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

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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)

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.

Suggested Citation

  • Sajjad Shokouhyar & Parna Saeidpour & Ali Otarkhani, 2018. "Predicting Customers' Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 10(4), pages 59-72, October.
    • Handle: RePEc:igg:jisss0:v:10:y:2018:i:4:p:59-72
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    Cited by:

    1. Sonya Zhang & Linda Ly & Norman Mach & Christian Amaya, 2022. "Topic Modeling and Sentiment Analysis of Yelp Restaurant Reviews," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 14(1), pages 1-16, January.

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