Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Ceren Aksoy; Ayhan Küçükmanisa; Zeynep Hilal Kilimci

Makale detayları

Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Yazarlar:

Ceren Aksoy | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Kocaeli Journal of Science and Engineering Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348822

pISSN:

2667-484X

Anahtar kelimeler:

Özet:

 Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record CDR. For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94 accuracy for the IBM Watson dataset and 96.34 accuracy for the Call Detailed Report CDR dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

 Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record CDR. For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94 accuracy for the IBM Watson dataset and 96.34 accuracy for the Call Detailed Report CDR dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

 Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record CDR. For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94 accuracy for the IBM Watson dataset and 96.34 accuracy for the Call Detailed Report CDR dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

 Customer churn forecasting is a challenging task recommended for churn prevention for companies operating in various industries such as banking, telecommunications, and insurance. Forecasting customer churn is very important for many companies because gaining potential customers usually costs more than retaining present ones. That is why companies, analysts, and researchers are center on analyzing the dynamics behind customer churn behaviors. In this study, we present a comparative study for the purpose of forecasting customer churn employing publicly available datasets, namely, IBM Watson and Call-Detailed Record CDR. For this purpose, logistic regression, random forest, decision tree, k-nearest neighbor, extreme gradient boosting, and naive Bayes techniques are evaluated as machine learning approaches while artificial neural networks and convolutional neural networks are assessed as deep learning models. Experiment results indicate that the random forest method exhibits superior performance with 79.94 accuracy for the IBM Watson dataset and 96.34 accuracy for the Call Detailed Report CDR dataset. To demonstrate the effectiveness of the suggested framework, a comparison with the state-of-the-art studies is performed.

Makale detayları

Forecasting Customer Churn using Machine Learning and Deep Learning Approaches

Yazarlar:

Ceren Aksoy | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Kocaeli Journal of Science and Engineering Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348822

pISSN:

2667-484X

Customer churn prediction

Random forest

Deep learning

Convolutional neural networksCustomer churn prediction

Random forest

Deep learning

Convolutional neural networks

Sayı

Makale detayları

Yazarlar: Ceren Aksoy | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı: Kocaeli University

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: Kocaeli Journal of Science and Engineering Vol.8 Issue 1, pp.60-70

Belge Tipi: Makale

Yayın Tarihi: 5/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001348822

pISSN: 2667-484X

Sayı

Yazarlar:

Ceren Aksoy | Ayhan Küçükmanisa | Zeynep Hilal Kilimci

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Kocaeli Journal of Science and Engineering Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001348822

pISSN:

2667-484X