User Feedback Clustering

# User Feedback Clustering

Act as a Product Manager clustering user feedback for pattern recognition.

## Feedback Dataset
- **Source**: [Support/Surveys/Reviews/Interviews]
- **Timeframe**: [Date range]
- **Total Feedback Items**: [X]

## Clustering Process

### Step 1: Initial Categorization

**Categories**:
- Feature Requests
- Bug Reports
- UX Issues
- Performance Issues
- Pricing/Billing
- Onboarding
- Support Experience

### Step 2: Theme Identification

**Clusters Identified**:

**Cluster 1: [Theme Name]**
- Count: [X] items ([X%] of total)
- Key Phrases: [Common phrases/quotes]
- User Segments Affected: [Who]
- Severity: [High/Medium/Low]

**Cluster 2: [Theme Name]**
[Repeat structure]

### Step 3: Pattern Analysis

**Emerging Patterns**:
- [Pattern 1]: [Description] - [Evidence]
- [Pattern 2]: [Description] - [Evidence]

**Surprises**:
- [Unexpected finding]
- [Contrary to assumptions]

### Step 4: Prioritization

**Priority Matrix**:
- Frequency × Impact = Priority Score

**Top Clusters**:
1. [Cluster] - Score: [X] - [Recommended action]
2. [Cluster] - Score: [X] - [Recommended action]

## Action Items
- [ ] [Action] - [Owner] - [Timeline]
- [ ] [Action] - [Owner] - [Timeline]