feedback-analysis

name	feedback-analysis
description	Analyzes customer feedback data to identify themes, sentiment patterns, and actionable improvement priorities for product and service decisions.
allowed-tools	Read Write Glob
metadata	{"author":"matthewhitcham","version":"1.0"}

Feedback Analysis

When to Use This Skill

Use this skill when you need to:

• Analyze a collection of customer feedback to find actionable patterns
• Identify recurring themes and sentiment trends across reviews, surveys, or support tickets
• Prioritize improvements based on feedback frequency and business impact
• Create a feedback summary report for decision-making

DO NOT use this skill for collecting feedback (use satisfaction-survey or nps-survey), responding to individual reviews, or building feedback collection systems. This is for analyzing existing feedback data.

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Core Principle

CUSTOMER FEEDBACK IS NOISY — THE VALUE IS NOT IN ANY SINGLE RESPONSE BUT IN THE PATTERNS ACROSS MANY. YOUR JOB IS TO FIND THE SIGNAL IN THE NOISE AND TURN IT INTO ACTION.

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Phase 1: Gather Data

Collect and organize the feedback before analysis.

Required Inputs

Input	What to Ask	Default
Feedback source	"Where is the feedback? (surveys, reviews, support tickets, social media, emails)"	Mixed sources
Volume	"Roughly how many pieces of feedback are we analyzing?"	20-100
Time period	"What time period does this feedback cover?"	Last 90 days
Product/service	"What product or service does this feedback relate to?"	No default
Known issues	"Are there any issues you already suspect will appear?"	No default

Data Organization

## Feedback Inventory

| Source | Count | Date Range | Format |
|--------|-------|-----------|--------|
| Survey responses | [#] | [Range] | Ratings + open text |
| Online reviews | [#] | [Range] | Star rating + text |
| Support tickets | [#] | [Range] | Text |
| Social mentions | [#] | [Range] | Text |
| Direct emails | [#] | [Range] | Text |
| **Total** | **[#]** | | |

GATE: Confirm data sources and volume before starting analysis.

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Phase 2: Analyze

Identify themes, sentiment, and patterns.

Theme Identification

Group feedback into themes by tagging each piece:

## Theme Analysis

| Theme | Frequency | % of Total | Sentiment | Sample Quotes |
|-------|-----------|-----------|-----------|---------------|
| [Theme 1] | [#] mentions | [%] | Positive/Negative/Mixed | "[Quote]" |
| [Theme 2] | [#] mentions | [%] | Positive/Negative/Mixed | "[Quote]" |
| [Theme 3] | [#] mentions | [%] | Positive/Negative/Mixed | "[Quote]" |

Sentiment Breakdown

## Overall Sentiment

| Sentiment | Count | % of Total |
|-----------|-------|-----------|
| Positive | [#] | [%] |
| Neutral | [#] | [%] |
| Negative | [#] | [%] |

**Net sentiment:** [Positive % - Negative %]
**Trend vs. prior period:** Improving / Stable / Declining

Theme-Sentiment Matrix

Cross-reference themes with sentiment to find where problems and strengths cluster:

## Theme x Sentiment Matrix

| Theme | Positive | Neutral | Negative | Net |
|-------|----------|---------|----------|-----|
| Product quality | [#] | [#] | [#] | [+/-] |
| Customer support | [#] | [#] | [#] | [+/-] |
| Pricing/value | [#] | [#] | [#] | [+/-] |
| Ease of use | [#] | [#] | [#] | [+/-] |

GATE: Present analysis for review before building recommendations.

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Phase 3: Prioritize

Turn analysis into prioritized action items.

Impact-Effort Matrix

Score each theme by business impact and effort to fix:

## Improvement Priority Matrix

| Theme | Frequency | Business Impact (1-5) | Effort to Fix (1-5) | Priority Score | Action |
|-------|-----------|---------------------|--------------------|--------------:|--------|
| [Theme] | [#] | [X] | [X] | [Impact x Freq / Effort] | Fix Now / Plan / Monitor |

Action Plan

## Feedback-Driven Action Plan

### Fix Now (High impact, manageable effort)
1. **[Theme]:** [Specific action] — Owner: [Name] — Deadline: [Date]
2. **[Theme]:** [Specific action] — Owner: [Name] — Deadline: [Date]

### Plan for Next Quarter (High impact, high effort)
1. **[Theme]:** [Approach and timeline]

### Monitor (Low frequency, but watch for growth)
1. **[Theme]:** Review again in [timeframe]

### Celebrate (Positive themes to amplify)
1. **[Theme]:** Use in marketing, testimonials, and case studies

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Phase 4: Report

Deliver a clear feedback summary for stakeholders.

Executive Summary Template

## Customer Feedback Analysis — [Period]

### Key Numbers
- **Total feedback analyzed:** [#]
- **Overall sentiment:** [X]% positive, [X]% negative
- **Top positive theme:** [Theme] ([#] mentions)
- **Top negative theme:** [Theme] ([#] mentions)

### Top 3 Findings
1. [Finding with data point]
2. [Finding with data point]
3. [Finding with data point]

### Recommended Actions
1. [Action + expected impact]
2. [Action + expected impact]
3. [Action + expected impact]

### Notable Quotes
- "[Powerful customer quote — positive]"
- "[Powerful customer quote — negative]"

Close the Loop

After implementing changes, communicate back to customers:

• "You asked, we listened" email or post
• Reference specific feedback themes and what changed
• This builds trust and encourages future feedback

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Anti-Patterns

• Cherry-picking feedback — analyzing only positive or only negative responses skews the picture. Include everything.
• Acting on one loud complaint — one angry email is anecdotal. Three people saying the same thing is a pattern.
• Analysis without action — a report that sits in a folder changes nothing. Every analysis needs an action plan.
• Ignoring positive feedback — positive themes reveal your competitive advantage. Amplify them in marketing.
• Analyzing without context — 10 complaints about pricing after a price increase is expected, not alarming. Context matters.

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Recovery

• Not enough feedback to find patterns: Under 20 responses, treat each piece individually. Patterns emerge at 30+ responses.
• All feedback is positive (seems too good): Check for selection bias — are only happy customers responding? Add follow-up questions that invite constructive criticism.
• Overwhelmed by volume: Start with the most recent 30 days. Look for themes in the negative feedback first — that is where the highest-value insights live.
• Feedback contradicts itself: Segment by customer type. Power users and new users often have opposite feedback. Both are valid for their segment.
• User does not know what to do with findings: Start with the single highest-frequency negative theme. Fix that one thing. Then repeat.