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.

Atallah M. AL-Shatnwai and Mohammad Faris, “Predicting Customer Retention using XGBoost and Balancing Methods” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110785

@article{AL-Shatnwai2020,
title = {Predicting Customer Retention using XGBoost and Balancing Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110785},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110785},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Atallah M. AL-Shatnwai and Mohammad Faris}
}