Abstract

Customer churn remains a significant concern in the telecommunications sector, leading to reduced profits and increased customer acquisition costs. The competitive nature of the industry allows customers the freedom to switch providers easily, necessitating effective models to predict and mitigate churn. This paper aimed to develop and compare optimal hybrid models for accurately predicting customer churn within a specified timeframe and to assess how various factors influence the time until churn. To achieve this, a dataset from an Iranian telecommunications company was utilised. The methodology involved a three-stage hybrid approach: initially, customers were segmented using K-means (KM) and Agglomerative clustering (AC) techniques. Subsequently, binary classification was performed using Logistic Regression (LR), Artificial Neural Networks (ANN), and Decision Trees (DT). Finally, the Cox proportional hazard model (CoxPH) was employed to estimate hazard rates and analyse the impact of covariates on churn time. Model performance was evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, Specificity, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The experimental results demonstrated that hybrid models generally outperformed individual Cox models across most predictive performance metrics. Specifically, the K-means + Logistic Regression + Cox (KM + LR + Cox) model was identified as the best performer, achieving an Accuracy of 88.24%, Precision of 93.18%, Specificity of 63.64%, and an F1-score of 93.01%. KM with two clusters (representing churners and non-churners) was optimal for customer segmentation. Covariate analysis revealed that factors such as ‘Cluster 1’ decreased survival length, suggesting that customers in ‘Cluster 2’ are more prone to churn. This paper successfully developed an optimal three-stage hybrid model, KM + LR + Cox, which effectively predicts customer churn and identifies key factors influencing churn duration, offering valuable insights for targeted retention strategies.

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