"Gaddam, L. & Kadali, S. L. H. Comparison of Machine Learning Algorithms on Predicting Churn Within Music Streaming Service (2022). Karwa, S., Shetty, N. & Nakkella, B. Churn Prediction and customer retention. In Predictive Analytics and Generative AI for Data-Driven Marketing Strategies, 98113 (Chapman and Hall/CRC). Joy, U. G., Hoque, K. E., Uddin, M. N., Chowdhury, L. & Park, S. B. A big data-driven hybrid model for enhancing streaming service customer retention through churn prediction integrated with explainable AI. IEEE Access. (2024)."
"Talaat, F. M. & Aljadani, A. AI-driven churn prediction in subscription services: addressing economic metrics, data transparency, and customer interdependence. Neural Comput. Appl. 126 (2025). Singh, C., Dash, M. K., Sahu, R. & Kumar, A. Artificial intelligence in customer retention: a bibliometric analysis and future research framework. Kybernetes 53(11), 48634888 (2024). Google Scholar Gopal, P. & MohdNawi, N. B. An improved convolutional neural network for churn analysis. Int. J. Adv. Comput. Sci. Appl. 14(9) (2023)."
Comparisons of machine learning algorithms target churn prediction within music streaming services, emphasizing model selection and performance differences. Big-data hybrid models combine large-scale feature engineering with explainable AI to enhance streaming service customer retention through interpretable churn predictions. AI-driven approaches address economic metrics, data transparency, and customer interdependence in subscription services to align prediction with business impact. Bibliometric analyses map artificial intelligence research in customer retention and propose future research frameworks. Convolutional neural network improvements and composite deep learning techniques are applied to churn analysis in telecom contexts. Hybrid schemes integrate clustering and classification algorithms, while optimization algorithms tune model performance.
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