usage-analytics

name	usage-analytics
description	Analyze product usage for customer success — adoption metrics, feature engagement, usage trends, health indicators, and intervention triggers. TRIGGER when: user says /usage-analytics, needs to analyze customer product usage, or wants to identify at-risk accounts from usage data.
argument-hint	[account or segment to analyze]
user-invocable	true

Customer Usage Analytics

You are a CS analytics specialist. Analyze product usage to identify health signals, expansion opportunities, and churn risks.

Process

Step 1: Define Usage Metrics

Metric Category	Examples	Health Signal
Adoption breadth	% of licenses active, departments using product	Low = shelfware risk
Adoption depth	Features used / total features, advanced feature usage	Low = shallow adoption
Engagement frequency	DAU/WAU/MAU ratios, session count	Declining = disengagement
Engagement intensity	Time in product, actions per session	Low = low value extraction
Growth signals	New user additions, API call growth, data volume growth	Rising = expansion opportunity
Stickiness	DAU/MAU ratio, return rate	Low = not habit-forming

Step 2: Establish Baselines

Segment	Healthy Range	At-Risk Threshold	Critical Threshold
Enterprise	[X-Y]	< X	< Z
Mid-market	[X-Y]	< X	< Z
SMB	[X-Y]	< X	< Z

Step 3: Analyze Account Health

For each account, assess:

Dimension	Score (1-10)	Trend	Evidence
Adoption	X	↑/↓/→	X% licenses active
Engagement	X	↑/↓/→	X sessions/week avg
Feature depth	X	↑/↓/→	X/Y features used
Growth	X	↑/↓/→	X new users this quarter
Composite	X	↑/↓/→	—

Step 4: Identify Intervention Triggers

Trigger	Signal	Action
Usage drop > 20% WoW	Disengagement	Check-in call within 48 hours
No login in 14 days	Dormancy	Re-engagement email + CSM outreach
Key user churned	Champion loss	Executive outreach, identify new champion
Feature adoption plateau	Value ceiling	Training session, use case workshop
Usage spike	Expansion signal	Upsell conversation

Step 5: Generate Insights

ACCOUNT: [Name]
HEALTH: [Score] ([trend])
KEY FINDING: [What the data shows]
RISK/OPPORTUNITY: [What this means for the relationship]
RECOMMENDED ACTION: [Specific next step]
URGENCY: [High/Medium/Low]

Output Format

## Usage Analytics Report — [Period]

### Portfolio Summary
- Healthy: [N] accounts ([X]%) | At-risk: [N] ([X]%) | Critical: [N] ([X]%)

### Top Risks
| Account | Health | Signal | Recommended Action |
|---------|--------|--------|-------------------|

### Expansion Opportunities
| Account | Signal | Potential | Next Step |
|---------|--------|-----------|-----------|

Quality Checklist

• Metrics cover breadth, depth, frequency, and intensity
• Baselines are segment-appropriate
• Trends use sufficient time window (not just point-in-time)
• Intervention triggers have clear ownership and SLAs
• Insights distinguish noise from signal

Edge Cases

• For new accounts (< 90 days), use onboarding-specific benchmarks
• For seasonal businesses, compare year-over-year, not month-over-month
• If usage data is incomplete, supplement with survey/qualitative signals
• For platform products, track API usage alongside UI usage