Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining
Author(s):
Abstract:
One of the most important issues in Customer Relationship Management is customer segmentation and product offer based on their needs. In practice, Customers 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 customers 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 customer's 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 customers 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.
Keywords:
Language:
Persian
Published:
Journal of Information Technology Management, Volume:9 Issue: 3, 2017
Pages:
549 to 570
https://www.magiran.com/p1738978