Customer Churn Behaviour Analysis Using Optimized XG-Boost Algorithm with Novel Hyperband Algorithm

Abstract

The quick development of technology infrastructure has changed business procedures. Customer churning has become a major concern and threat to all companies as consumers have more options for goods and services. Primary contribution is the creation of a framework for predicting customer churn that helps businesses recognize those customers who are likely to experience turnover. A key component of customer retention tactics is customer churn forecasting, which enables companies to spot at-risk clients and take preventative action to lower churn rates. The suggested model offers a novel approach to customer forecasting churn by utilizing Hyperband cross-validation and the well-known gradient boosting technique XG-Boost to achieve effective hyperparameter tuning. The proposed model utilizes historical customer data, including demographic information, transactional records, and customer interactions, as input features. Because XG-Boost can handle intricate interactions and identify non-linear patterns in the data, it is used as the base classifier. By utilizing feature importance scores from XG-Boost, the model gains valuable insights into the significance of different features in predicting customer Churn. To enhance the performance of the model, Hyperband cross-validation is adopted for hyperparameter optimization. Hyperband adaptively allocates computational resources to different hyperparameter configurations, efficiently balancing exploration and exploitation. This ensures that the model converges to optimal hyperparameter settings within a limited computational Budget. The proposed model is evaluated on a real-world dataset from a telecommunications company, containing a diverse set of customers and labelled churn outcomes. Performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC), are used to assess the model's effectiveness in predicting customer churn. The results demonstrate that the proposed XG-Boost model with Hyperband cross-validation outperforms traditional hyperparameter optimization methods, achieving higher accuracy and better generalization. The model's interpretability is enhanced through feature importance analysis, allowing businesses to understand the key drivers of churn and tailor their retention strategies accordingly.