Voice-of-Customer Miner

中文速览（Quick Guide）

• 做什么：从评论站、应用商店、Reddit 与从业者论坛等公开渠道挖掘客户原声，提炼未满足需求、竞品弱点与切换触发点。
• 何时用：想不等访谈周期就拿到客户声音时；对象无公开足迹、需要私有区域自有用户声音、或需要统计置信度时不适用。
• 核心步骤：①问最多 3 个问题（谁的声音、支撑什么决策、有无主题）并按假设推进；②过 search-plan 闸后做来源清扫；③逐条带 URL 抓原话；④按需求（而非功能）归主题并标注事实/推断/假设与来源偏差；⑤给 4 个下一步选项。
• 国内可达性：正文指定的公开源以境外站点为主，部分（如 Reddit、应用商店评论）在国内不可直接访问；此时可改用国内可访问的公开社区与评论渠道，并保持同样的「原话 + URL + 偏差标注」纪律。

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.
• 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 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):
2. Whose customer voice — yours, a competitor's, or a set?
3. What decision should this inform?
4. Any specific theme to focus on, or open sweep?
5. 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.
6. 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.
7. Emit the schema below exactly.

Output schema (do not reorder)

# 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** assumptions to validate in real interviews
Each bullet: label, confidence, URL where relevant.

A copy/paste fill-in version of this schema, with quality checks, lives in template.md.

Final Step (offer exactly 4 options)

1. Generate discovery interview questions from the top theme (discovery-interview-prep)
2. Feed the weaknesses into a competitive battle card (battle-card-builder)
3. Organize the top unmet needs into an opportunity → solution tree, then hand that tree to
   downstream opportunity/solution decomposition
4. Re-run scoped to one theme in Verbose Mode

Accept 1, 2, 3, 4, 1 and 2, Verbose Mode, or a custom path.

Examples

A theme done right (fictional product, illustrative verbatims):

> ### Theme: getting historical data out at contract end
> - Frequency: recurring — 9 reviews across two sites plus a forum thread, past 6 months
> - Verbatim: "export took three support tickets and still dropped custom fields" — [G2-style review, URL]
> - Verbatim: "we stayed a year longer than we wanted because leaving meant losing our audit trail" — [forum thread, URL]
> - Who says it: ops managers at 50-200-person firms — Inference (reviewer titles where shown)
> - Reading: exit friction is functioning as involuntary retention — Inference; a rival with
> effortless migration turns this from their moat into their churn event.

Notice the theme name contains no feature ("export tool") — it names the need, so discovery can
explore solutions the reviews never imagined.

See examples/sample.md for a complete worked mining run (fictional
FSM-software market) where frequency honesty caps a vivid theme at low confidence and each
source's bias becomes a reading instruction. examples/sample-industrial.md
shows the thin-voice case — what honest mining looks like when the market barely posts reviews.

Common Pitfalls

• Feature-name theming. Clustering by the feature customers blame instead of the need underneath
  hands your roadmap to the loudest UI complaint.
• Verbatim laundering. Paraphrasing a review and quoting it. If it has quote marks, it must be a
  real excerpt at a real URL — this domain's do-not-invent list exists because fabricated customer
  quotes are both tempting and toxic.
• Rant amplification. One vivid one-star review presented as a theme. Frequency honesty is the
  discipline: recurring, concentrated, or isolated — say which.
• Skew blindness. Reading review sites as a census. The angry and the vocal are over-sampled;
  the satisfied-and-silent majority never posts. Bias notes per source are mandatory.
• Skipping the validation handoff. Shipping themes straight into the roadmap. The output's
  "assumptions to validate in real interviews" section is the bridge to discovery — use it.

When Sources Are Out of Reach (Failure Exits)

• Review sites unreachable or paywalled (G2, Capterra, app stores): Say which sources failed and switch to what is reachable — user-provided review exports, support tickets, community archives, or locally accessible sources the user nominates (for Chinese markets: practitioner forums, Zhihu-style Q&A, app store CN listings). Same sweep discipline applies; note each substitute source's skew like any other.
• Not a single verbatim URL captured: Stop and say so. Do not emit a theme with zero verbatims — paraphrased "quotes" are verbatim laundering (see Pitfalls). The honest deliverable is "no public voice found; here is what to do instead" → run discovery-interview-prep.
• Thin-voice market: Follow the examples/sample-industrial.md pattern — fewer themes, lower confidence caps, explicit absence-of-evidence notes; never pad frequency claims to look busy.
• Garbage or contradictory input (gibberish product names, conflicting decisions): Ask one clarifying question naming the specific contradiction; if it stays unclear, proceed only on labeled Assumptions and mark the whole run's confidence accordingly — do not silently guess.

FAQ (Wrong → Right)

References

• autonomous-investigation (Workflow) — the governing protocol
• intelligence-collection-disciplines (Component) — OSINT review-mining sources and bias tradecraft
• jobs-to-be-done (Component) — the solution-free framing themes should land in
• discovery-interview-prep (Interactive) — where the validation happens
• Opportunity solution tree (external optional reference, not installed here) — structures the opportunity hypotheses
• battle-card-builder (Workflow) — consumes the weak points
• Adapted from market-intelligence/voice-of-customer-miner-prompt.md in the
  https://github.com/deanpeters/product-manager-prompts repo.