Startup Review Mining

Review Mining Skill — Quick Reference

This skill extracts recurring customer pain and constraints from reviews and testimonials, then converts them into product bets and experiments. Treat reviews as a biased sample; triangulate before betting.

Key Distinction from software-ux-research:

• software-ux-research = UI/UX pain points only
• startup-review-mining (this skill) = ALL pain dimensions (pricing, support, integration, performance, onboarding, value gaps)

Modern Best Practices (Dec 2025):

• Start with source hygiene: sampling plan, platform skews, and fake-review defenses.
• Build a taxonomy (theme × segment × severity) before counting keywords.
• Convert insights into bets with explicit success metrics and decision rules.
• Handle customer/market data with purpose limitation, retention, and access controls.

When to Use This Skill

Invoke when users ask for:

• Pain point extraction from reviews (any source)
• Competitive weakness analysis
• Feature gap identification
• Switching trigger analysis (why customers leave competitors)
• Market opportunity discovery through customer complaints
• Review sentiment analysis across platforms
• B2B software evaluation (G2, Capterra, TrustRadius)
• B2C app analysis (App Store, Play Store)
• Community sentiment (Reddit, HN, Twitter/X)
• Support pain patterns (forums, tickets, Stack Overflow)

Quick Reference Table

Review Source Hygiene (Dec 2025)

Sampling and Bias Checklist

• Define the time window (e.g., last 6–12 months) and why.
• Sample across ratings (not only 1-star or 5-star).
• Separate segments (SMB vs enterprise; dev vs non-dev; geo; regulated vs not).
• Flag incentives and moderation (some platforms skew toward promoters).

Fake Review and Manipulation Defenses

• Look for repeated phrasing, unnatural timing bursts, and identical complaints/praise across accounts.
• Prefer “verified” signals when available; still spot-check.
• Treat suspicious clusters as weak evidence until corroborated elsewhere.

Compliance note: if you use reviews for marketing claims or comparisons, be aware of the FTC’s consumer reviews/testimonials rule (16 CFR Part 465). Source: https://www.ftc.gov/legal-library/browse/federal-register-notices/16-cfr-part-465-trade-regulation-rule-use-consumer-reviews-testimonials-final-rule

The 7 Pain Dimensions

Reviews reveal pain across 7 dimensions - not just UI/UX:

Decision Tree: Choosing Mining Approach

Review Mining Need: [What do you want to learn?]
    |
    +-- Finding pain points in a market?
    |   +-- B2B software? → G2 + Capterra + TrustRadius (resources/source-by-source-extraction.md#b2b)
    |   +-- Consumer app? → App Store + Play Store (resources/source-by-source-extraction.md#b2c)
    |   +-- Developer tool? → GitHub Issues + Stack Overflow + HN (resources/source-by-source-extraction.md#tech)
    |
    +-- Analyzing specific competitors?
    |   +-- Direct comparison? → Competitor Weakness Matrix (templates/competitor-weakness-matrix.md)
    |   +-- Why customers switch? → Switching Trigger Analysis (templates/switching-trigger-analysis.md)
    |   +-- Feature gaps? → Feature Request Aggregator (templates/feature-request-aggregator.md)
    |
    +-- Finding opportunities?
    |   +-- Quick wins (<2 weeks)? → Opportunity Template (templates/opportunity-from-reviews.md#quick-wins)
    |   +-- Medium bets (2-8 weeks)? → Opportunity Template (templates/opportunity-from-reviews.md#medium)
    |   +-- Big differentiation? → Opportunity Template (templates/opportunity-from-reviews.md#big-bets)
    |
    +-- Understanding sentiment?
    |   +-- Real-time complaints? → Twitter/X monitoring (resources/source-by-source-extraction.md#social)
    |   +-- Community opinion? → Reddit + HN (resources/source-by-source-extraction.md#community)
    |   +-- Launch feedback? → ProductHunt (resources/source-by-source-extraction.md#producthunt)
    |
    +-- Comprehensive analysis?
        +-- Full market research? → Review Mining Report (templates/review-mining-report.md)

Review Sources (Complete Guide)

Tier 1: Very High Signal (Start Here)

Tier 2: High Signal

Tier 3: Medium Signal

Navigation: Resources

Extraction Methodology

• resources/source-by-source-extraction.md - Platform-specific extraction guides for each source with search queries, filters, and export methods

Analysis Frameworks

• resources/pain-categorization-framework.md - 7-dimension pain taxonomy with severity scoring and prioritization
• resources/sentiment-analysis-patterns.md - Identifying pain vs praise, detecting intensity, and spotting trends

Competitive Intelligence

• resources/competitor-review-comparison.md - Cross-competitor analysis methodology with switching trigger identification

Opportunity Mapping

• resources/review-to-opportunity-mapping.md - Converting pain points to actionable product opportunities with ROI estimation

External References

• data/sources.json - Review platform URLs, API endpoints, and tool recommendations

Navigation: Templates

Full Reports

• templates/review-mining-report.md - Comprehensive mining report with all dimensions

