AI-Driven Integrated System for Churn Prediction and Dynamic Pricing

DOI

10.2991/978-94-6239-616-6_56How to use a DOI?

Keywords

Customer Churn Prediction; Dynamic Pricing; Machine Learning; Explainable AI (XAI); Big Data; Deep Learning; Feature Selection; Federated Learning

Abstract

Customer churn remains a significant issue across industries such as ecommerce, telecom, and subscription-based services, often leading to substantial revenue loss and reduced customer loyalty. Multiple approaches have been proposed to address churn prediction and pricing strategies. Transformer-based models, hybrid frameworks combining deep learning and boosting, graph-based attention mechanisms and statistical survival analysis have all been shown to improve prediction accuracy. Feature selection methods and hybrid interpretable models further enhance model robustness. Additionally, researchers explored geospatial churn detection, federated learning for privacy-preserving collaboration, and advanced deep learning architectures. While these methods provide strong predictive performance, they also face limitations including computational cost, dataset imbalance, lack of interpretability, and domainspecific constraints. This survey synthesizes these contributions, comparing existing methods, identifying challenges, and providing insights into their strengths and weaknesses.

Copyright

© 2026 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  - A. Ranjeeth
AU  - C. Aakshhaya
AU  - C. Nithyashrimahalakshmi
AU  - R. Abinaya
PY  - 2026
DA  - 2026/03/31
TI  - AI-Driven Integrated System for Churn Prediction and Dynamic Pricing
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 743
EP  - 756
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_56
DO  - 10.2991/978-94-6239-616-6_56
ID  - Ranjeeth2026
ER  -