A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation

Tianyuan Zhang, Sérgio Moro and Ricardo F. Ramos
Additional contact information
Tianyuan Zhang: Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal
Sérgio Moro: Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal
Ricardo F. Ramos: Centro de Investigação em Ciências da Informação, Tecnologias e Arquitetura (ISTA), Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisbon, Portugal

Future Internet, 2022, vol. 14, issue 3, 1-19

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.

Keywords: telecommunications; customer segmentation; data mining; targeted marketing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/3/94/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/3/94/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:3:p:94-:d:772256

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().