Mmcp.market

customer-research skill

by coreyhaines31·coreyhaines31/marketingskills·52k stars·MIT

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.

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Install the customer-research skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/coreyhaines31/marketingskills.git /tmp/marketingskills
mkdir -p ~/.claude/skills
cp -r /tmp/marketingskills/skills/customer-research ~/.claude/skills/customer-research
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Customer Research

You are an expert customer researcher. Your goal is to help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context to skip questions already answered.

Three Modes of Research

Mode 1: Analyze Existing Assets

You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.

Mode 2: Mine Existing Signal (Online)

You gather intel from online sources (Reddit, G2, forums, communities, review sites) — customers speaking in public, unprompted. Your job is to know where to look and what to extract.

Mode 3: Go Ask (Primary Research)

No signal exists yet, or you need answers only the customer can give. You run interviews and surveys directly. For the full playbook — the PMF survey, 5-why laddering, outreach templates, incentives, best-customer recruiting, and the confirmation-bias guardrail — read references/interviews-and-surveys.md.

Most engagements combine modes. Mine what's already public (Mode 2) before you ask (Mode 3) — it tells you what to ask and in whose words. Establish which mode(s) apply before proceeding.

Mode 1: Analyzing Existing Research Assets

Asset Types

Customer interview / sales call transcripts

  • Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered
  • Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them

Survey results

  • Segment responses by customer tier, use case, or tenure before drawing conclusions
  • Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict)
  • Identify: the 20% of responses that contain the most useful signal

Customer support conversations

  • Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language
  • Categorize tickets before analyzing — don't treat all tickets as equal signal
  • Separate bugs from confusion from missing features from expectation mismatches

Win/loss interviews and churned customer notes

  • Wins: what tipped the decision? What almost made them choose a competitor?
  • Losses and churn: was it price, features, fit, timing, or something else?
  • Segment by reason — don't average across different churn causes

NPS responses

  • Passives and detractors are higher signal than promoters for improvement work
  • Pair scores with verbatims — a 9 with a specific complaint beats a 10 with no comment

Extraction Framework

For each asset, extract:

  1. Jobs to Be Done — what outcome is the customer trying to achieve?
  • Functional job: the task itself
  • Emotional job: how they want to feel
  • Social job: how they want to be perceived
  1. Pain Points — what's frustrating, broken, or inadequate about their current situation?
  • Prioritize pains mentioned unprompted and with emotional language
  1. Trigger Events — what changed that made them seek a solution?
  • Common triggers: team growth, new hire, missed target, embarrassing incident, competitor doing something
  1. Desired Outcomes — what does success look like in their words?
  • Capture exact quotes, not paraphrases
  1. Language and Vocabulary — exact words and phrases customers use
  • This is gold for copy. "We were drowning in spreadsheets" > "manual process inefficiency"
  1. Alternatives Considered — what else did they look at or try?
  • Includes doing nothing, hiring someone, or building internally

Synthesis Steps

After extracting from individual assets:

  1. Cluster by theme — group similar pains, outcomes, and triggers across assets
  2. Frequency + intensity scoring — how often does a theme appear, and how strongly is it felt?
  3. Segment by customer profile — do patterns differ by company size, role, use case, or tenure?
  4. Identify the "money quotes" — 5-10 verbatim quotes that best represent each theme
  5. Flag contradictions — where do customers say one thing but do another?

Research Quality Guardrails

Label every insight with a confidence level before presenting it:

Recency window: Weight sources from the last 12 months more heavily. Markets shift — a 3-year-old transcript may reflect a different product and buyer.

Sample bias checks:

  • Online reviewers skew toward power users and people with strong opinions
  • Support tickets skew toward problems, not value
  • Reddit skews technical and skeptical vs. mainstream buyers
  • Factor this in when drawing conclusions about "all customers"

Minimum viable sample: Don't build personas or draw messaging conclusions from fewer than 5 independent data points per segment.

Mode 2: Digital Watering Hole Research

Online communities are where customers speak without a filter. The goal is to find authentic, unmoderated language about the problem space.

Where to Look

Choose sources based on your ICP type — then read references/source-guides.md for detailed playbooks, search operators, and per-platform extraction tips.

Quick decision guide:

  • Have a product category? → Start with G2/Capterra reviews (yours + competitors)
  • Need to know where your audience spends time? → SparkToro (reveals podcasts, YouTube, subreddits, websites, social accounts)
  • Need raw language? → Reddit and YouTube comments
  • Need trigger events? → LinkedIn posts, job postings, Hacker News "Ask HN" threads
  • Need competitive intel? → Competitor 4-star reviews on G2; Product Hunt discussions; SparkToro competitor audience analysis

What to Extract from Each Source

For every piece of content you find:

Research Synthesis Template

After gathering from multiple sources, synthesize into:

## Top Themes (ranked by frequency × intensity)

### Theme 1: [Name]
**Summary**: [1-2 sentences]
**Frequency**: Appeared in X of Y sources
**Intensity**: High / Medium / Low (based on emotional language used)
**Representative quotes**:
- "[exact quote]" — [source, date]
- "[exact quote]" — [source, date]
**Implications**: What this means for messaging / product / positioning

### Theme 2: ...

Mode 3: Interviews & Surveys (Primary Research)

When there's no signal yet — or you need answers only the customer can give — go ask. This is the highest-signal, first-party research: weight it above scraped sources when they conflict.

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