Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/24847
Author(s): | Tianyuan, Z. Moro, S. Ramos, R. F. |
Date: | 2022 |
Title: | A data-driven approach to improve customer churn prediction based on telecom customer segmentation |
Volume: | 14 |
Number: | 3 |
ISSN: | 1999-5903 |
DOI (Digital Object Identifier): | 10.3390/fi14030094 |
Keywords: | Telecommunications Customer segmentation Data mining Targeted marketing |
Abstract: | Numerous valuable clients can be lost to competitors in the telecommunication industry, leading to profit loss. Thus, understanding the reasons for client churn is vital for telecommunication companies. This study aimed to develop a churn prediction model to predict telecom client churn through customer segmentation. Data were collected from three major Chinese telecom companies, and Fisher discriminant equations and logistic regression analysis were used to build a telecom customer churn prediction model. According to the results, it can be concluded that the telecom customer churn model constructed by regression analysis had higher prediction accuracy (93.94%) and better results. This study will help telecom companies efficiently predict the possibility of and take targeted measures to avoid customer churn, thereby increasing their profits.Numerous valuable clients can be lost to competitors in the telecommunication industry, leading to profit loss. Thus, understanding the reasons for client churn is vital for telecommunication companies. This study aimed to develop a churn prediction model to predict telecom client churn through customer segmentation. Data were collected from three major Chinese telecom companies, and Fisher discriminant equations and logistic regression analysis were used to build a telecom customer churn prediction model. According to the results, it can be concluded that the telecom customer churn model constructed by regression analysis had higher prediction accuracy (93.94%) and better results. This study will help telecom companies efficiently predict the possibility of and take targeted measures to avoid customer churn, thereby increasing their profits. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
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article_88075.pdf | Versão Editora | 1,24 MB | Adobe PDF | View/Open |
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