An analysis on classification models for customer churn prediction

Mouli, Kathi Chandra; Raghavendran, Ch. V.; Bharadwaj, V. Y.; Vybhavi, G. Y.; Sravani, C.; Vafaeva, Khristina Maksudovna; Deorari, Rajesh; Hussein, Laith

doi:10.1080/23311916.2024.2378877

Cogent Engineering

2024

DOI: 10.1080/23311916.2024.2378877

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An analysis on classification models for customer churn prediction

Kathi Chandra Mouli,

Ch. V. Raghavendran,

V. Y. Bharadwaj

et al.

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Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention

Farman,

Talpur,

Amjad

et al. 2024

VFAST trans. softw. eng.

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Customer attrition is especially an issue in industries such as retail, banking, and telecommunications where customer acquisition costs are significantly higher than the costs of retaining repeat customers. The customer lack of interest is now predictable through machine learning models, and deep learning has become instrumental in early intervention for retention. In order to assess the quality of churn prediction, the study tests six basic machine learning techniques: random forest, logistic regression, and the k-nearest neighbors method, as well as four deep learning techniques: long short term memory (LSTM), bidirectional LSTM, convolutional neural networks (CNN), and artificial neural networks (ANN). The performance of the model is then assessed via the evaluation matrices, including the accuracy, precision, recall, and F1-score from the customer's behavioral data after feature extraction from large datasets. The study reveals that DL models offer improved handling of the churn and non-churn customer classification and Random Forest as well as other ML models comparable accuracy. This research can conclude that LSTM and ANN models outshine in actual-world churn prediction circumstances, especially when long-term consumer behavior evaluation is required. To enhance the current outcomes of a given prediction model, this research focuses on data preprocessing and the utilization of bootstrapping, feature extraction, and the combination of multiple models. The implications of the study provide specific practical recommendations for firms to effectively manage customer churn and increase customer retention by employing data-dealing techniques.

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Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention

Farman,

Talpur,

Amjad

et al. 2024

VFAST trans. softw. eng.

View full text Add to dashboard Cite

show abstract

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An analysis on classification models for customer churn prediction

Cited by 1 publication

References 23 publications

Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention

Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention

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