BBE-LSWCM enhances predictive accuracy by incorporating both long window historical data and short window recent activity through a hybrid ML framework. By leveraging aggregated user behavioral data over a longer historical window (e.g., last few weeks) and user activities over a short window from the recent past (e.g., current session or last hour), the model can capture a comprehensive view of user behavior.
The long window historical data provides context and trends in user behavior over an extended period, capturing patterns that may indicate potential churn or intended tasks. On the other hand, the short window recent activity captures real-time interactions and immediate signals of user intent or behavior changes. By combining these two sources of information, BBE-LSWCM can effectively balance the importance of in-session versus longer history behaviors based on underlying data dynamics.
This approach allows for more robust predictions as it considers both macro-level trends and micro-level interactions, leading to improved accuracy in identifying high-risk users for subscription cancellation or predicting intended tasks accurately.