Customer Review Aggregator

Customer Review Aggregator & Analyzer

Pull reviews from multiple platforms and extract actionable insights with sentiment analysis, pain-point detection, marketing-claim extraction, and competitor comparison.

Contents

• references/sources.md - supported platforms and sentiment dimensions
• references/intake-prompts.md - scope and data-collection prompts, example use cases
• references/output-templates.md - report templates for every analysis type

Workflow

1. Define scope. Present the scope prompt from references/intake-prompts.md to capture product, platforms, competitors, analysis focus, and time period. 2. Gather review data. Offer the three data-collection methods (paste, CSV upload, URLs via WebFetch) from references/intake-prompts.md and collect the reviews. 3. Analyze sentiment. Score overall sentiment and feature-level sentiment using the dimensions in references/sources.md. 4. Identify pain points. Cluster negative feedback by theme, rank by frequency, and assign impact plus a recommendation. 5. Extract marketing claims. Derive evidence-backed claims from positive reviews, each with supporting quotes, a confidence level, and a use case. 6. Compare competitors. When competitors are provided, build the side-by-side comparison and surface weaknesses to exploit. 7. Analyze feature requests. Rank requested features by mention count, urgency, and competitor coverage. 8. Assemble the report. Populate the full analysis report and offer deliverable formats.

For all output formats and tables, see references/output-templates.md.

Intake Prompts

Scope prompt (Step 1)

Let me help you aggregate and analyze customer reviews.

Please specify:
1. Your Product/Company: [Name]
2. Platforms to Analyze:
   - [ ] G2
   - [ ] Capterra
   - [ ] Trustpilot
   - [ ] App Store
   - [ ] Google Play
   - [ ] ProductHunt
   - [ ] Reddit
   - [ ] Twitter/X

3. Competitors to Compare (optional): [Names]
4. Analysis Focus:
   - [ ] Overall sentiment trends
   - [ ] Feature-specific feedback
   - [ ] Pain point identification
   - [ ] Marketing claims extraction
   - [ ] Competitive comparison
   - [ ] Feature request analysis

5. Time Period: Last [30/60/90/180] days

Data collection prompt (Step 2)

To analyze reviews, I need access to review data. Here are the options:

Option A: Manual Copy/Paste
- Copy reviews from the platforms into a text file and I will analyze the content.

Option B: CSV Export
- Export reviews from platforms (if available) and upload the CSV files.

Option C: URLs
- Provide URLs to review pages and I will use WebFetch to analyze public reviews.

Which method works best for you?

Example use cases

Use Case 1: Competitive Review Analysis

User: "Analyze our G2 reviews vs. Competitor A and Competitor B"

Aggregate reviews for all three products, build the competitor comparison table, surface competitor weaknesses, and recommend positioning.

Output Templates

Templates for each analysis type. Populate from the gathered review data.

Pain point identification

Extract and categorize customer pain points by frequency, impact, and recommendation.

## Top Pain Points (by frequency)

### 1. Onboarding Complexity (mentioned in 34% of reviews)
- "Setup took 3 weeks with our IT team"
- "Documentation was confusing for non-technical users"
- "Wish there was better onboarding support"

**Impact**: High - affecting trial-to-paid conversion
**Recommendation**: Create interactive onboarding wizard + video tutorials

### 2. Mobile App Performance (mentioned in 28% of reviews)
- "App crashes on iOS frequently"
- "Android version is slow and buggy"
- "Can't access key features on mobile"

**Impact**: Medium - reducing daily active usage
**Recommendation**: Prioritize mobile optimization in Q2 roadmap

Marketing claims extraction

Generate evidence-backed marketing claims, each tied to supporting reviews, a confidence level, and a recommended use case.

## Marketing Claims (from 500+ reviews)

### Claim: "Saves teams 15+ hours per week"
**Supporting Reviews (78 mentions)**:
- "Cut our reporting time from 20 hours to 4 hours" - VP Marketing, TechCorp
- "Team saves 3 hours daily on data entry" - Operations Manager, FinanceHub
- "Automated workflows freed up 15 hours weekly" - CEO, StartupXYZ

**Confidence Level**: High (78 mentions, avg 15.3 hours saved)
**Use Case**: Homepage hero section, case studies

### Claim: "Industry-leading customer support"
**Supporting Reviews (156 mentions)**:
- "Best support I've experienced in 10 years" - IT Director
- "Response time under 2 hours, always helpful" - Product Manager
- "They actually listen and implement feedback" - Founder

**Confidence Level**: High (4.8/5 support rating across platforms)
**Use Case**: Pricing page, competitive battle cards

Competitor comparison

Side-by-side analysis. Mark category leaders in a dedicated "Leader" column rather than inline symbols.

