Enhanced Framework to Predict Customer Churn Using Machine Learning

A. Pravin · Beatriz Lúcia Salvador Bizotto · Mithileysh Sathiyanarayanan · T. Prem Jacob

5 citations0/0 files storedInternational Conference on Inventive Computation Technologies (ICICT)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

The leading challenge for most businesses is customer churn; it is very hard for every business organization to maintain their customers. If customers are not satisfied with the service provided by an organization, they will switch to another organization that better meets their needs. In terms of churn prediction (CP), many research work was proposed by researchers, who have used machine learning (ML) algorithms for prediction. In Telecommunication sector, due to switchover of the customer to other organization, the service provider will face a huge loss. Churn prediction in any type of business to preserve their customers by meeting all the expectations of the customer. A framework has been designed for estimating the customer exit within the banking industry by applying Machine Learning Algorithms. H2O AutoML is used to predict the churn from the banking data. By leveraging H2O AutoML, we employed different kinds of machine learning approaches, Like Stacked Ensemble, Gradient Boosting Machines (GBM), along with Deep Learning approaches. Results demonstrate that the suggested framework attained higher predictive accuracy, surpassing the performance of individual models in metrics like RMSE, MAE, and overall accuracy rates. It will help the banking sector to retain their customers.

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References

2019Customer churn prediction in telecom using machine learning in big data platform 2008Customer churn prediction using improved balanced random forests 2018A machine learning framework for customer purchase prediction in the non-contractual setting 2024A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners 2022Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques 2023Customer churn prediction using composite deep learning technique 2024ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry 2024A framework to improve churn prediction performance in retail banking

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