Abstract: Advanced wireless technology and smart mobile devices allow users to watch Internet video from almost anywhere. The major VOD platforms are competing with each other for customers, slowly shifting from a "product-centric" strategic goal to a "customer-centric" one. At present, existing research is limited to platform business model and development strategy as well as user behavior research, but there is less research on customer retention prediction. In order to effectively solve the customer retention prediction problem, this study applies machine learning methods to video-on-demand platform customer retention prediction, improves the traditional RFM model to establish the RFLH theoretical model for video-on-demand platform customer retention prediction, and uses machine learning methods to predict the number of customer retention days. The Optuna algorithm is used to determine the model hyperparameters, and the SHAP framework is integrated to analyze the important factors affecting customer retention. The experimental results show that the comprehensive performance of the LightGBM model is better than other models. The total number of user logins in the past week, the length of video playback in the same day, and the time difference between the last login and the present are important features that affect customer retention prediction. This study can help companies develop effective customer management strategies to maximize potential customer acquisition and existing customer retention for maximum market advantage.

Quansheng Zhao, Zhijie Zhao, Liu Yang, Lan Hong and Wu Han, “Research on Customer Retention Prediction Model of VOD Platform Based on Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140427

@article{Zhao2023,
title = {Research on Customer Retention Prediction Model of VOD Platform Based on Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140427},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140427},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Quansheng Zhao and Zhijie Zhao and Liu Yang and Lan Hong and Wu Han}
}