About

This canvas is a comprehensive behavioral analytics and predictive modeling workflow that identifies the user behaviors and workflows predicting long-term success on Zerve through interpretable machine learning. The workflow begins by profiling raw event data (structure, events, lifecycle phases), defines a rigorous success metric combining 30-day retention with workflow maturity, engineers 40+ interpretable behavioral features across five categories (early behavior, execution patterns, workflow depth, agent usage, temporal consistency), trains three complementary models (Logistic Regression, Random Forest, Gradient Boosting—all achieving >99% ROC-AUC) to identify top predictors, analyzes counter-intuitive behavioral patterns, and culminates in actionable product recommendations highlighting AI agent intensity (13.8x driver), session frequency/habit formation (55x driver), and collaboration (56x driver) as critical success factors.