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customer-research

Analyze customer research assets and produce confidence-scored insight syntheses.

Updated Mar 17, 2026
Search Tags:#competitive intelligence#customer research#voice of customer#jobs to be done#interview analysis#persona building#review mining
One-click install
npx skills add https://github.com/fianchettogianni/my-marketing-skills --skill customer-research-fianchettogianni
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/fianchettogianni/my-marketing-skills/tree/main/skills/customer-research
Command: npx skills add https://github.com/fianchettogianni/my-marketing-skills --skill customer-research-fianchettogianni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the guesswork in marketing, product, and positioning decisions by replacing untested internal assumptions with authentic, data-backed customer insights. It addresses the common pain point of teams making high-stakes decisions based on opinions rather than actual customer behavior, language, and needs.

Core Features & Use Cases

  • Dual Research Modes: Seamlessly switches between analyzing existing research assets (interview transcripts, surveys, support tickets, win/loss notes, G2/Capterra reviews) and conducting new digital watering hole research from sources like Reddit, LinkedIn, Hacker News, and niche community platforms.
  • Structured Insight Extraction: Applies a proven framework to pull critical data points including jobs to be done, pain points, trigger events, desired outcomes, exact customer vocabulary, and considered alternatives from any research source.
  • Validated Syntheses: Compiles findings into actionable, confidence-scored outputs including themed research reports, voice of customer quote banks, data-backed customer personas, jobs-to-be-done maps, and competitive intelligence summaries, with guardrails to avoid sample bias and low-confidence conclusions.
  • Use Case Example: If you have 20 customer interview transcripts, this Skill will cluster themes by frequency and emotional intensity, extract high-signal "money quotes," and flag contradictions to help you refine your messaging and product positioning.

Quick Start

Ask the AI to analyze your existing customer interview transcripts and extract the top 3 recurring pain points along with verbatim customer quotes you can use in your marketing copy.

Frequently Asked Questions about customer-research

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

FAQPage Schema
How do I extract voice of customer quotes from interview transcripts?▼

To extract voice of customer quotes from interview transcripts, you can analyze existing research assets to cluster themes by frequency and emotional intensity, pulling high-signal verbatim quotes for marketing copy. This process flags contradictions and eliminates assumption-driven messaging decisions.

What is the best way to analyze customer reviews for jobs to be done?▼

Analyzing customer reviews for jobs to be done involves mining data from sources like G2 and Capterra to identify pain points, desired outcomes, and considered alternatives. This structured extraction replaces internal assumptions with data-backed customer personas.

Can I use Reddit and LinkedIn for digital watering hole customer research?▼

Yes, you can conduct digital watering hole customer research from platforms like Reddit, LinkedIn, Hacker News, and niche communities. This primary research gathers authentic customer behavior data and synthesizes it into themed reports with confidence scoring.

How do I build data-backed customer personas from support tickets and surveys?▼

Building data-backed customer personas from support tickets and surveys requires synthesizing existing research assets to extract trigger events, exact customer vocabulary, and jobs to be done. This structured approach eliminates guesswork in product and positioning decisions.

Does this customer research approach help avoid sample bias in competitive intelligence?▼

This customer research approach includes guardrails specifically designed to avoid sample bias and low-confidence conclusions when generating competitive intelligence summaries. It applies validated syntheses to ensure your insights are data-backed and reliable.

What is review mining for customer research and when do I need it?▼

Review mining for customer research is the process of extracting jobs to be done, pain points, and customer vocabulary from review site data. You need it when making high-stakes product or marketing decisions to replace untested internal assumptions with authentic customer insights.

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