foundations-retention-optimizer

Analyze post-PMF retention cohorts and churn risk for SaaS growth.

37|5|Updated Nov 18, 2025

Search Tags:#analytics #growth #retention #cohort-analysis #churn #lifetime-value

Ready to run

npx skills add https://github.com/BellaBe/lean-os --skill foundations-retention-optimizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: foundations-retention-optimizer
Source: https://github.com/BellaBe/lean-os/tree/main/.claude/skills/foundations-retention-optimizer
Command: npx skills add https://github.com/BellaBe/lean-os --skill foundations-retention-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Retention Optimizer Agent maximizes customer lifetime value by analyzing retention cohorts, predicting churn, and guiding engagement and monetization strategies after PMF.

Core Features & Use Cases

Cohort Analysis: Segment users by acquisition, behavior, and revenue to identify sticky patterns.
Churn Prediction & Intervention: Detect at-risk users and design re-engagement campaigns.
Engagement & Monetization: Define value moments and habit-forming actions to improve retention and revenue.
Use Case: Analyze cohorts from a data warehouse to prioritize win-back campaigns for high-LTV users.

Quick Start

To begin, supply your user-event dataset (e.g., data/cohorts.csv) and run a cohort retention analysis.

Frequently Asked Questions about foundations-retention-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze customer retention cohorts to identify churn patterns?▼

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.

What's the best way to predict churn risk and design win-back campaigns?▼

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.

How do I measure the impact of feature adoption on customer lifetime value?▼

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.

What data do I need to run a post-PMF retention analysis?▼

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.

Can I use cohort analysis to evaluate network effects and viral loops?▼

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.

What are the limitations of cohort retention analysis for early-stage products?▼

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.