Prediction of Feature Importance for YouTube Premium Cancellation Using Artificial Neural Network and Ensemble Learning Algorithms
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Abstract
This research aims to develop a high-performance predictive model for subscriber churn in the YouTube Premium service and to identify key determinants influencing user decisions, providing a basis for proactive customer retention strategies. Utilizing a dataset of 10,983 records, the study integrates Artificial Neural Networks (ANN) with ensemble learning algorithms, specifically XGBoost, LightGBM, and CatBoost. The methodology incorporates Optuna for hyperparameter optimization and an iterative feature selection framework combined with SHAP (SHapley Additive exPlanations) for model interpretability. The results demonstrate that the optimized LightGBM model, adjusted for class imbalance, achieves superior performance, with an Area Under the Curve (AUC) of 0.93, an accuracy of 0.85, and a recall of 0.90, while utilizing only eight critical variables. The analysis reveals that behavioral factors significantly outweigh demographic variables in predicting churn. Positive indicators for retention include “ContentCount” which exerts the highest influence, followed by “Period” “WatchTime” and “Subscribed” (number of channels followed). Conversely, “CamelCase” and “SupportTickets” are identified as critical negative indicators that significantly increase churn risk. Additionally, “Contract” type and “TechSupport” serve as supplementary factors in reducing cancellation rates. These findings not only validate the effectiveness of ensemble learning in handling complex behavioral data but also provide strategic insights for monitoring users with low engagement or transaction-related issues. Implementing these proactive measures can effectively mitigate customer loss and ensure long-term business sustainability.
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