/voice-of-customer-miner

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`](../autonomous-investigation/SKILL.md)

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`](../intelligence-collection-disciplines/SKILL.md)).

• **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.

• **Honest frequency.** *Recurring across sources* ≠ *concentrated in one thread* ≠ *isolated but

vivid*. Say which; one articulate ranter is not a theme.

• **When NOT to use:** no meaningful public footprint (early-stage, niche enterprise) → run

[`discovery-interview-prep`](../discovery-interview-prep/SKILL.md) instead; you need *your* users' voice on a private area → mine your own tickets and research; statistical confidence required → this is qualitative theming.

Application

1. **Credit inline context**, then ask only the unanswered questions (max 3): 1. Whose customer voice — yours, a competitor's, or a set? 2. What decision should this inform? 3. Any specific theme to focus on, or open sweep? 2. **Show the 3-bullet search plan** — which voice sources you'll sweep, how you'll select representative verbatims, how observation will be separated from interpretation. Continue unless revised. 3. **Sweep mixed voice sources** — review sites (G2, Capterra, TrustRadius), app stores, Reddit and practitioner forums, community boards, social threads — capturing short real quotes with URLs and noting each source's bias. 4. **Emit the schema below exactly.**

Output schema (do not reorder)

~~~markdown

Voice-of-Customer Snapshot

1. Scope

**Products mined:** | **Decision supported:** | **Sources swept:** | **As-of date:**

2. Need Themes

For each of the top 3-5 themes:

Theme: [Underlying need, solution-free, 4 to 8 words]

• **Frequency:** [recurring across sources / concentrated / isolated]
• **Verbatim:** "[short real quote]" — [source, URL]
• **Verbatim:** "[short real quote]" — [source, URL]
• **Who says it:** [role/segment, if evident — labeled]
• **Reading:** [Inference — what this suggests]

3. Competitor Weak Points

• **[Competitor]:** [weakness in customers' words; frequency; URL]
• [Max 5, strongest evidence only]

4. Switching Triggers

• [What pushes customers off a product; what pulls them; labeled, cited]

5. So What?

• **3** opportunity hypotheses (phrased as problems, not features)
• **2** battle-card-ready weaknesses (with evidence quality noted)
• **3** assum