v1.0.0 to v1.0.0

---

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

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