| name | codexkit-churn-risk-analyzer |
| description | Build customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes. |
| version | 1.0.0 |
| category | data |
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
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
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
