AI-driven Streaming Customer Churn Prediction and Management Research

Authors

  • Jialu Yan

DOI:

https://doi.org/10.54097/6vyn3t22

Keywords:

AI driven; Customer churn prediction; Streaming media management; Machine learning; Personalized intervention

Abstract

Against the backdrop of increasingly fierce competition in the streaming media field, how to prevent customer churn has become a severe test faced by major operating platforms. The use of artificial intelligence technology, especially in-depth mining and analysis of customer behavior data, provides solid support for predicting and responding to customer churn. This article aims to study how to use AI technology to accurately identify potential customer groups that may be lost and develop targeted management strategies from the perspectives of machine learning, deep learning, natural language processing, and data mining. The article also proposes practical measures to address challenges such as data quality, model interpretability, and changes in customer behavior, with the aim of enhancing customer loyalty and promoting the refinement and efficiency of churn management.

References

[1] Xu, Qing, Yu, Bing, Zhou, Peimin, et al. "Research on the Construction of Interpretable Prediction Model for Lower Limb Deep Venous Thrombosis in Patients Undergoing Total Hip Arthroplasty Based on Machine Learning and SHAP." China Hospital Statistics 13.1 (2024): 31.

[2] Wang, Wenjuan. "Design of Health Assessment and Intervention System Based on Artificial Intelligence and Machine Learning." Electronic Technology 2023 (9): 356-357.

[3] Wu, Linjing, Ma, Xinqian, Liu, Qingtang, et al. "Big data supported MOOC forum teacher intervention prediction and application." Research on Electronic Education 42.7 (2021): 7.

[4] Zhou, Jianliang, Hu, Feixiang, Xing, Yandong, et al. "Identification of unsafe behaviors among construction workers based on personality traits and machine learning classification algorithms." Science, Technology and Engineering 22.29 (2022): 13013-13020.

[5] Liu, Haihong, Zhang, Xiaolei, Xue, Ru, et al. "Analysis of influencing factors on cognitive function status of elderly people with subjective cognitive decline based on machine learning." Journal of Nursing 30.23 (2023): 57-62.

[6] Zhu, Xiaoshe. "Research on a machine learning based comprehensive evaluation of physical fitness testing for college students and intelligent recommendation method for exercise intervention prescriptions." Journal of Guangzhou City Vocational College 17.3 (2023): 96-100.

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Published

08-01-2025

Issue

Section

Articles

How to Cite

Yan, J. (2025). AI-driven Streaming Customer Churn Prediction and Management Research. Mathematical Modeling and Algorithm Application, 3(3), 1-4. https://doi.org/10.54097/6vyn3t22