Churn Risk Analyzer

When to Use

• During monthly or quarterly customer health reviews
• When churn rate increases and root causes are unclear
• When building a customer health scoring model for the first time
• When preparing retention plays for at-risk accounts

Procedure

Step 1 — Define Health Dimensions

Dimension	Weight	Indicators
Usage	30%	Login frequency, feature adoption, DAU/MAU ratio
Engagement	25%	Support ticket sentiment, NPS, event attendance
Support	15%	Ticket volume, escalations, resolution time
Sentiment	15%	NPS score, CSAT, qualitative feedback
Contract	15%	Time to renewal, expansion signals, payment health

Adjust weights by business model (self-serve vs enterprise).

Step 2 — Score Each Dimension

Score	Level	Criteria
5	Healthy	Strong usage, positive sentiment, expanding
4	Good	Regular usage, neutral/positive feedback
3	Moderate	Declining trends, some concerns
2	At-Risk	Significant decline, negative signals
1	Critical	Minimal engagement, escalations, churn signals

Step 3 — Calculate Health Score

Health Score = Σ (Dimension Score × Weight)

Step 4 — Segment Customers

Health Score	Segment	Action
4.0–5.0	🟢 Healthy	Expansion play, referral ask
2.5–3.9	🟡 At-Risk	Proactive outreach, value reinforcement
1.0–2.4	🔴 Red	Executive sponsor call, save plan

Step 5 — Intervention Playbooks

🟢 Healthy:

• Identify expansion opportunities (upsell, cross-sell)
• Request referral or case study
• Invite to advisory board or beta programs

🟡 At-Risk:

• Schedule CSM check-in within 48 hours
• Re-onboard on underused features
• Share success stories from similar companies
• Offer training session or office hours

🔴 Red:

• Executive sponsor call within 24 hours
• Create 30-day save plan with specific milestones
• Offer concessions if justified (credit, extended trial)
• Prepare for graceful offboarding if save fails

Inputs

Input	Required	Format
Customer list	Yes	Account names with contract data
Usage data	Yes	Login counts, feature adoption metrics
Support data	Recommended	Ticket count, CSAT, escalations
NPS/sentiment data	Recommended	Scores or qualitative feedback
Contract details	Recommended	Renewal dates, ARR, payment status

Output

## Churn Risk Report — [Period]

### Portfolio Health Summary

| Segment | Count | % of Base | ARR at Risk |
|---------|-------|-----------|-------------|
| 🟢 Healthy | 120 | 60% | — |
| 🟡 At-Risk | 55 | 27.5% | $820K |
| 🔴 Red | 25 | 12.5% | $450K |

### Top 10 At-Risk Accounts

| Account | Health Score | Top Risk Factor | ARR | Renewal | CSM Action |
|---------|-------------|-----------------|-----|---------|------------|
| Acme Corp | 2.8 | Usage ↓ 40% | $120K | 60 days | Re-onboarding |
| Beta Inc | 2.5 | NPS dropped to 4 | $85K | 90 days | Exec call |
| [etc.] | | | | | |

### Intervention Queue

| Priority | Account | Action | Owner | Deadline |
|----------|---------|--------|-------|----------|
| 1 | Acme Corp | Schedule exec sponsor call | VP CS | This week |
| 2 | Beta Inc | Feature re-onboarding | CSM | Next week |

### Churn Risk Drivers (Pareto)
1. Usage decline (40% of at-risk accounts)
2. Support escalation unresolved (25%)
3. Champion left the company (20%)
4. Contract/pricing dissatisfaction (15%)

Definition of Done

• Health dimensions defined with weights
• Each customer scored per dimension
• Weighted health score calculated
• Customers segmented into 🟢/🟡/🔴
• Intervention playbook per segment
• Top at-risk accounts listed with actions and owners
• Churn risk drivers identified (Pareto)

Examples

Prompt

We have 200 B2B customers. Churn rate spiked from 5% to 8% this quarter.
Here is our customer data: [paste usage, support, NPS data]
Build a churn risk model with health scores, segment our portfolio,
and create intervention playbooks for at-risk accounts.

Quality Criteria

• Data sources and assumptions are explicitly stated
• Calculations are reproducible from provided inputs
• Visualizations or tables have clear labels, units, and time ranges
• Caveats and confidence levels are documented for estimates

Verification (4C)

Check	Question
Correctness	Are formulas, aggregations, and statistical methods applied correctly?
Completeness	Does the analysis cover all requested metrics and time ranges?
Context-fit	Are the chosen metrics relevant to the business question being answered?
Consequence	If this data were used for a decision today, what blind spots remain?

Edge Cases

• Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
• Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
• Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.

Changelog

• v1.0.0 — Initial release