Analyse CSAT / NPS / CES survey results and turn the score into actions. Use when asked to analyse NPS, CSAT, or CES data, compute an NPS score, interpret su...
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Cs Health Scorecard
试用Build a customer health scorecard for a specific account. Use when asked to score account health, assess renewal risk, build a health dashboard, or evaluate...
它能做什么
Build a customer health scorecard for a specific account. Use when asked to score account health, assess renewal risk, build a health dashboard, or evaluate an account's likelihood to renew or expand. Produces a structured health scorecard with a RAG status, dimension scores, key risks, and recommended actions.
技能文档
Customer Health Scorecard Skill
Produce a structured, data-driven health scorecard for a customer account — giving the CSM and leadership a clear view of renewal risk, expansion potential, and the actions needed to move the account in the right direction.
Required Inputs
Ask for these if not already provided:
- Account name and tier (enterprise / mid-market / SMB)
- Contract value (ARR) and renewal date
- Product usage data — logins, DAU/MAU ratio, key feature adoption
- Support data — open tickets, CSAT or NPS score, recent escalations
- Engagement data — last QBR date, executive sponsor status, champion name
- Commercial data — payment history, expansion conversations, seats used vs. licensed
- Any known risks or recent changes at the account
Scoring Framework
Score each dimension 1–5. Weight as shown. Calculate weighted total out of 100.
| Dimension | Weight | What to Score |
|---|---|---|
| Product Adoption | 30% | DAU/MAU ratio, breadth of features used, power users identified |
| Engagement | 20% | QBR cadence, executive sponsor active, champion strength |
| Outcomes | 20% | Customer hitting their stated goals / success metrics |
| Support Health | 15% | Ticket volume trend, unresolved escalations, CSAT |
| Commercial | 15% | On-time payments, seats utilised, expansion signals |
Score → RAG conversion:
- 80–100: Green (healthy, renew likely)
- 60–79: Amber (at risk, needs attention)
- 0–59: Red (high churn risk, escalate)
Programmatic Helper
This skill ships with a stdlib-only Python script that applies the weights above and converts the weighted total to a RAG status — so the headline score is computed identically every time and weights always sum to 100%.
# Five scores 1-5 in order: adoption engagement outcomes support commercial
python3 scripts/health_score.py --scores 4 3 4 2 5 --account "Acme Corp"
# Or from JSON (lets you override the default weights per account/segment)
python3 scripts/health_score.py --input account.json
It returns the per-dimension weighted points, the total out of 100, and the RAG band (Green ≥80, Amber 60–79, Red <60) with a one-line next step. Run it to set the headline number, then write the dimension detail and actions below around it. Add --json for downstream tooling.
Output Format
Customer Health Scorecard: [Account Name]
CSM: [Name] | Tier: [Enterprise / Mid-Market / SMB] ARR: £/$/€[X] | Renewal date: [Date] | Days to renewal: [N] Overall health: [Green / Amber / Red] — [Score]/100 Last updated: [Date]
Health Score Summary
| Dimension | Score (1–5) | Weight | Weighted Score | Trend |
|---|---|---|---|---|
| Product Adoption | [1–5] | 30% | [X] | ↑ / → / ↓ |
| Engagement | [1–5] | 20% | [X] | ↑ / → / ↓ |
| Outcomes | [1–5] | 20% | [X] | ↑ / → / ↓ |
| Support Health | [1–5] | 15% | [X] | ↑ / → / ↓ |
| Commercial | [1–5] | 15% | [X] | ↑ / → / ↓ |
| Total | — | 100% | [X]/100 |
Dimension Detail
Product Adoption — [Score]/5
- DAU/MAU ratio: [X]% (benchmark: >25% = healthy)
- Key features adopted: [List features in use]
- Features not adopted: [List unused high-value features]
- Power users identified: [Yes / No — how many]
- Assessment: [1–2 sentences on adoption health]
Engagement — [Score]/5
- Last QBR: [Date] — [Outcome summary]
- Next QBR: [Scheduled / Overdue]
- Executive sponsor: [Active / Passive / Vacant]
- Champion: [Name, role, strength: strong / moderate / weak]
