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samuelgabrielsikorjak-sys
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customer-research

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

Updated Jul 23, 2026
Search Tags:#personas#interviews#customer research#voice of customer#jobs to be done#review mining#pmf survey
One-click install
npx skills add https://github.com/samuelgabrielsikorjak-sys/techscope-website --skill customer-research-samuelgabrielsikorjak-sys
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/samuelgabrielsikorjak-sys/techscope-website/tree/main/.claude/skills/marketingskills-main/skills/customer-research
Command: npx skills add https://github.com/samuelgabrielsikorjak-sys/techscope-website --skill customer-research-samuelgabrielsikorjak-sys

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 evidence. This Skill turns raw research material — interview transcripts, surveys, support tickets, G2 reviews, Reddit threads — into structured insights like jobs-to-be-done, pain points, trigger events, and verbatim customer language. ## Core Features & Use Cases - Three research modes: analyze existing assets (transcripts, surveys, tickets), mine online watering holes (Reddit, G2, Hacker News, app stores), and run primary research (interviews and PMF surveys). - Structured extraction framework: captures jobs to be done, pains, triggers, desired outcomes, exact customer vocabulary, and alternatives considered, with confidence labels and sample-bias guardrails. - Persona and deliverable generation: builds evidence-based personas, VOC quote banks, JTBD maps, and competitive intelligence summaries. - Use Case: You have 20 customer interview transcripts and want homepage messaging. The Skill extracts recurring themes and money quotes, scores them by frequency and intensity, produces a VOC quote bank, then hands off to a copywriting skill. ## Quick Start Ask the assistant to analyze your customer interview transcripts and extract the top pain themes, trigger events, and verbatim quotes into a research synthesis report.

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, exact customer vocabulary, and alternatives considered from each transcript. Then cluster themes across interviews, score by frequency and intensity, and label each insight with a confidence level based on source count.

How do I do customer research with no existing data?▼

Start with digital watering hole research: mine Reddit, G2, competitor reviews, and niche communities for unprompted customer language. Then run 5-10 customer interviews using casual outreach, and avoid building personas until you have at least 5-10 data points per segment.

What is the PMF survey and the 40% benchmark?▼

The PMF survey asks users "How would you feel if you could no longer use the product?" If 40% or more answer "very disappointed," you likely have product/market fit. Superhuman used this method and reached 58% by segmenting and serving the very-disappointed cohort.

Which review sites and communities are best for B2B customer research?▼

For B2B SaaS, prioritize G2 and Capterra reviews (especially 3-star and competitor 4-star reviews), role-specific subreddits, Hacker News, and LinkedIn. For B2C, use app store reviews, hobby subreddits, and YouTube or TikTok comments.

Why should I not average churn reasons across all customers?▼

Different churn causes (price, features, fit, timing) require different fixes, so averaging hides the actionable signal. Segment exit survey and win/loss data by reason first, then pair open-ended verbatims with quantitative data before drawing conclusions.

What are the limitations of online review mining?▼

Online reviewers skew toward power users and strong opinions, support tickets skew toward problems, and Reddit skews technical and skeptical. Weight sources from the last 12 months, require multiple independent sources per theme, and label insights with confidence levels.

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