Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Design and maintain customer health scoring systems with automated alerts and trending analysis
日本語の概要は準備中です。原文の説明を表示しています。
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Build systematic customer health monitoring with composite scores, trend tracking, and automated alerting for proactive customer success.
Based on Gainsight Health Score Design and Totango Customer Success metrics, focusing on:
| Claude Does | You Decide |
|---|---|
| Designs scoring framework | Dimension weights |
| Calculates composite scores | Alert thresholds |
| Identifies trending patterns | Intervention triggers |
| Suggests monitoring cadence | Resource allocation |
| Recommends improvements | Business rule exceptions |
Design a health score monitor for my customer portfolio:
Business Context:
- Product type: [SaaS/Platform/Service]
- Contract model: [Annual/Monthly/Multi-year]
- Key value metric: [What shows customer success?]
- CSM:Account ratio: [1:X]
Available Data Points:
- Product: [List usage metrics available]
- Support: [List support metrics available]
- Financial: [List financial signals]
- Relationship: [List engagement data]
Current Challenges:
- [What's not working with current approach?]
Standard 4-Pillar Model:
| Dimension | Weight | What It Answers |
|---|---|---|
| Product | 30-40% | Are they using it? |
| Support | 15-25% | Are they happy? |
| Financial | 20-25% | Are they paying/growing? |
| Relationship | 20-25% | Are we connected? |
Adjust weights based on your business:
Product Health Metrics:
| Metric | Type | Scoring |
|---|---|---|
| DAU/MAU | Leading | % of benchmark |
| Feature adoption | Leading | % features used |
| Time in product | Leading | Minutes vs avg |
| Key feature usage | Leading | Yes/No or frequency |
| Usage trend | Leading | Up/Flat/Down |
Support Health Metrics:
| Metric | Type | Scoring |
|---|---|---|
| CSAT score | Lagging | 1-5 scale |
| Ticket volume | Leading | vs baseline |
| Escalations | Leading | Count (negative) |
| Response sentiment | Leading | Positive/Neutral/Negative |
| Time to resolution | Lagging | vs SLA |
Financial Health Metrics:
| Metric | Type | Scoring |
|---|---|---|
| Payment status | Lagging | Current/Late |
| Expansion | Leading | Pipeline/Discussion |
| Contract type | Lagging | Multi-year bonus |
| Renewal date | Context | Days remaining |
| ARR trend | Lagging | Growth/Flat/Decline |
Relationship Health Metrics:
| Metric | Type | Scoring |
|---|---|---|
| Champion engaged | Leading | Active/Passive/None |
| Exec sponsor | Leading | Yes/No |
| NPS/CSAT | Lagging | Score |
| QBR attendance | Leading | Regular/Sporadic/None |
| Last touch | Leading | Days since contact |
Example Scoring Matrix:
Product Score (0-100):
+30: DAU/MAU > 70%
+20: DAU/MAU 50-70%
+10: DAU/MAU 30-50%
+0: DAU/MAU < 30%
+25: Feature adoption > 60%
+15: Feature adoption 40-60%
+5: Feature adoption < 40%
+20: Usage trending up
+10: Usage stable
-10: Usage trending down
+15: Key feature used weekly
+5: Key feature used monthly
-10: Key feature abandoned
Max: 100 (or normalize)
| Condition | Alert Level | Action |
|---|---|---|
| Score drops >15 pts | 🔴 Critical | Immediate CSM review |
| Score below 40 | 🟠 High | Intervention required |
| Score 40-60 | 🟡 Medium | Proactive outreach |
| Renewal <60 days + <60 score | 🔴 Critical | Save plan |
| Usage drop >30% WoW | 🟡 Medium | Check-in call |
| Activity | Frequency | Owner |
|---|---|---|
| Score refresh | Daily/Weekly | Automated |
| Alert review | Daily | CSM |
| Portfolio review | Weekly | CS Manager |
| Model calibration | Quarterly | CS Ops |
Input:
Design health score for our B2B SaaS platform:
Business Context:
- Product: Project management software
