Context Slice Adoption Analysis Types
Usage Trend Analysis
Track how engagement changes over time:
- Daily/weekly/monthly active users
- Feature adoption curves
- Engagement depth (sessions, actions per session)
Churn Risk Detection
Identify users likely to leave:
- Declining activity patterns
- Feature disengagement
- Support ticket correlation
Segment Comparison
Compare behavior across user groups:
- Plan tier differences
- Company size patterns
- User role variations
Adoption Analysis Output Template
# Adoption Analysis: [Feature/Product]
## Overview
- **Analysis period:** [Date range]
- **Users analyzed:** [N]
- **Key finding:** [One sentence summary]
## Usage Trends
### Overall Trajectory
[Description of whether usage is growing, flat, or declining]
| Period | Active Users | Change | Sessions | Actions/Session |
|--------|--------------|--------|----------|-----------------|
| [Period 1] | [N] | — | [N] | [N] |
| [Period 2] | [N] | [+/-X%] | [N] | [N] |
| [Period 3] | [N] | [+/-X%] | [N] | [N] |
### Feature Adoption
| Feature | Adoption Rate | Trend | Notes |
|---------|---------------|-------|-------|
| [Feature 1] | [X%] | [↑/↓/→] | [Context] |
| [Feature 2] | [X%] | [↑/↓/→] | [Context] |
| [Feature 3] | [X%] | [↑/↓/→] | [Context] |
## Segment Analysis
### By [Segment Dimension]
| Segment | Users | Adoption | Engagement | Trend |
|---------|-------|----------|------------|-------|
| [Segment 1] | [N] | [X%] | [H/M/L] | [↑/↓/→] |
| [Segment 2] | [N] | [X%] | [H/M/L] | [↑/↓/→] |
| [Segment 3] | [N] | [X%] | [H/M/L] | [↑/↓/→] |
**Key differences:**
- [Notable difference 1]
- [Notable difference 2]
## Risk Signals
### Users at Risk
| Risk Level | Count | % of Base | Primary Signal |
|------------|-------|-----------|----------------|
| High | [N] | [X%] | [What indicates risk] |
| Medium | [N] | [X%] | [What indicates risk] |
### Risk Patterns Identified
1. **[Pattern 1]:** [Description and frequency]
2. **[Pattern 2]:** [Description and frequency]
### Healthy User Profile
Users with strong adoption typically:
- [Behavior 1]
- [Behavior 2]
- [Behavior 3]
## Recommendations
### Retention Actions
1. **[Action]** — Target: [who], Expected impact: [what]
2. **[Action]** — Target: [who], Expected impact: [what]
### Product Improvements
1. **[Improvement]** — Based on: [evidence]
2. **[Improvement]** — Based on: [evidence]
### Further Investigation
1. **[Question]** — Why: [context]
## Data Notes
- **Sample:** [Any filtering or selection criteria]
- **Limitations:** [What the data can't tell us]
- **Confidence:** [How much to trust these findings]Risk Signal Framework
Declining Engagement
| Signal | Definition | Risk Level |
|---|---|---|
| Frequency drop | 50%+ reduction in login frequency | High |
| Feature abandonment | Stopped using core feature | High |
| Session shortening | Sessions 50% shorter than baseline | Medium |
| Shallow engagement | Views but no actions | Medium |
Behavioral Patterns
| Pattern | Implication |
|---|---|
| Setup incomplete | Never fully onboarded |
| Single feature use | Missing product value |
| Support surge | Struggling with product |
| Admin-only access | Team not adopted |
Segment Comparison Tips
- Control for tenure — New users behave differently than veterans
- Watch for Simpson's paradox — Overall trends can hide segment differences
- Define segments meaningfully — Based on behavior or business value, not just demographics
- Compare apples to apples — Same time period, same product version