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Journals / Acta Infologica / 2026 / Volume: 9 Issue: 1

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

Muhammet Sinan Başarslan(İstanbul Medeniyet University, Faculty of Engineering and Natural Sciences, Computer Engineering)0000-0002-7996-9169
Aslıhan Ünal(İstanbul Gelişim University, Faculty of Economics, Administrative and Social Sciences, Department of Business Administration)0000-0001-5896-8880
Fatih Kayaalp(Düzce University, Faculty of Engineering, Computer Engineering)0000-0002-8752-3335
Pages
—
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed model is competitive with the existing models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Customer churn is an important issue in increasing both the long- and short-term revenues.
📊
Key Findings & Data:In addition, a 3-layered LSTM model was proposed.
💡
Conclusion & Impact:The proposed model is competitive with the existing models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Customer Churn Analysis · Ensemble Learning · Machine Learning

Abstract

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.

🔖

Cite This Article

Başarslan, M. S., Ünal, A., Kayaalp, F. (2025). A Novel and Robust LSTM Model for Customer Churn Analysis Using Deep, Machine Learning, and Ensemble Learning: A Telecommunications Case. Acta Infologica, Volume: 9 Issue: 1. https://doi.org/10.26650/acin.1584030

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