Predicting Customers' Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry

Sajjad Shokouhyar, Parna Saeidpour and Ali Otarkhani
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Sajjad Shokouhyar: Department of Information Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
Parna Saeidpour: Department of Information Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
Ali Otarkhani: Department of Information Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran

International Journal of Information Systems in the Service Sector (IJISSS), 2018, vol. 10, issue 4, 59-72

Abstract: This article aims to predict reasons behind customers' churn in the mobile communication market. In this study, different data mining techniques such as logistic regression, decision trees, artificial neural networks, and K-nearest neighbor were examined. In addition, the general trend of the use of the techniques is presented, in order to identify and analyze customers' behavior and discover hidden patterns in the database of an active Coin the field of VAS1for mobile phones. Based on the results of this article, organizations and companies active in this area can identify customers' behavior and develop the required marketing strategies for each group of customers.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisss0:v:10:y:2018:i:4:p:59-72

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