Predictive Analytics and Generative AI for Customer Churn Prediction and Proactive Retention

Anju Thomas, Tamizh Murugan T., P. Ranjana, Constance Xavier S.

2025

Abstract

Churn prediction system is the advanced analytics and machine learning based system which predicts the customer that might churn from the business. Thus, drawing leverage from information regarding transaction histories, behavioral patterns, engagement levels and user feedback, the system enables businesses to take proactive measures to retain consumers, recovering lost revenue, while ensuring the long-term sustainability of the firm. The key processes such as data preparation, feature engineering, model building and insight generation. Next step, Generative AI takes this model a step further, making them more accurate and frequent in predicting potential churners, to nip the issue in the bud.

Paper Citation

in Harvard Style

Thomas A., T. T., Ranjana P. and S. C. (2025). Predictive Analytics and Generative AI for Customer Churn Prediction and Proactive Retention. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 128-134. DOI: 10.5220/0013878900004919

in Bibtex Style

@conference{icrdicct`2525,
author={Anju Thomas and Tamizh T. and P. Ranjana and Constance S.},
title={Predictive Analytics and Generative AI for Customer Churn Prediction and Proactive Retention},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={128-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013878900004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Predictive Analytics and Generative AI for Customer Churn Prediction and Proactive Retention
SN - 978-989-758-777-1
AU - Thomas A.
AU - T. T.
AU - Ranjana P.
AU - S. C.
PY - 2025
SP - 128
EP - 134
DO - 10.5220/0013878900004919
PB - SciTePress