| name | customer-research |
| description | Customer research — run and synthesize primary research (interviews, surveys, PMF survey), mine public voice-of-customer signal (Reddit, G2/Capterra, Hacker News, app stores, communities), and build evidence-backed JTBD maps, VOC quote banks, and personas. Use for "customer research", "talk to customers", "customer interviews", "interview questions", "survey design", "PMF survey", "analyze transcripts", "support ticket analysis", "win/loss", "churn research", "voice of customer", "VOC", "review mining", "G2 reviews", "Reddit mining", "digital watering holes", "Sales Safari", "jobs to be done", "JTBD", "build personas", "ICP refinement". |
| allowed-tools | Read, Write, Glob, Grep, WebSearch, WebFetch |
Customer Research
Find out what customers actually think, say, and struggle with — so positioning, product, and copy rest on evidence instead of assumption.
When this skill activates
Implicit: "talk to our customers", "why do they churn", "write interview questions", "analyze these transcripts / tickets / reviews", "what language do buyers use", "build a persona", "sharpen our ICP".
Explicit: "Use the customer-research skill to [task]."
Routed from: /mk:research customer, /mk:research icp, /mk:research market (demand-side half only — see Market sizing below), the market-researcher agent, /mk:plan when the ICP section of the hub needs evidence.
Scope
Covers:
- Mode 1 — analyze existing assets: interview/sales-call transcripts, surveys, support tickets, win/loss notes, NPS verbatims.
- Mode 2 — mine public signal: Reddit, G2/Capterra/Trustpilot, Hacker News, Indie Hackers, Product Hunt, LinkedIn posts + job postings, YouTube/TikTok comments, app-store reviews.
- Mode 3 — primary research: recruiting, outreach, casual interviews, 5-why laddering, survey design, the PMF survey.
- Synthesis: JTBD maps, theme clustering with frequency x intensity, confidence labelling, VOC quote banks.
- Persona and ICP construction from evidence (including the no-reviews-yet proxy ladder).
Does NOT cover:
- Competitor teardowns (pricing, positioning, feature matrices) → [[competitor-profiling]]. This skill only extracts what customers say about competitors.
- Owning the ICP/positioning record → [[product-marketing]] writes
plans/marketing-context.md; this skill feeds it evidence.
- Tactic/channel ideation from the findings → [[marketing-ideas]].
- Top-down market sizing (TAM/SAM/SOM) — no method here; see Market sizing below.
Three modes
Most engagements combine them. Mine before you ask: Mode 2 tells you what to ask in Mode 3, and in whose words. Establish which modes apply before doing anything else.
| Mode | Situation | Method |
|---|
| 1. Analyze existing assets | You already hold raw material | Extract signal with the framework below |
| 2. Mine public signal | Customers speak unprompted in public | Watering-hole research, references/source-guides.md |
| 3. Go ask | No signal yet, or only the customer can answer | Interviews + surveys, references/interviews-and-surveys.md |
Mode 1 — extraction framework
For every asset, extract six things:
- Jobs to Be Done — functional job (the task), emotional job (how they want to feel), social job (how they want to be perceived).
- Pain points — what is broken or inadequate now. Prioritize pains raised unprompted and with emotional language.
- Trigger events — what changed that started the search. Common: team growth, new hire, missed target, an embarrassing incident, a competitor move.
- Desired outcomes — success in their words. Capture exact quotes, never paraphrase.
- Language and vocabulary — "we were drowning in spreadsheets" beats "manual process inefficiency". This is the copy input.
- Alternatives considered — including doing nothing, hiring someone, and building internally.
Asset-specific reading order:
- Transcripts / sales calls — find the moment they decided to look, what they tried before, what success means to them.
- Surveys — segment by tier/use case/tenure before concluding. Flag where open-ended answers contradict multiple-choice ones (they often do).
- Support conversations — categorize first: bugs vs. confusion vs. missing features vs. expectation mismatch. Not all tickets are equal signal. Mine "I wish it could…".
