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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Tianyuan, Z., Moro, S. & Ramos, R. F. (2022). A data-driven approach to improve customer churn prediction based on telecom customer segmentation. Future Internet. 14 (3)
Exportar Referência (IEEE) 
Z. Tianyuan et al.,  "A data-driven approach to improve customer churn prediction based on telecom customer segmentation", in Future Internet, vol. 14, no. 3, 2022
Exportar BibTeX 
@article{tianyuan2022_1730780426362,
	author = "Tianyuan, Z. and Moro, S. and Ramos, R. F.",
	title = "A data-driven approach to improve customer churn prediction based on telecom customer segmentation",
	journal = "Future Internet",
	year = "2022",
	volume = "14",
	number = "3",
	doi = "10.3390/fi14030094",
	url = "https://www.mdpi.com/journal/futureinternet"
}
Exportar RIS 
TY  - JOUR
TI  - A data-driven approach to improve customer churn prediction based on telecom customer segmentation
T2  - Future Internet
VL  - 14
IS  - 3
AU  - Tianyuan, Z.
AU  - Moro, S.
AU  - Ramos, R. F.
PY  - 2022
SN  - 1999-5903
DO  - 10.3390/fi14030094
UR  - https://www.mdpi.com/journal/futureinternet
AB  - 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.
ER  -