Advancements of Customer Churn in the Telecommunications and Financial Industries Based on Machine Learning

Yankai Wang

2024

Abstract

Faced with increasingly fierce market competition, customers often frequently have a variety of options when selecting items and services. The issue of customer churn has become a pressing concern for the majority of businesses, as seen by financial organizations (such as banks) and telecommunications companies. This paper provides an overview of the application of machine learning techniques in predicting customer churn in the telecommunications and financial industries. The purpose is to summarize the most advanced methods and evaluate their effectiveness in predicting customer turnover. In the telecommunications field, literature emphasizes the application of K-means clustering in customer segmentation, followed by predictive models such as XGBoost and Adaboost, which have been shown to perform well in capturing complex relationships in customer data. Similarly, in the financial field, random forests, support vector machines (SVM), and LightGBM are widely popular for their ability to handle large-scale datasets and nonlinear patterns, thereby improving the accuracy of customer churn prediction. Based on existing research, this paper discusses the challenges and improvement methods of artificial intelligence and machine learning in the field of customer churn prediction and analysis.

Paper Citation

in Harvard Style

Wang Y. (2024). Advancements of Customer Churn in the Telecommunications and Financial Industries Based on Machine Learning. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 611-616. DOI: 10.5220/0013332000004568

in Bibtex Style

@conference{ecai24,
author={Yankai Wang},
title={Advancements of Customer Churn in the Telecommunications and Financial Industries Based on Machine Learning},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={611-616},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013332000004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Advancements of Customer Churn in the Telecommunications and Financial Industries Based on Machine Learning
SN - 978-989-758-726-9
AU - Wang Y.
PY - 2024
SP - 611
EP - 616
DO - 10.5220/0013332000004568
PB - SciTePress