customer-research

Analyze customer research to extract jobs, pains, and desired outcomes.

Updated Apr 13, 2026
One-click install
npx skills add https://github.com/mmenigma/ClaudeAI --skill customer-research-mmenigma
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: customer-research
Source: https://github.com/mmenigma/ClaudeAI/tree/main/skills/customer-research
Command: npx skills add https://github.com/mmenigma/ClaudeAI --skill customer-research-mmenigma

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps uncover what customers actually think, feel, say, and struggle with — grounding messaging, product decisions, and copy in reality rather than assumptions.

Core Features & Use Cases

  • Extraction framework: jobs to be done, pains, triggers, desired outcomes, language, and alternatives.
  • Mode-driven workflows: Mode 1 analyzes existing assets; Mode 2 sources online research to supplement insights.
  • Asset types and outputs: transcripts, surveys, reviews, tickets; verbatim quotes, themed summaries, and a deliverable plan.
  • Synthesis and guardrails: clustering by theme, frequency and intensity scoring, source weighting, and confidence labeling.
  • Deliverables and alignment: top themes, VOC quote bank, persona and messaging recommendations, and research gap analysis.

Quick Start

Clarify your goal, gather relevant assets, apply the extraction framework, and produce a themes-based synthesis with recommended deliverables.

Frequently Asked Questions about customer-research

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

FAQPage Schema
How do I extract jobs to be done from customer research transcripts?

Customer research analysis uncovers what customers think and struggle with by applying an extraction framework to identify jobs to be done, pain points, and desired outcomes. It grounds product and messaging decisions in actual customer reality rather than internal assumptions.

Can I analyze existing support tickets and survey data for VOC verbatim quotes?

Yes, Mode 1 analyzes existing assets like support tickets and surveys to extract verbatim quotes and themed summaries. The process applies source weighting and confidence labeling to ensure the extracted VOC data accurately reflects customer sentiment.

What is the best way to find customer pain points and triggers from online reviews?

Use Mode 2 to source online research and supplement insights when existing assets are insufficient. This mode applies the extraction framework to online data to identify pain points, triggers, and desired outcomes, producing a structured synthesis with confidence labeling.

Does this customer research process provide messaging and persona recommendations?

Yes, the analysis produces recommended deliverables including persona and messaging recommendations. By synthesizing top themes and generating a VOC quote bank, it aligns deliverables with identified customer language, desired outcomes, and research gap analysis.

How do I handle research gap analysis when customer feedback is inconsistent?

The framework handles inconsistent feedback through source weighting, frequency and intensity scoring, and explicit confidence labeling. This approach clusters data into top themes and includes a research gap analysis to identify areas lacking sufficient reliable customer evidence.