| name | voice-of-customer-miner |
| argument-hint | [whose customer voice, and the decision it informs] |
| description | Mine public reviews, app stores, and forums for unmet needs, competitor weaknesses, and switching triggers — with quoted evidence. Use when you want customer voice without waiting on interviews. |
| intent | Mine public customer voice for unmet needs, competitor weaknesses, and switching triggers, with real quoted verbatims and labeled inference. Bridges competitive intelligence and discovery: outputs feed JTBD canvases, opportunity solution trees, and battle cards — as hypotheses to validate, not verdicts. |
| type | workflow |
| theme | market-intelligence |
| best_for | ["Finding what users actually complain about and wish for — yours and competitors' — from the public record","Arming battle cards with competitor weaknesses in customers' own words","Seeding discovery interviews and opportunity trees with evidence-backed hypotheses"] |
| scenarios | ["Mine the reviews of our top two competitors — what are their customers angriest about?","Before the interview cycle starts, what does the public web say our segment's unmet needs are?"] |
| estimated_time | 20-35 min per run |
Voice-of-Customer Miner
Purpose
Mine public customer voice — review sites, app stores, Reddit and practitioner forums, community
boards — for unmet needs, competitor weaknesses, and switching triggers: search plan → source sweep
→ verbatim capture → need themes → so what → next-step options. This bridges competitive
intelligence and discovery: it delivers customers' exact words without waiting on an interview cycle.
But public voice skews toward the angry and the vocal, so every theme it surfaces is a hypothesis to
validate, never a verdict — the output's last stop is always a real conversation.
Input
Works best with: the product(s) or competitor(s) to mine — yours, a rival's, or a set — and the
decision this should inform.
Also useful: a theme to focus on (onboarding, pricing, reliability) if you have one; otherwise the
sweep runs open.
Input supplied inline with the invocation — text after the skill name, a pasted context dump, or an
appended ARGUMENTS: line — counts as answers already given. Use it against the question budget;
don't re-ask.
Arriving empty-handed? That works too. The skill opens with at most 3 questions (whose voice,
what decision, theme or open sweep) and proceeds on labeled assumptions if they go unanswered.
Example invocation: Mine voice-of-customer for [Competitor A] and [Competitor B], focus on onboarding — informs whether our Q1 bet is a migration tool.
Key Concepts
- Governing protocol: honors the
autonomous-investigation
contract — question budget of 3, search-plan gate, Fact/Inference/Assumption labels, Just Enough
Mode, stable schema, 4-option Final Step. Discipline: OSINT's review-and-community layer (see
intelligence-collection-disciplines).
- Theme by need, not by feature. "Exports are broken" is a feature complaint; "I can't get my
data where my team works" is the underlying need. Theming by need is the same solution-free
discipline as JTBD and painstorming — and it's what makes themes portable into discovery.
- Verbatims are the product. Short, real, quoted customer language with URLs. Verbatims teach
persona language: the exact words customers use become interview probes and positioning copy.
Never fabricate quotes, ratings, review counts, or reviewer roles.
- Every source has a known skew. Reviewers skew negative; vendor communities skew loyal; app
stores over-represent update anger. Note the bias per source — public voice is evidence with a
known skew, not ground truth.
