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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Churn Customer Estimation Method based on LightGBM for Improving Sales
Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140215
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.
Abstract: Churn customer estimation method is proposed for improving sales. By analyzing the differences between customers who churn and customers who do not churn (returning), we will conduct a customer churn analysis to reduce customer churn and take steps to reduce the number of unique customers. By predicting customers who are likely to defect using decision tree models such as LightGBM, which is a machine learning method, and logistic regression, we will discover important feature values in prediction and utilize the knowledge obtained through Exploratory Data Analysis: EDA. As results for experiments, it is found that the proposed method allows estimation and prediction of churn customers as well as characteristics and behavior of churn customers. Also, it is found that the proposed method is superior to the conventional method, GradientBoostingClassifier: GBC by around 10%.
Keywords: LightGBM (light gradient boosting machine); EDA (exploratory data analysis); churn prediction; linear regression; gradient boosting method; GradientBoostingClassifier: GBC
Kohei Arai, Ikuya Fujikawa, Yusuke Nakagawa, Ryuya Momozaki and Sayuri Ogawa, “Churn Customer Estimation Method based on LightGBM for Improving Sales” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140215
@article{Arai2023,
title = {Churn Customer Estimation Method based on LightGBM for Improving Sales},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140215},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140215},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Kohei Arai and Ikuya Fujikawa and Yusuke Nakagawa and Ryuya Momozaki and Sayuri Ogawa}
}
