foundations-retention-optimizer

Cohort analysis segments users by acquisition date or behavior to track retention curves over time. Load your user-event dataset and group by cohort to reveal which segments have sticky engagement patterns and which face early churn, enabling targeted intervention.

Churn prediction identifies at-risk users through behavioral signals and engagement decline. Once flagged, segment high-LTV users separately to prioritize re-engagement campaigns that recover revenue from your most valuable cohorts.

Feature adoption analysis evaluates how specific actions or feature usage correlate with retention and revenue. Segment cohorts by adoption patterns and compare lifetime value across adopters and non-adopters to isolate value-driving moments.

Post-PMF retention analysis requires user-event data with timestamps, cohort identifiers, and revenue signals. Standard inputs include CSV exports from your data warehouse with user IDs, event dates, engagement actions, and monetization events.

Yes. Network effects analysis within cohorts reveals whether referral behavior, collaboration features, or peer engagement drive retention and LTV differences. Segment users by network activity to isolate the impact of viral mechanics on cohort stickiness.

Cohort analysis requires sufficient historical user data and monetization events to detect meaningful patterns. Pre-PMF products with limited transaction history or unstable retention curves may lack signal; this approach is optimized for post-PMF SaaS and product-led growth contexts.