mohitagw15856/cs-health-scorecard

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.

Reads from / Writes to the Brain

If a professional-brain (brain/) exists, ground in it instead of re-asking for what you already know:

• Read first: the account's entities/ file, its stakeholders/ (champion, economic buyer, detractors), and knowledge/. Run python3 ../professional-brain/scripts/brain_query.py ./brain "<account name>" and carry each fact's provenance tag through.
• 📥 Propose to the Brain: after producing, propose recording the health verdict + key risks to the account entities/ file, and a renewal-risk entry to decisions/ if a call is made, each provenance-tagged. Show them, get a yes, then write with ../professional-brain/scripts/brain_write.py … --commit (append-only, dry-run by default).

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]

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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 | |

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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]

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Top Risks

| Risk | Severity | Mitigation | |---|---|---| | [Risk description] | High / Medium / Low | [Specific action to mitigate] |

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Recommended Actions

Immediate (this week):

1. [Action — owner — deadline]

This month:

1. [Action — owner — deadline]

Before renewal:

1. [Action — owner — deadline]

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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]

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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