- Contract: Annual, avg $50K
- Value metric: Projects completed, team adoption
- CSM ratio: 1:40
Available Data:
- Product: Logins, projects created, tasks completed, integrations used
- Support: Zendesk tickets, CSAT, NPS
- Financial: ARR, payment status, expansion pipeline
- Relationship: Meeting attendance, email engagement
Challenges:
- Too many "yellow" accounts, can't prioritize
- Alerts are noisy, CSMs ignoring them
Output:
## Health Score Framework: Project Management SaaS
### Design Principles
Given 1:40 ratio and "alert fatigue" issue:
1. **Prioritize leading indicators** - Catch issues early
2. **Reduce noise** - Tighter alert thresholds
3. **Enable triage** - Clear Red/Yellow/Green actions
---
### Scoring Model
#### Dimension Weights
| Dimension | Weight | Rationale |
|-----------|--------|-----------|
| Product | 40% | Usage = value realization |
| Support | 15% | Lower touch model |
| Financial | 25% | Contract health critical |
| Relationship | 20% | Moderate touch needed |
---
#### Product Score (40 points max)
| Metric | Criteria | Points |
|--------|----------|--------|
| **Weekly Active Users** | | |
| | >70% of seats | 12 |
| | 50-70% | 8 |
| | 30-50% | 4 |
| | <30% | 0 |
| **Projects Created (monthly)** | | |
| | >5 per user | 10 |
| | 2-5 per user | 6 |
| | <2 per user | 2 |
| **Tasks Completed** | | |
| | >80% completion rate | 8 |
| | 50-80% | 5 |
| | <50% | 0 |
| **Integrations** | | |
| | 2+ active | 6 |
| | 1 active | 3 |
| | None | 0 |
| **Usage Trend** | | |
| | Increasing | 4 |
| | Stable | 2 |
| | Declining | -4 |
---
#### Support Score (15 points max)
| Metric | Criteria | Points |
|--------|----------|--------|
| **CSAT** | | |
| | >4.5 | 6 |
| | 4.0-4.5 | 4 |
| | 3.5-4.0 | 2 |
| | <3.5 | 0 |
| **Ticket Volume** | | |
| | Below baseline | 5 |
| | At baseline | 3 |
| | 2x+ baseline | 0 |
| **Escalations** | | |
| | None (90 days) | 4 |
| | Resolved | 2 |
| | Open | -4 |
---
#### Financial Score (25 points max)
| Metric | Criteria | Points |
|--------|----------|--------|
| **Payment Status** | | |
| | Current | 10 |
| | 30 days late | 5 |
| | 60+ days | 0 |
| **Expansion** | | |
| | Closed this year | 8 |
| | In discussion | 5 |
| | No opportunity | 2 |
| **Contract Type** | | |
| | Multi-year | 7 |
| | Annual | 4 |
| | Month-to-month | 0 |
---
#### Relationship Score (20 points max)
| Metric | Criteria | Points |
|--------|----------|--------|
| **Champion Status** | | |
| | Active advocate | 8 |
| | Engaged | 5 |
| | Passive | 2 |
| | Gone/None | 0 |
| **NPS** | | |
| | 9-10 (Promoter) | 6 |
| | 7-8 (Passive) | 4 |
| | 0-6 (Detractor) | 0 |
| **Last Touch** | | |
| | <30 days | 6 |
| | 30-60 days | 4 |
| | 60-90 days | 2 |
| | >90 days | 0 |
---
### Health Bands
| Score | Status | Count Target | CSM Action |
|-------|--------|--------------|------------|
| 80-100 | 🟢 Healthy | 60% | Quarterly touch, expansion |
| 60-79 | 🟡 Monitor | 25% | Monthly touch, watch trends |
| 40-59 | 🟠 At Risk | 12% | Bi-weekly, intervention plan |
| 0-39 | 🔴 Critical | 3% | Weekly, executive escalation |
**Target Distribution** at 1:40 ratio:
- 24 Healthy (quarterly = 8 touches/month)
- 10 Monitor (monthly = 10 touches)
- 5 At Risk (bi-weekly = 10 touches)
- 1 Critical (weekly = 4 touches)
- **Total: 32 touch points/month** (manageable)
---
### Alert Rules (Noise Reduction)
**Only Alert When:**
| Alert | Condition | Frequency |
|-------|-----------|-----------|
| 🔴 Critical New | Score drops below 40 | Immediate |
| 🔴 Renewal Risk | Score <60 AND renewal <60 days | Immediate |
| 🟠 Score Drop | Drops >15 points in 7 days | Daily digest |
| 🟡 Watch | Usage down >30% WoW | Weekly digest |
**Suppress Alerts For:**
- Accounts <30 days old (onboarding)
- Accounts in active implementation
