the customer drop or churn. These patterns can be used to reduce the risk and costs of customers churn and to design optimum services.
کلیدواژه‌ها
customer behavior؛ Data Mining؛ Dynamic Clustering؛ Fuzzy clustering؛ Trajectory Analysis
مراجع
آمار
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