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MarcoBolsa
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customer-research

Analyze customer interviews, surveys, reviews, and online communities to extract voice-of-customer insights.

Updated May 13, 2026
Search Tags:#reddit#persona#customer research#voice of customer#jobs to be done#g2#review mining
One-click install
npx skills add https://github.com/MarcoBolsa/motor-cotacoes --skill customer-research-marcobolsa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/MarcoBolsa/motor-cotacoes/tree/main/.claude/skills/marketingskills/customer-research
Command: npx skills add https://github.com/MarcoBolsa/motor-cotacoes --skill customer-research-marcobolsa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Teams often build messaging, personas, and product decisions on assumptions instead of real customer language. This Skill turns raw research assets (transcripts, surveys, support tickets, reviews) and online community data into structured, confidence-scored customer insights. ## Core Features & Use Cases - Existing Asset Analysis: Extract jobs-to-be-done, pain points, trigger events, desired outcomes, and verbatim language from interview transcripts, surveys, support tickets, NPS responses, and win/loss notes. - Digital Watering Hole Research: Mine Reddit, G2, Capterra, Hacker News, LinkedIn, app store reviews, and niche communities for unfiltered customer language, with per-source playbooks in the references guide. - Persona & Deliverable Generation: Build evidence-based personas, VOC quote banks, JTBD maps, and competitive intelligence summaries with high/medium/low confidence labels. - Use Case: You have 20 customer interview transcripts and want homepage messaging. The Skill extracts recurring pain themes and money quotes, scores them by frequency and intensity, then hands off to a copywriting skill. ## Quick Start Analyze my customer interview transcripts and online reviews to identify the top pain themes, verbatim quotes, and a research-backed persona.

Frequently Asked Questions about customer-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze customer interview transcripts for insights?▼

Extract jobs-to-be-done, pain points, trigger events, desired outcomes, and exact customer vocabulary from each transcript. Then cluster findings by theme, score by frequency and intensity, and label each insight with a confidence level based on how many independent sources mention it.

How do I do customer research without existing interviews?▼

Use digital watering hole research: mine Reddit threads, G2 and Capterra reviews, Hacker News, LinkedIn posts, and niche communities where your target customers speak unfiltered. Extract verbatim quotes, pain themes, and switching triggers from each source.

What review sites are best for competitor customer research?▼

G2 and Capterra are the primary sources for B2B software. Read competitor 4-star reviews for buried complaints, 3-star reviews for honest tradeoffs, and the 'What do you dislike' sections for weaknesses you can position against.

How many data points do I need before building a customer persona?▼

Build personas only after collecting at least 5-10 data points from a consistent segment, such as interviews, reviews, or community posts. Inventing persona details without data produces profiles that represent no real customer.

Can support tickets be used for voice-of-customer research?▼

Yes, but categorize tickets first into bugs, confusion, feature requests, and expectation mismatches, since not all tickets carry equal signal. Mine them for recurring complaints and 'I wish it could' language, while noting they skew toward problems rather than value.

What are the limitations of online review mining?▼

Online reviewers skew toward power users and people with strong opinions, Reddit skews technical and skeptical, and support tickets skew negative. Weight sources from the last 12 months more heavily and avoid drawing conclusions from fewer than five independent data points per segment.

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