Predicting Customer Retention using XGBoost and Balancing Methods

Atallah M. AL-Shatnwai · Mohammad Faris

24 citations0/0 files storedInternational Journal of Advanced Computer Science and Applications

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Customer churn and segmentation Customer Service Quality and Loyalty Imbalanced Data Classification Techniques

Abstract

Customer retention is considered as one of the important concerns for many companies and financial institutions like banks, telecommunication service providers, investment ser-vices, insurance and retail sectors. Recent marketing indicators and metrics show that attracting and gaining new customers or subscribers is much more expensive and difficult than retaining existing ones. Therefore, losing a customer or a subscriber will negatively impact the growth and the profitability if the company. In this work, we propose a customer retention model based on one of the most powerful machine learning classifiers which is XGBoost. The latter classifier is experimented when combined wit different oversampling methods to improve its performance in the used imbalanced dataset. The experimental results show very promising results compared to other well-known classifiers.

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2008Handling class imbalance in customer churn prediction 2019Customer churn prediction in telecom using machine learning in big data platform 2015A comparison of machine learning techniques for customer churn prediction 2002Turning telecommunications call details to churn prediction: a data mining approach 2017An empirical comparison of techniques for the class imbalance problem in churn prediction 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services 2013Telecommunication subscribers' churn prediction model using machine learning 2012Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies

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2022Analysis of customer churn prediction using machine learning and deep learning algorithms 2022Customer churn prediction model: a case of the telecommunication market

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