- Win/loss + churn notes — wins: what tipped it, what nearly won instead. Losses: price, features, fit, or timing. Segment by reason; never average across causes.
- NPS — passives and detractors carry more improvement signal than promoters. A 9 with a specific complaint beats a 10 with no comment.
Synthesis
- Cluster by theme across assets.
- Score frequency x intensity — how often it appears, how strongly it's felt.
- Segment by profile (company size, role, use case, tenure) — do the patterns diverge?
- Pull 5-10 money quotes per theme, verbatim with source and date.
- Flag contradictions — where customers say one thing and do another.
Quality guardrails
Label every insight before presenting it:
| Confidence | Criteria |
|---|
| High | Theme in 3+ independent sources, raised unprompted, consistent across segments |
| Medium | 2 sources, or prompted only, or confined to one segment |
| Low | Single source; may be an outlier; needs validation |
- Recency window — last 12 months is primary; 12-24 months with caution; 2+ years for baseline context only. A theme that holds across old and new data is durable.
- Sample bias — reviewers skew to strong opinions; tickets skew to problems, not value; Reddit skews technical and skeptical vs. mainstream buyers. Say so when generalizing.
- Minimum viable sample — no personas and no messaging conclusions from fewer than 5 independent data points per segment.
Full source-by-source weighting table: references/source-guides.md.
Mode 2 — digital watering holes
Pick sources by ICP type, then use the per-platform playbooks and search operators in references/source-guides.md.
| ICP type | Primary sources |
|---|
| B2B SaaS / technical buyers | Reddit (role subs), G2/Capterra, Hacker News, LinkedIn, Indie Hackers |
| SMB / founders | r/entrepreneur, r/smallbusiness, Indie Hackers, Product Hunt, Facebook Groups |
| Developer / DevOps | r/devops, r/programming, Hacker News, Stack Overflow, Discord |
| B2C / consumer | App-store reviews (1-3 star), hobby subreddits, YouTube + TikTok comments |
| Enterprise | LinkedIn, G2 enterprise filter, job postings, analyst reports |
Quick routing: have a category → G2/Capterra (yours + competitors, 3-star first). Need raw language → Reddit, YouTube comments. Need triggers → LinkedIn posts, job postings, "Ask HN". Need competitive gaps → competitor 4-star reviews.
Capture per item: source (platform, URL, date) · verbatim quote · context (what prompted it) · sentiment · theme tag (#pain #trigger #outcome #language #alternative #objection #competitor) · profile signals (role, company size, industry). After 20-30 entries the patterns surface; quotes recurring across unrelated sources are the highest-confidence insights.
Mode 3 — interviews and surveys
Load references/interviews-and-surveys.md before running any interview or survey. Headlines:
- The first rule of customer research: you do not talk about customer research. Framed as a study, people perform and give the socially acceptable answer. Keep it a casual chat. Don't lead, don't pitch, don't defend the product.
- Prove yourself wrong, not right. Research is disconfirmation, not validation.
- Recruit the customers you want more of — segment the CRM by high deal size, short sales cycle, low churn; take referrals from sales and CS; close every call with "who else should we talk to?".
- Keep asking why — ladder each answer 3-5 levels to the root motivation or business outcome. Capture pain points (drive acquisition) and passion points (drive retention and referrals).
- The PMF survey — "How would you feel if you could no longer use [product]?" with the 40% "very disappointed" benchmark.
- Survey design — short, open-ended for language mining, never leading; multiple-choice answers are artifacts of the options you supplied.
First-party interview and survey signal outranks scraped sources when the two conflict. Analyze everything you gather back through the Mode 1 framework and confidence labels.
ICP refinement and personas
Personas are built from research, not invented. Gate: at least 5-10 data points from one consistent segment before writing one. Structure, proxy ladder for pre-revenue products, and anti-patterns: references/personas-and-icp.md.
Feeding the hub: the ICP section of plans/marketing-context.md is owned by [[product-marketing]]. This skill supplies segment definition, trigger events, ranked pains, desired outcomes, objections, alternatives, and the vocabulary list — each with its confidence label and sources. Do not overwrite the hub silently; propose the diff.
