About

This canvas performs a comprehensive behavioral analysis of Zerve users to identify drivers of long-term success, discovering that engagement intensity and feature diversity and not AI usage or monetization, are the strongest predictors of user success. The workflow loads user event data, engineers behavioral features through sessionization and activity aggregation, trains a Random Forest classifier to rank feature importance, and generates visualizations showing that successful users demonstrate higher engagement, deeper session interactions, and broader feature exploration.