growing-nrr

SKILL.md

NRR Engine (Net Revenue Retention)

Overview

NRR is the single metric that most reliably predicts SaaS survival and valuation. This skill builds health scoring, onboarding flows, expansion triggers, churn prediction, and community retention that turn your existing customer base into your most efficient growth engine.

When to Use

  • NRR is below 110% and needs systematic improvement
  • Customer health scoring does not exist or relies on login frequency
  • Onboarding is generic and activation rates are low
  • Expansion revenue is opportunistic, not trigger-based
  • Churn is detected at renewal time instead of predicted early
  • Customer feedback is scattered with no synthesis

Don't use when: NRR is above 120% with health scoring, expansion triggers, and predictive churn models already operating.

Quick Reference

Phase Duration Output
Customer health scoring model Week 1 Health model with signals, weights, thresholds
Personalized onboarding at scale Week 2 Activation milestone map and segmented paths
Customer feedback synthesis Week 3 Monthly synthesis report with prioritized themes
Automated expansion triggers Week 3-4 Trigger definitions, conditions, and routing
Churn prediction and prevention Week 4-5 Cohort analysis, predictive signals, intervention playbooks
Community as retention moat Week 5-6 Community strategy and retention programs

Core Deliverables

  • Customer Health Scoring Model -- Multi-signal scoring with thresholds and routing rules
  • Onboarding Flow Design -- Segmented activation paths with milestone-based intervention triggers
  • Customer Feedback Synthesis -- Monthly report aggregating all sources into prioritized, revenue-weighted themes
  • Expansion Trigger Workflows -- Usage, feature, team, milestone, and stakeholder-based triggers tied to health scores
  • Churn Prediction Model -- Cohort analysis, predictive signals, intervention playbooks, and win-back campaigns
  • Community Retention Plan -- Ecosystem switching costs through templates, peer connections, and expertise investment

Common Mistakes

  • Using login frequency as health score (depth and breadth matter, not volume)
  • Running calendar-based QBRs instead of signal-based outreach
  • Treating all churn the same (poor-fit churn is healthy; Tier 1 churn is an emergency)
  • Attempting to upsell unhappy customers (fix health below 70 first)
  • Single-threading on one champion (one departure away from churn)
  • Building generic onboarding for all segments
  • Ignoring churned customers (10-15% will return with the right timing)

Integration

Feeds into: tracking-marketing-metrics, managing-marketing-ops

Refresh: Health model quarterly. Onboarding monthly. Feedback synthesis monthly. Expansion triggers monthly. Churn model quarterly. Full NRR strategy review every 6 months.

See workflow.md for detailed phase-by-phase execution, health scoring templates, onboarding frameworks, expansion triggers, churn prediction models, and community tactics.

Weekly Installs
3
GitHub Stars
2
First Seen
Feb 23, 2026
Installed on
opencode3
gemini-cli3
github-copilot3
codex3
kimi-cli3
amp3