Market sizing (routing, not a method)
/mk:research market and the market-researcher agent claim TAM/SAM/SOM sizing. This skill does not provide a sizing methodology — the upstream source contains none, and none is invented here. What it can contribute to a sizing exercise:
- Segment definition and qualifying criteria (who is actually in the market).
- Bottom-up demand evidence: trigger frequency, alternatives in use, willingness-to-switch signals, budget/objection language from interviews.
- Falsification of a top-down number — if a segment's buyers all report a different job or a free workaround, the SAM claim is wrong.
Top-down sizing inputs (analyst reports, census/firmographic counts, public revenue benchmarks) must come from cited external sources via WebSearch/WebFetch and be marked as such. Never state a market size without a citation or a stated bottom-up derivation; if neither exists, write [NEEDS DATA].
Key concepts
- Disconfirmation over validation — a question that cannot return an answer you dislike is worthless. Rewrite it.
- Falsifiable findings — every insight ships with its confidence label, source count, and the observation that would disprove it ("how would we know this is wrong?"). Required by
.claude/workflows/marketing-rules.md for research reports.
- Frequency x intensity — ranking themes on both, not on count alone; one furious customer and twelve shrugs are different signals.
- Jobs to Be Done — functional, emotional, and social; buyers hire a product for an outcome, not a feature.
- Money quote — verbatim customer language, sourced and dated, that carries a theme into copy without translation.
- Passion points vs. pain points — pains drive acquisition, passions drive retention and referral. Ladder for both.
- Proxy evidence — provisional persona input from competitor/adjacent reviews when first-party data does not exist yet; tagged as proxy, replaced as real data arrives.
Output
plans/marketing/<research>/report.md — the /mk:research deliverable: themes ranked by frequency x intensity, confidence-labelled findings, money quotes with sources, implications, and open questions.
Ask which deliverable(s) are wanted before generating. Options: research synthesis report · VOC quote bank (quotes by theme, for copy) · persona document (1-3) · JTBD map by segment · competitive intelligence summary (what customers say about competitors vs. you) · research gap analysis (what is still unknown and how to find it).
PII: redact names, emails, phone numbers, and company-identifying details from quotes written into plans/marketing/ — per .claude/workflows/automation-rules.md. Quote the language, not the person.
Before proceeding
If context is unclear, lead with (1) and (2), follow up as needed:
- What's the goal — messaging, personas, product gaps, churn?
- What do you already have — transcripts, surveys, tickets, reviews, nothing?
- Which segment — all customers, a tier, churned users, lost prospects?
- What's the product? (skip if
plans/marketing-context.md answers it)
- What deliverable?
Read plans/marketing-context.md first and skip anything it already answers.
Cross-references
plans/marketing-context.md — required hub; this skill's ICP/persona findings feed its ICP section.claude/workflows/marketing-rules.md — quality gates, no-hallucinated-metrics, falsifiable findings, output conventions.claude/workflows/automation-rules.md — PII redaction for customer data- [[product-marketing]] — owns the ICP/positioning record this skill supplies evidence for
- [[competitor-profiling]] — competitor teardowns; pair for
/mk:research competitor
- [[marketing-ideas]] — turns findings into tactics; pair for
/mk:research market
- [[copywriting]] · [[cro]] · [[cold-email]] · [[content-strategy]] · [[ads]] — downstream consumers of the VOC quote bank and JTBD map
references/interviews-and-surveys.md · references/source-guides.md · references/personas-and-icp.md.claude/skills/marketing/README.md — full kit overview
Provenance
Imported from coreyhaines31/marketingskills (MIT, (c) 2025 Corey Haines) and adapted for KitForge: KitForge frontmatter, /mk: routing and output paths, plans/marketing-context.md replaces the upstream .agents/product-marketing.md context file, PII + falsifiability gates added, upstream handoffs to skills ClauKit lacks (churn-prevention, prospecting, marketing-plan) re-pointed; the SparkToro tool integration referenced upstream is not shipped here.