- Known seasonal low periods
- Manual "snooze" by CSM
---
### Dashboard Views
**Portfolio Summary:**
┌─────────────────────────────────────┐ │ My Portfolio: 40 accounts │ ├─────────────────────────────────────┤ │ 🟢 24 (60%) │ 🟡 10 (25%) │ │ 🟠 5 (12%) │ 🔴 1 (3%) │ ├─────────────────────────────────────┤ │ ⚠️ 3 accounts dropped this week │ │ 📈 5 accounts improved │ └─────────────────────────────────────┘
**Account Drill-Down:**
┌─────────────────────────────────────┐ │ Account: TechCorp | Score: 62 🟡 │ ├─────────────────────────────────────┤ │ Product: 28/40 ████████░░ │ │ Support: 12/15 █████████░ │ │ Financial: 14/25 ██████░░░░ │ │ Relationship: 8/20 ████░░░░░░ │ ├─────────────────────────────────────┤ │ Trend: ↘️ -8 pts (30 days) │ │ Renewal: 90 days │ │ Action: Schedule check-in │ └─────────────────────────────────────┘
Input:
Analyze health trends for my portfolio:
Q4 Scores:
- Oct: 23 Green, 12 Yellow, 4 Orange, 1 Red
- Nov: 20 Green, 14 Yellow, 5 Orange, 1 Red
- Dec: 18 Green, 13 Yellow, 7 Orange, 2 Red
Also:
- 3 accounts churned in Q4
- 2 expansions closed
- NPS survey: 42 average (was 48)
Output:
## Q4 Health Trend Analysis
### Portfolio Drift Summary
| Status | Oct | Nov | Dec | Change |
|--------|-----|-----|-----|--------|
| 🟢 Green | 23 (58%) | 20 (50%) | 18 (45%) | ↘️ -13% |
| 🟡 Yellow | 12 (30%) | 14 (35%) | 13 (33%) | → +3% |
| 🟠 Orange | 4 (10%) | 5 (13%) | 7 (18%) | ↗️ +8% |
| 🔴 Red | 1 (2%) | 1 (2%) | 2 (5%) | ↗️ +3% |
**Trend: ⚠️ Deteriorating**
- 5 accounts moved from Green to lower status
- Orange/Red grew from 12% to 23%
- Portfolio health declining month-over-month
---
### Churn Correlation
| Churned Account | Last Score | Days at Red |
|-----------------|------------|-------------|
| Account A | 28 | 45 days |
| Account B | 35 | 30 days |
| Account C | 41 | 22 days |
**Insight**: All churned accounts were Red/Orange for 20+ days
**Action**: Accounts at Orange >14 days need intervention
---
### Key Drivers of Decline
**Analyzing accounts that dropped:**
| Factor | Accounts Affected | Avg Point Drop |
|--------|-------------------|----------------|
| Usage decline | 8 | -12 pts |
| Champion change | 3 | -18 pts |
| Support issues | 4 | -8 pts |
| Payment delays | 2 | -6 pts |
**Primary Driver**: Usage decline (likely seasonal + holiday)
---
### NPS Correlation
| NPS Segment | Avg Health Score | Q4 Change |
|-------------|------------------|-----------|
| Promoters (9-10) | 78 | -3 |
| Passives (7-8) | 58 | -6 |
| Detractors (0-6) | 38 | -10 |
**Insight**: Detractor scores dropping fastest
**Action**: Prioritize intervention for Detractors
---
### Q1 Recommendations
**Immediate (Week 1):**
1. Save plan for 2 Red accounts
2. Intervention for 7 Orange accounts
3. Outreach to 3 champion-change accounts
**Short-term (Month 1):**
1. Re-engagement campaign for low-usage accounts
2. Proactive support reach-out to ticket-heavy accounts
3. NPS follow-up calls with Detractors
**Strategic (Quarter):**
1. Investigate seasonal patterns (plan for Q4 2026)
2. Champion backup program implementation
3. Revisit Orange threshold (too many?)
---
### Target for Q1
| Status | Dec | Q1 Target | Delta |
|--------|-----|-----------|-------|
| 🟢 Green | 18 | 22 | +4 |
| 🟡 Yellow | 13 | 14 | +1 |
| 🟠 Orange | 7 | 3 | -4 |
| 🔴 Red | 2 | 1 | -1 |
**Success = Move 5 accounts up at least one tier**
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
日本語の概要は準備中です。原文の説明を表示しています。
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
日本語の概要は準備中です。原文の説明を表示しています。
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
日本語の概要は準備中です。原文の説明を表示しています。
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
日本語の概要は準備中です。原文の説明を表示しています。
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
日本語の概要は準備中です。原文の説明を表示しています。