- Assessment: [1–2 sentences]
Outcomes — [Score]/5
- Customer's stated goals: [List 2–3 goals from onboarding or last QBR]
- Progress against goals: [On track / Partial / Off track]
- Evidence of value: [Metric or quote that demonstrates ROI]
- Assessment: [1–2 sentences]
Support Health — [Score]/5
- Open tickets: [N] (priority breakdown: P1: X, P2: X, P3: X)
- CSAT / NPS: [Score] (benchmark: >8 CSAT / >30 NPS = healthy)
- Unresolved escalations: [Yes / No — details if yes]
- Ticket trend (last 90 days): Increasing / Stable / Decreasing
- Assessment: [1–2 sentences]
Commercial — [Score]/5
- Seats licensed: [N] | Seats active: [N] ([X]% utilisation)
- Payment history: [On time / Late — details]
- Expansion signals: [Yes — describe / No]
- Downgrade or cancellation signals: [Yes — describe / No]
- Assessment: [1–2 sentences]
Top Risks
| Risk | Severity | Mitigation |
|---|---|---|
| [Risk description] | High / Medium / Low | [Specific action to mitigate] |
Recommended Actions
Immediate (this week):
- [Action — owner — deadline]
This month:
- [Action — owner — deadline]
Before renewal:
- [Action — owner — deadline]
Renewal Forecast
| Scenario | Probability | ARR at risk |
|---|---|---|
| Full renewal at current ARR | [X]% | £/$/€0 |
| Renewal with contraction | [X]% | £/$/€[X] |
| Churn | [X]% | £/$/€[full ARR] |
Recommended renewal play: [Expand / Hold / Save / Manage out]
Scoring Rubric (0–40)
Score any output of this skill before handing it over; 32+ is ship-quality.
| Dimension | 0 | 5 | 10 |
|---|---|---|---|
| Score integrity | Weighted total doesn't compute from the dimension scores and stated weights, or the RAG band contradicts the total | Arithmetic is correct but weights were adjusted silently, or the headline RAG smooths over a dimension that tells a different story | Total computes exactly (score × weight on the 1–5 scale, out of 100), RAG matches the 80/60 bands, and any dimension that contradicts the overall status is called out rather than averaged away |
| Evidence per dimension | Dimension scores asserted with no supporting data ("engagement feels weak") | Most dimensions cite data, but at least one score leans on gut feel or a stale data point presented as current | Every dimension score is anchored to named, dated evidence (usage figures, ticket counts, QBR dates, seat utilisation) and benchmarks are applied where the format provides them |
| Risk specificity | Risks are labels ("low engagement", "churn risk") with no people, dates, or dollar amounts | Risks are real but partially vague — severity assigned without a mitigation, or mitigations without owners | Every risk names the person/event/amount involved ("champion departs 25 July, no successor"), carries a severity, and has a mitigation someone could start this week |
| Renewal calibration | Forecast missing, probabilities don't sum to 100%, or the recommended play ignores the score | Forecast present and sums correctly, but ARR-at-risk figures don't reconcile to contract line items, or the play is generic | Probabilities sum to 100%, ARR at risk maps to actual contract components, the play (Expand/Hold/Save/Manage out) follows from the score and risks, and actions are owned, dated, and sequenced against the renewal date |
Quality Checks
- Score is based on data, not gut feel — each dimension has evidence
- Risks are specific (not "low engagement" — something like "executive sponsor left in March, no replacement identified")
- Actions have owners and deadlines
- Renewal probability is calibrated against pipeline reality
- Trend arrows reflect direction of change vs. last scorecard, not just current state
Anti-Patterns
- Do not score health dimensions on gut feel — every score needs specific supporting evidence
- Do not give a Green status to accounts with unresolved P1 issues or missed milestones
- Do not list risks vaguely — "low engagement" without specifics is not actionable
- Do not leave recommended actions without named owners and deadlines
- Do not conflate product usage frequency with product value delivery
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