---
name: customer-health-score-calculator
description: "Builds a weighted customer health scoring model from product usage, support volume, NPS, and contract signals — returning a 0–100 score, a Green/Yellow/Red risk classification, the top drivers behind each score, and recommended CS plays per account. Use when scoring account health, flagging churn risk, prioritizing CSM outreach, or standardizing a health scoring methodology across a book of business."
argument-hint: "[account-metrics] [scoring-weights]"
allowed-tools: ["Read"]
---

# Customer Health Score Calculator

## Purpose
Convert raw account signals — usage, support, sentiment, and contract data — into a single weighted health score with a defensible risk tier and the specific drivers behind it, so CS teams can prioritize outreach and catch churn risk before renewal conversations begin.

## When to Use
Invoke this skill when the user asks to:
- Calculate a health score for one account or a full book of business
- Flag which customers are at risk of churn or downgrade
- Prioritize CSM outreach based on account health
- Build or standardize a health scoring methodology for the CS team
- Understand why a specific account's health is declining
- Segment accounts into Green/Yellow/Red tiers for QBR or renewal planning

## Inputs Required
Before starting, collect:
- **Account identifier(s)**: Name or ID for each account being scored (single or batch)
- **Product usage metrics**: Login frequency, active users vs. licensed seats, feature adoption breadth, usage trend over last 30/90 days
- **Support signals**: Ticket volume, ticket severity mix, average resolution time, escalations in last 90 days
- **NPS / sentiment**: Most recent NPS or CSAT score, and prior score if available (for trend)
- **Contract data**: ARR/MRR, contract start date, renewal date, tenure, plan tier, prior expansions or contractions
- **Custom weights** (optional): Desired weighting across the four categories
- **Scoring intent** (optional): Renewal prioritization vs. expansion targeting vs. general health monitoring

If custom weights are not provided, use the default weighting: Product Usage 40%, Support Signals 20%, NPS/Sentiment 20%, Contract/Relationship 20%. State the weights used at the top of the output.

## Instructions

### Step 1: Normalize Each Input to a 0–100 Sub-Score

Score every input category on a 0–100 scale before weighting. Use these rubrics:

**Product Usage (default 40%)**
- **Adoption depth** (0–100): % of purchased seats active in last 30 days. 90%+ = 100; 70–89% = 75; 50–69% = 50; 30–49% = 25; <30% = 0
- **Feature breadth** (0–100): % of core features adopted vs. plan's intended set
- **Usage trend** (0–100): Compare last-30-day usage to prior 30 days. Growing = 100; flat = 60; declining <15% = 35; declining ≥15% = 10
- Blend: adoption depth 50%, feature breadth 25%, usage trend 25%

**Support Signals (default 20%)**
- Start at 100, subtract for risk signals:
  - Ticket volume above normal baseline for the account size: −15 to −30
  - Any P1/critical escalation in last 90 days: −25
  - Average resolution time exceeding SLA: −15
  - Rising ticket trend week-over-week: −15
- Floor the sub-score at 0. Note: healthy accounts can open tickets — weight severity and escalation over raw volume.

**NPS / Sentiment (default 20%)**
- Promoter (9–10) = 100; Passive (7–8) = 60; Detractor (0–6) = 20; No recent survey = 50 (neutral, flag as data gap)
- Apply a trend modifier: score dropped vs. prior survey = −20; improved = +10 (cap at 100)

**Contract / Relationship (default 20%)**
- Tenure: >2 years = +baseline stability; new account in first 90 days = flag onboarding risk
- Renewal proximity: renewal within 90 days sharpens risk sensitivity (apply the score, don't inflate)
- Expansion history: prior upsell = +15; prior contraction = −25
- Executive sponsor / champion known and engaged = +10; champion departed or unknown = −20
- Anchor the sub-score around 70 and adjust with the modifiers above, clamped 0–100.

### Step 2: Compute the Weighted Health Score

Multiply each sub-score by its weight and sum:
`Health Score = (Usage × W₁) + (Support × W₂) + (NPS × W₃) + (Contract × W₄)`

Round to the nearest whole number. Verify weights sum to 100%.

### Step 3: Assign Risk Classification

- **Green (Healthy): 75–100** — On track. Candidate for expansion or advocacy.
- **Yellow (At Risk): 50–74** — Watch. Specific driver dragging the score; intervene before renewal.
- **Red (Critical): 0–49** — Churn risk. Requires immediate CSM action and likely escalation.

Apply override rules regardless of numeric score:
- Detractor NPS + renewal within 90 days → cap at Yellow
- Usage declining ≥15% AND champion departed → flag Red
- P1 escalation unresolved → cannot be Green

### Step 4: Identify Top Drivers and Recommended Plays

For each account:
1. List the **top 2 positive drivers** and **top 2 negative drivers** (the sub-scores moving the total most).
2. Recommend **1–3 specific CS plays** tied to the negative drivers:
   - Low adoption → targeted enablement session or in-app adoption campaign
   - Declining usage trend → executive check-in to re-establish value
   - Detractor NPS → service recovery call + closed-loop follow-up
   - Renewal within 90 days + Yellow/Red → renewal risk plan and multi-threading
   - Champion departed → new-stakeholder discovery and relationship rebuild

### Step 5: Batch Roll-Up (When Scoring Multiple Accounts)

If more than one account is provided:
- Produce a summary table sorted by risk (Red first, then Yellow, then Green)
- Show: Account, Health Score, Tier, ARR, Renewal Date, Top Negative Driver, Priority Play
- Add a portfolio line: count and total ARR in each tier, and total ARR at risk (Yellow + Red)

## Output Format
- **Structure**: Weights header → per-account scorecard (single) OR summary table + per-account scorecards (batch) → portfolio roll-up (batch only)
- **Length**: 250–400 words per account scorecard; batch summaries scale with account count
- **Format**: Per-account scorecards as structured blocks; batch roll-up as a sorted table
- **Sections**: Weighting used → Score breakdown by category (with sub-scores) → Weighted total & tier → Top drivers → Recommended plays → (batch) Portfolio roll-up
- **Tone**: RevOps analyst — quantitative, transparent about the math, decisive on classification

## Quality Checklist
Before delivering output, verify:
- [ ] Weights are stated explicitly and sum to exactly 100%
- [ ] Every category shows its 0–100 sub-score AND its weighted contribution
- [ ] The weighted total equals the sum of the four weighted contributions (arithmetic checks out)
- [ ] Risk tier matches the numeric score OR an override rule is cited by name
- [ ] Each account lists specific top drivers — not "usage is low" without the number
- [ ] Recommended plays map directly to the identified negative drivers
- [ ] Batch output is sorted Red → Yellow → Green with total ARR at risk quantified

## Notes
- Usage trend is more predictive of churn than absolute usage — a shrinking heavy user often outranks a stable light user for risk.
- No-survey NPS is scored neutral (50) but should always be flagged as a data gap; a missing signal is itself a risk indicator.
- If any category's data is entirely missing, redistribute its weight proportionally across the available categories and disclose the adjustment — never silently score a gap as zero.
- Recalibrate thresholds after one renewal cycle: if too many Greens churned, the usage weight or trend sensitivity likely needs tuning.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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