## Competitor Analysis: Your Product vs. Competitor A vs. Competitor B

| Metric | Your Product | Competitor A | Competitor B | Leader |
|--------|--------------|--------------|--------------|--------|
| Overall Rating | 4.6/5 (G2) | 4.3/5 | 4.1/5 | Your Product |
| # of Reviews | 847 | 1,243 | 623 | Competitor A |
| Support Rating | 4.8/5 | 4.1/5 | 3.9/5 | Your Product |
| Ease of Use | 4.2/5 | 4.5/5 | 3.8/5 | Competitor A |
| Value for Money | 4.0/5 | 3.7/5 | 4.3/5 | Competitor B |
| Feature Breadth | 4.7/5 | 4.4/5 | 4.2/5 | Your Product |

### Your Competitive Advantages:
1. **Superior Support** - 0.7 points ahead of nearest competitor
2. **Feature Richness** - Most comprehensive feature set
3. **Growing Review Volume** - 34% increase in 6 months

### Competitor Weaknesses to Exploit:
1. Competitor A: "Expensive for small teams" (mentioned 89 times)
2. Competitor B: "Poor customer support" (mentioned 123 times)
3. Competitor A: "Difficult to customize" (mentioned 67 times)

Feature request analysis

Identify what customers want, ranked by mentions with urgency and competitor coverage.

## Top Feature Requests (from 847 reviews)

| Rank | Feature | Mentions | Urgency | Competitor Has? |
|------|---------|----------|---------|-----------------|
| 1 | API Access | 156 | High | Competitor A: yes |
| 2 | Mobile Offline Mode | 134 | Medium | None |
| 3 | Advanced Reporting | 112 | High | Competitor B: yes |
| 4 | Slack Integration | 98 | Low | Both: yes |
| 5 | Custom Dashboards | 87 | Medium | Competitor A: yes |

**Priority Recommendations**:
1. **API Access** - Table stakes feature, blocking enterprise deals
2. **Advanced Reporting** - High demand, competitive parity needed
3. **Mobile Offline Mode** - Differentiation opportunity, no competitors offer

Full analysis report

Master report template populated in Step 3 of the workflow.

## Review Analysis Report

**Analysis Period**: [Date Range]
**Total Reviews Analyzed**: [Count]
**Platforms Covered**: [List]

### Executive Summary
[2-3 sentence overview of key findings]

### Overall Sentiment
- Positive: [%]
- Neutral: [%]
- Negative: [%]
- Average Rating: [X.X/5]
- Trend: [Improving / Stable / Declining]

### Feature Sentiment Breakdown
[Table of features with sentiment scores]

### Top Pain Points
[Ranked list with frequency and impact]

### Marketing Claim Opportunities
[Evidence-backed claims with supporting quotes]

### Competitive Insights
[Comparison with competitors if provided]

### Feature Requests
[Prioritized list of customer requests]

### Recommended Actions
1. [Immediate action item]
2. [Short-term priority]
3. [Long-term strategic initiative]

Deliverable formats

Offer these output formats after the analysis is complete:

1. Executive Summary (1-page PDF) 2. Detailed Analysis Report (multi-page document) 3. Competitive Battle Card (sales enablement) 4. Product Roadmap Input (feature prioritization) 5. Marketing Claims Library (with supporting evidence) 6. Pain Point Presentation (presentation slides)

Review Sources

Supported review platforms and what each covers:

• G2 - B2B software reviews
• Capterra - business software marketplace
• Trustpilot - general business reviews
• App Store - iOS app reviews
• Google Play - Android app reviews
• ProductHunt - product launches
• Reddit - community discussions
• Twitter/X - social sentiment

Sentiment dimensions

Analyze reviews across these dimensions:

Overall sentiment

• Positive, neutral, negative distribution
• Trend analysis over time
• Rating breakdown (1-5 stars)
• NPS calculation (if applicable)

Feature-level sentiment

For each named feature, report positive vs negative mention counts, common phrases, and a 0-10 sentiment score.

Feature: "Customer Support"
- Positive mentions: 156 (78%)
- Negative mentions: 44 (22%)
- Common phrases: "quick response", "helpful team", "slow to resolve"
- Sentiment score: 7.8/10

Feature: "Pricing"
- Positive mentions: 89 (45%)
- Negative mentions: 108 (55%)
- Common phrases: "expensive", "not worth it", "good value for enterprise"
- Sentiment score: 5.2/10