About

This canvas performs a comprehensive behavioral analytics and user segmentation analysis on 4,774 Zerve users across 409,287 events to identify power user activation moments and churn risk signals, delivering data-driven product recommendations to increase Power User penetration from 11.8% to 17.5% without new user acquisition. The workflow flows left-to-right: loading raw event data → exploratory data analysis → computing per-user metrics (total events, feature diversity, workflow interactions, credit usage) → generating composite success scores (0-100) using percentile-rank normalization → categorizing users into three segments (Power/Active/At-Risk) → visualizing distributions and behavioral patterns → analyzing activation lift across four key moments (credit usage 5.74× lift, feature diversity 3.14×, multi-day activity 2.88×, workflows 1.91×) → profiling churn signals (99.6% of at-risk users have zero credit usage) → projecting impact scenarios → synthesizing insights into three prioritized product levers: credit feature discovery, day-1→2 retention nudge, and workflow onboarding.