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This canvas performs a comprehensive behavioral analytics study of Zerve platform users, identifying what drives successful long-term adoption through a multi-pillar success definition, predictive modeling, and deep segmentation analysis. The workflow sequences from data loading and EDA through feature engineering and success labeling, then branches into parallel predictive modeling and behavioral insights pipelines, both generating actionable visualizations on user segments, event-type lift, and model performance.