Boosting Algorithms for Customer Reload Prediction: Optimizing Outcomes with XGBoost, AdaBoost, and CatBoost

DOI

10.2991/978-94-6463-758-8_185How to use a DOI?

Keywords

Machine Learning; Boosting Algorithms; Reload Behavior

Abstract

The telecommunications industry generates large volumes of data daily, creating significant opportunities for deeper analysis. One of the main challenges faced by companies in this sector is predicting customer reload behavior, where customers decide to top up or reload their services, which directly impacts a company’s profitability. Accurate reload prediction is therefore crucial in helping companies develop effective retention strategies. This study evaluates three machine learning algorithms based on boosting: XGBoost, AdaBoost, and CatBoost, to build a customer reload prediction model. Boosting algorithms are known for their ability to iteratively correct prediction errors, improve model accuracy, and handle complex and imbalanced data. Through experiments conducted on customer data from the telecommunications industry, the results showed that XGBoost achieved an accuracy of 90.04%, AdaBoost 87.02%, and CatBoost outperformed with an accuracy of 88.09%. These findings demonstrate that XGBoost offers the most effective solution for predicting reload behavior, while XGBoost and AdaBoost also provide solid results. This research provides valuable insights for telecommunications companies in identifying customers likely to reload, as well as designing better, data-driven retention strategies.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kusnaeni
AU  - Hartina Husain
AU  - Muhammad Rifki Nisardi
AU  - Wahyuni Ekasasmita
AU  - Nur Rahmi
PY  - 2025
DA  - 2025/07/30
TI  - Boosting Algorithms for Customer Reload Prediction: Optimizing Outcomes with XGBoost, AdaBoost, and CatBoost
BT  - Proceedings of the 9th International Conference on Accounting, Management, and Economics 2024 (ICAME 2024)
PB  - Atlantis Press
SP  - 2317
EP  - 2330
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-758-8_185
DO  - 10.2991/978-94-6463-758-8_185
ID  - 2025
ER  -