Research Article

DOI :10.26650/acin.1584030 IUP :10.26650/acin.1584030 Full Text (PDF)

A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case

Muhammet Sinan Başarslan, Aslıhan Ünal, Fatih Kayaalp

Customer churn is an important issue in increasing both the long- and short-term revenues. If companies identify customers’ churn behavior, they can prevent churn, ensure customer loyalty, and, in turn, gain better financial returns. The telecommunications sector is a customer-oriented sector that requires customer retention to survive in the market. In this sector, customer churn is observed at a high level. In recent years, artificial intelligence-based customer churn analysis has been widely used to predict customer churn behavior. In this study, a customer churn analysis was conducted using publicly shared Telco telecommunications data. Predictive models were constructed using machine learning (LR, KNN, SVM, DT, RF, ANN), ensemble learning (XGBoost, Majority Voting), and deep learning (LSTM) methods. In addition, a 3-layered LSTM model was proposed. Accuracy (Acc), F1-score (F1), Precision (Prec), and Recall (Rec) rates were used to evaluate the models. As a result, the novel 3-layered LSTM model achieved 91.90% Acc, 91.49% Prec, 92.31% Rec, and 91.90% F1 values. The proposed model is competitive with the existing models.

Keywords: Customer Churn Analysis, Ensemble Learning, Machine Learning, Deep Learning, Telecommunication

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APA

Başarslan, M.S., Ünal, A., & Kayaalp, F. (2019). A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case. Acta Infologica, 0(0), -. https://doi.org/10.26650/acin.1584030

AMA

Başarslan M S, Ünal A, Kayaalp F. A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case. Acta Infologica. 2019;0(0):-. https://doi.org/10.26650/acin.1584030

ABNT

Başarslan, M.S.; Ünal, A.; Kayaalp, F. A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case. Acta Infologica, [Publisher Location], v. 0, n. 0, p. -, 2019.

Chicago: Author-Date Style

Başarslan, Muhammet Sinan, and Aslıhan Ünal and Fatih Kayaalp. 2019. “A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case.” Acta Infologica 0, no. 0: -. https://doi.org/10.26650/acin.1584030

Chicago: Humanities Style

Başarslan, Muhammet Sinan, and Aslıhan Ünal and Fatih Kayaalp. “A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case.” Acta Infologica 0, no. 0 (Mar. 2025): -. https://doi.org/10.26650/acin.1584030

Harvard: Australian Style

Başarslan, MS & Ünal, A & Kayaalp, F 2019, 'A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case', Acta Infologica, vol. 0, no. 0, pp. -, viewed 10 Mar. 2025, https://doi.org/10.26650/acin.1584030

Harvard: Author-Date Style

Başarslan, M.S. and Ünal, A. and Kayaalp, F. (2019) ‘A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case’, Acta Infologica, 0(0), pp. -. https://doi.org/10.26650/acin.1584030 (10 Mar. 2025).

MLA

Başarslan, Muhammet Sinan, and Aslıhan Ünal and Fatih Kayaalp. “A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case.” Acta Infologica, vol. 0, no. 0, 2019, pp. -. [Database Container], https://doi.org/10.26650/acin.1584030

Vancouver

Başarslan MS, Ünal A, Kayaalp F. A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case. Acta Infologica [Internet]. 10 Mar. 2025 [cited 10 Mar. 2025];0(0):-. Available from: https://doi.org/10.26650/acin.1584030 doi: 10.26650/acin.1584030

ISNAD

Başarslan, MuhammetSinan - Ünal, Aslıhan - Kayaalp, Fatih. “A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case”. Acta Infologica 0/0 (Mar. 2025): -. https://doi.org/10.26650/acin.1584030

TIMELINE

Submitted	29.11.2024
Accepted	21.02.2025
Published Online	27.02.2025

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