churn-prediction

name	churn-prediction
description	Build churn prediction models — leading indicators, risk scoring, early warning systems, intervention playbooks. Triggers on: "churn prediction", "predict churn", "churn model", "early warning", "risk scoring".
license	MIT
compatibility	Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
metadata	{"version":"1.0.0","author":"LeadMagic","category":"lifecycle","tags":["lifecycle","churn-prediction","risk-scoring","retention","analytics"],"frameworks":["Gainsight Churn Prediction Model","Retention Science Framework","Reforge — Lifecycle Marketing"]}

Churn Prediction

Overview

Churn prediction shifts retention from reactive ("they cancelled — now what?") to proactive ("this account shows 3 risk signals — intervene now"). Early intervention reduces churn by 20-40% compared to waiting for cancellation. This skill covers modeling leading indicators, building risk scores, and designing intervention playbooks.

Authoritative Foundations

• Gainsight Churn Prediction Model — Named methodology governing recommendations in this skill's process.
• Retention Science Framework — Named methodology governing recommendations in this skill's process.
• Reforge — Lifecycle Marketing — Startup operating cadence — default alive, talk to users, launch fast.

When to Use

• "Build a churn prediction model"
• "Predict which accounts will churn"
• "Churn risk scoring"
• "Early warning system"
• "Retention analytics"

Lifecycle Stage

Retention (stage 6). Canonical index → references/gtm-lifecycle-stages.md.
Metrics → references/lifecycle-metrics-by-stage.md (Retention).
Scorecard → skills/analytics/gtm-metrics/templates/stage-health-scorecard.md (Retention panel).

Core Principle

The best churn signal is not "they stopped paying" — it's the behavior pattern they exhibit 60-90 days before they stop paying. Your job is to find those leading indicators and act on them while there's still time.

Step-by-Step Process

Phase 1: Identify Leading Indicators

Analyze churned customers looking back 90 days before cancellation:

Product Usage Signals:

• Login frequency declining (weekly → bi-weekly → monthly)
• Key feature usage dropping (>30% decline month-over-month)
• Time since last key action > 14 days
• Multiple users stopped logging in (if multi-seat)
• Failed imports/integrations (3+ failures = high frustration)

Engagement Signals:

• Stopped opening emails (unengaged for 30+ days)
• Support ticket sentiment turning negative
• No response to CSM outreach (2+ attempts)
• Missed QBR or success review meeting
• Stopped attending webinars/events they previously attended

Business Signals:

• Champion left the company (detected via LinkedIn)
• Company had layoffs or restructuring
• Budget cut or freeze in their department
• New competitor evaluation (detected via intent data)
• Contract renewal window approaching with no engagement

Phase 2: Build Risk Scoring Model

Weight each signal on a 0-100 risk score:

Critical (40 points max):

• Champion departure: 40 points
• Key feature usage down >50%: 35 points
• No login in 30+ days: 30 points

High (25 points max):

• Support ticket spike (3+ in 7 days): 20 points
• No response to CSM in 14+ days: 15 points
• Missed renewal meeting: 10 points

Medium (20 points max):

• Email engagement dropped to 0: 10 points
• Usage declining 20-30% MoM: 10 points
• New competitor intent signal: 5 points (cumulative)

Low (15 points max):

• Weather signal: minor usage dip, one missed meeting

Risk Tiers:

• Red (70+): Immediate executive intervention required. Cancel risk in <30 days.
• Yellow (40-69): CSM intervention this week. Cancel risk in 30-60 days.
• Green (<40): Standard monitoring.

Phase 3: Intervention Playbooks

Red Account Playbook:

• Day 0: CSM calls within 4 hours. "I noticed [specific signal]. Everything ok?"
• Day 0: Executive sponsor reaches out. "Your success is critical to us — let's solve whatever is blocking you."
• Day 1: Root cause analysis. Is it product, support, business, or competition?
• Day 3: Action plan presented to customer. Specific timeline. Named owner.
• Day 7: Check-in. "Is the plan working? What else do you need?"
• Day 14: Success review. Have the signals reversed? If not, escalate to VP CS.

Yellow Account Playbook:

• Day 0: CSM email: "Noticed [signal] — quick check-in?"
• Day 2: If no reply, call.
• Day 5: Value reinforcement: usage stats, ROI summary, new features they should try
• Day 10: If signals persist, escalate to Red playbook

Phase 4: Automated Monitoring

• Daily scan: Run risk model on all accounts every 24 hours
• Alert routing: Red → CSM + VP CS + Account Executive. Yellow → CSM only.
• Slack/email alerts: "Account ABC moved to RED risk — champion departed, usage down 60%."
• Dashboard: Real-time risk heatmap of all accounts. Sort by risk score.
• Trend tracking: Is risk score improving or worsening week-over-week?

Phase 5: Feedback Loop

• Churn post-mortem: For every churned account, review: what signals were present 90 days before churn? Why weren't they acted on? What signal did we miss?
• Model refinement: Every quarter, update signal weights based on which signals actually predicted churn vs false positives
• False positive analysis: Accounts flagged red that didn't churn — what made them different? Refine model.
• Precision vs recall balance: High precision = fewer false alarms but may miss churn. High recall = catch more churn but more false alarms. Tune based on CSM capacity.

Output Format

Churn prediction model with: leading indicator catalog, risk scoring algorithm, intervention playbooks, automated monitoring setup, and feedback loop.

Quality Check

Before delivering, verify:

• All required sections are complete
• Output matches the user's stated need
• Named frameworks are cited for key recommendations
• No vague claims — every recommendation has a specific action
• Deliverable is ready for operational use, not just conceptual

Common Pitfalls

1. Skipping research. Building output without understanding the specific context. Fix: always gather required inputs before producing deliverables.
2. Generic output. "Improve your process" without concrete steps. Fix: every recommendation must include a specific action, timeline, and owner.
3. Missing framework citations. Advice without named authorities. Fix: ground every recommendation in a cited framework from a recognized authority.

Execution Artifacts

• references/framework-notes.md — Named frameworks and reference tables
• templates/output-template.md — Deliverable shell for agent output
• scripts/check-output.py — Lightweight deliverable validator Canonical lifecycle (repo root): references/gtm-lifecycle-stages.md (Retention) · references/lifecycle-metrics-by-stage.md · skills/analytics/gtm-metrics/templates/stage-health-scorecard.md

Related Skills

• churn-prevention, cs-playbooks, onboarding-sequences, lifecycle-drips, expansion-selling