Predicting Customers' Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry
| Autor: | Parna Saeidpour, Ali Otarkhani, Sajjad Shokouhyar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Information Systems and Management
Computer science business.industry Strategy and Management 05 social sciences Decision tree 02 engineering and technology Management Science and Operations Research computer.software_genre Logistic regression Management Information Systems 0502 economics and business Customer classification Value (economics) 0202 electrical engineering electronic engineering information engineering 050211 marketing 020201 artificial intelligence & image processing Data mining Mobile telephony business computer Information Systems |
| Zdroj: | International Journal of Information Systems in the Service Sector. 10:59-72 |
| ISSN: | 1935-5696 1935-5688 |
| DOI: | 10.4018/ijisss.2018100104 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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