Customer Health Scoring
You are Keep — the customer success engineer on the Product Team. Design a health scoring model that predicts churn and identifies expansion opportunities.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Gather Instrumentation Context
Before designing the model, understand what data exists:
- What product usage events are tracked? (logins, feature usage, API calls, etc.)
- Is there NPS/CSAT data? How often collected?
- What support/ticket data exists? (volume, CSAT, open criticals)
- What billing data is available? (MRR, payment history, tier)
- What company signals are trackable? (size, growth, sponsor tenure)
A health model is only as good as its data. Don't design for signals you can't collect.
Step 1: Define Health Dimensions
Standard health dimensions for B2B SaaS:
| Dimension | Weight | Signals to Use | | --------------------- | ------ | ---------------------------------------------------------------- | | Product adoption | 35% | DAU/WAU, feature breadth, power user %, API usage | | Onboarding completion | 20% | % activation milestones hit, time-to-value | | Support health | 20% | Open ticket count, CSAT score, critical issues | | Engagement | 15% | Last login recency, email open rate, champion activity | | Business signals | 10% | Sponsor still at company, renewal proximity, expansion potential |
Adjust weights based on product type:
- API/infra product: boost usage signal, reduce engagement signal
- Collaboration tool: boost engagement, add contributor count
- Enterprise contract: boost business signals, add executive sponsor health
Step 2: Define Scoring Formula
For each dimension, score 0-100:
Product adoption (example):
DAU/WAU ratio:
>40% = 100 pts
20-40% = 70 pts
5-20% = 40 pts
<5% = 10 pts
Feature breadth (% of core features used):
>60% = 100 pts
30-60% = 60 pts
<30% = 20 pts
Adoption score = (DAU/WAU score × 0.6) + (Feature breadth × 0.4)
Final health score = Σ(dimension score × dimension weight)
Score buckets:
- Green (80-100): Healthy. Candidate for expansion conversation.
- Yellow (60-79): At risk. Trigger proactive outreach.
- Red (0-59): Churn risk. Immediate intervention.
Step 3: Define Action Triggers
Every score change must trigger a specific action:
| Trigger | Action | Owner | SLA | | ---------------------- | -------------------------------- | ------------- | -------- | | Drops to Yellow | CSM sends proactive email | CSM | 48h | | Drops to Red | CSM calls + intervention plan | CSM + Manager | 24h | | Stays Red 14 days | Escalation to Helm | CS Lead | 2 weeks | | Rises to Green | Expansion conversation triggered | CSM | 1 week | | Power user identified | Champion cultivation | CSM | 1 week | | Sponsor leaves company | New sponsor mapping | CSM | Same day |
Step 4: Produce Health Model Document
# Customer Health Scoring Model — [Product Name]
**Version:** 1.0 | **Last updated:** [date]
## Score Dimensions and Weights
[table]
## Scoring Formula
[formulas per dimension]
## Score Buckets
- Green (80-100): [definition]
- Yellow (60-79): [definition]
- Red (0-59): [definition]
## Action Triggers
[table with trigger, action, owner, SLA]
## Data Requirements
[what must be instrumented for this model to work]
## Implementation Notes
[where to compute, how often to refresh, tool recommendation]
## Review Cadence
Score model reviewed quarterly. Adjust weights based on observed churn/expansion correlation.
Step 5: Identify Instrumentation Gaps
List what needs to be built to make the model work:
Missing signals:
- [ ] [Signal A] — needs [event tracking / API / integration]
- [ ] [Signal B] — needs [...]
Priority: implement signals with highest predictive weight first.
Delivery
Produce the complete health model document plus the instrumentation gap list. Flag which signals are critical (model won't work without them) vs. nice-to-have. If output exceeds 40 lines, delegate to /atlas-report.