Review Mining

---|-----------|-----------|-------| | ... | ... | ... | ... |

Voice of Customer Swipe File

Words users use for the problem: [list of exact phrases] Words users use for the desired outcome: [list of exact phrases] Emotional language: [frustration words, relief words]

Positioning Opportunities

• [Opportunity 1]: [what you can claim based on competitor weakness]
• [Opportunity 2]: [underserved use case you can own]

## Frameworks & Best Practices

**Where to mine by product type:**
| Product Type | Best Sources |
|-------------|-------------|
| B2B SaaS | G2, Capterra, TrustRadius |
| B2C / Consumer | Trustpilot, App Store, Play Store |
| Developer Tools | Reddit, Hacker News, GitHub Issues |
| E-commerce / DTC | Trustpilot, Amazon reviews |
| Any | Twitter/X complaints, Reddit threads |

**Review analysis principles:**
- **1-2 star reviews** reveal deal-breakers and switching triggers
- **3 star reviews** reveal "good enough but frustrated" — the most persuadable users
- **4-5 star reviews** reveal what users truly value (defend these in your product)
- **Recent reviews** (last 6-12 months) matter more than old ones
- **Verified purchase/user** reviews carry more weight

**Verbatim language is the output.** The exact words users use to describe their pain are more valuable than your summary. These become headlines, email subject lines, ad copy, and landing page copy.

**Common mistakes:**
- Only reading negative reviews (you miss what users actually value)
- Summarizing instead of quoting (you lose the authentic language)
- Treating all complaints equally (frequency x severity matters)
- Ignoring the context of who's reviewing (enterprise vs SMB, power user vs casual)
- Mining once and never returning (do this quarterly)

## Related Skills
- `competitive-analysis` — for broader competitor research beyond reviews
- `user-research-synthesis` — for synthesizing your own customer interviews
- `feedback-synthesis` — for analyzing feedback from your own users
- `cold-outreach` — use voice-of-customer language in prospecting emails

## Examples

**Prompt:** "I'm building a project management tool. What are the biggest pain points people have with Asana and Monday.com?"

**Good output includes:** Mining Trustpilot, G2, and Capterra for Asana and Monday.com, extracting the top 5-7 pain points with verbatim quotes, identifying switching triggers, and mapping them to positioning opportunities.

**Prompt:** "We're a Trustpilot alternative. Help me understand what businesses hate about Trustpilot."

**Good output includes:** Mining Trustpilot's own reviews (meta!), G2, and Reddit for complaints about Trustpilot, extracting themes like review gating, pricing, fake review handling, and producing a voice-of-customer swipe file the founder can use in outreach.