slice icon Context Slice

Feedback Theme Analysis

What to Look For

  1. Feature requests — What do users want to do?
  2. Pain points — What frustrates them?
  3. Praise — What do they love? (protect these)
  4. Confusion — Where do they get lost?
  5. Comparisons — What alternatives do they mention?

Theme Identification Process

  1. Read through feedback to identify recurring topics
  2. Group similar feedback into themes
  3. Count frequency of each theme
  4. Identify sentiment within themes (positive, negative, neutral)
  5. Extract representative quotes

Feedback Analysis Output Template

# Feedback Analysis: [Source/Period]

## Overview
- **Total responses:** [N]
- **Time period:** [Date range]
- **Source:** [Where feedback came from]
- **Overall sentiment:** [Positive/Negative/Mixed] — [X%/Y%/Z%]

## Top Themes

### 1. [Theme Name] — [X% of feedback]
**Sentiment:** [Mostly positive/negative/mixed]

**Summary:** [2-3 sentence description of what users are saying]

**Representative quotes:**
> "[Quote 1]"
> "[Quote 2]"
> "[Quote 3]"

**Product implication:** [What this means for the product]

### 2. [Theme Name] — [X% of feedback]
...

### 3. [Theme Name] — [X% of feedback]
...

### 4. [Theme Name] — [X% of feedback]
...

## Sentiment Breakdown

| Theme | Positive | Negative | Neutral | Total |
|-------|----------|----------|---------|-------|
| [Theme 1] | [N] | [N] | [N] | [N] |
| [Theme 2] | [N] | [N] | [N] | [N] |
| [Theme 3] | [N] | [N] | [N] | [N] |
| [Theme 4] | [N] | [N] | [N] | [N] |

## Feature Requests

| Request | Frequency | Theme Connection |
|---------|-----------|------------------|
| [Request 1] | [N mentions] | [Related theme] |
| [Request 2] | [N mentions] | [Related theme] |
| [Request 3] | [N mentions] | [Related theme] |

## Competitor Mentions

| Competitor | Context | Frequency |
|------------|---------|-----------|
| [Name] | [Why mentioned] | [N] |
| [Name] | [Why mentioned] | [N] |

## Recommendations

### Address Immediately
1. **[Recommendation]** — [Rationale based on feedback]

### Monitor
1. **[Theme/issue]** — [Why it needs watching]

### Investigate Further
1. **[Question]** — [What more we need to learn]

## Methodology Notes
- [How themes were identified]
- [Any limitations in the data]
- [Confidence level in findings]

Analysis Tips

  • Look for patterns, not outliers — One angry customer ≠ a trend
  • Distinguish needs from solutions — Users say "add button" but need "faster workflow"
  • Consider who's giving feedback — Power users? New users? Churned users?
  • Time matters — Recent feedback reflects current state; old feedback may be stale
  • Sentiment isn't everything — Neutral feedback about core features = they're working