Source-Specific Extraction

• templates/b2b-review-extraction.md - B2B software review extraction (G2, Capterra, TrustRadius)
• templates/b2c-review-extraction.md - Consumer app review extraction (App Store, Play Store)
• templates/community-sentiment.md - Community sentiment analysis (Reddit, HN, ProductHunt)

Competitive Analysis

• templates/competitor-weakness-matrix.md - Cross-competitor weakness comparison
• templates/switching-trigger-analysis.md - Why customers leave competitors
• templates/feature-request-aggregator.md - Unmet needs collection

Pain Analysis

• templates/pain-point-extraction-worksheet.md - Structured worksheet for 7-dimension pain extraction

Opportunity Output

• templates/opportunity-from-reviews.md - Pain → Opportunity conversion

Related Skills

• ../software-ux-research/SKILL.md - UI/UX Sibling: UI/UX-specific research (this skill goes broader)
• ../startup-idea-validation/SKILL.md - Consumer: Uses review mining data for validation scoring
• ../startup-trend-prediction/SKILL.md - Parallel: Combines with trend data for timing
• ../router-startup/SKILL.md - Orchestrator: Routes to this skill for pain discovery
• ../product-management/SKILL.md - Consumer: Uses pain points for discovery and roadmapping

Operational Workflow

Standard Mining Flow

1. SCOPE (Define Target)
   +-- Which product/market to analyze?
   +-- Which competitors to include?
   +-- Time range (last 6-12 months recommended)

2. EXTRACT (Gather Data)
   +-- B2B: G2 → Capterra → TrustRadius
   +-- B2C: App Store → Play Store
   +-- Tech: GitHub → HN → Stack Overflow
   +-- Social: Twitter/X → Reddit → LinkedIn

3. CATEGORIZE (7 Dimensions)
   +-- UI/UX Pain
   +-- Pricing Pain
   +-- Support Pain
   +-- Integration Pain
   +-- Performance Pain
   +-- Onboarding Pain
   +-- Value Pain

4. SCORE (Prioritize)
   +-- Frequency (how often mentioned)
   +-- Severity (how painful)
   +-- Addressability (can we solve it?)

5. MAP (Convert to Opportunities)
   +-- Quick Wins (<2 weeks)
   +-- Medium Bets (2-8 weeks)
   +-- Big Differentiation (8+ weeks)

6. OUTPUT (Deliverable)
   +-- Review Mining Report
   +-- Competitor Weakness Matrix
   +-- Opportunity Backlog

Integration with Validation Pipeline

USER ASKS                              SKILL FLOW
──────────────────────────────────────────────────────────────
"Find opportunities in X market"  → startup-review-mining → Pain Report
                                         ↓
"What's trending?"               → startup-trend-prediction → Timing Analysis
                                         ↓
"Should we build this?"          → startup-idea-validation → GO/NO-GO Score
                                         ↓
"What skills do we need?"        → router-startup → Implementation Path

Turning Insights Into Bets

• Convert pains → opportunities using templates/opportunity-from-reviews.md.
• Turn opportunities into decisions using:
  • ../product-management/templates/strategy/opportunity-assessment.md
  • ../startup-idea-validation/templates/validation-experiment-planner.md

Do / Avoid (Dec 2025)

Do

• Keep an audit trail (source links, sampling notes, timestamps).
• Score insights by frequency × severity × segment importance × addressability.
• Triangulate top insights via interviews, support tickets, or usage data.

Avoid

• Keyword counting without context or segmentation.
• Treating sentiment as demand without willingness-to-pay signals.
• Copying competitor feature requests without understanding the underlying job.

What Good Looks Like

• Coverage: a defined time window and segment tags (plan documented, not ad-hoc scraping).
• Taxonomy: 10–30 themes with frequency + severity, each backed by verbatim quotes and links.
• Quality: spot-check a sample of clustered/summarized outputs and log corrections.
• Actionability: top themes become hypotheses with experiments and decision thresholds.
• Compliance: respect platform terms and detect suspicious/fake-review patterns (audit trail preserved).

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

• Summarization and clustering: require spot-checks and preserve source links.
• Automated extraction: log prompts/settings and keep reproducibility notes.

Usage Notes

For Claude: When user asks to "find pain points" or "analyze reviews":

1. Ask which market/product/competitors to analyze
2. Use source-by-source-extraction.md for platform-specific queries
3. Categorize findings into 7 pain dimensions
4. Score by frequency × severity × addressability
5. Output using review-mining-report.md template

For Claude: When user asks to "find opportunities":

1. First extract pain points (above workflow)
2. Use review-to-opportunity-mapping.md to convert
3. Categorize into Quick Wins / Medium Bets / Big Differentiation
4. Feed to startup-idea-validation for scoring

Output Formats:

• Pain points: 7-dimension matrix with severity scores
• Competitor analysis: Weakness comparison with quote evidence
• Opportunities: Prioritized backlog with effort/impact estimates
• Switching triggers: Why customers leave (ranked by frequency)

Key Principle: Reviews are the voice of the customer. Mine them systematically.