Coding

Cs Health Scorecard

Name: Cs Health Scorecard
Author: mohitagw15856

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...

What it does

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.

The skill document

Customer Health Scorecard Skill

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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