codexkit-churn-risk-analyzer

# 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 ```markdown ## 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 ```text 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