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voice-of-customer-miner

挖掘公开评论、应用商店和论坛，发现未被满足的需求、竞争对手的弱点以及促使用户转用其他产品的触发因素，并附上引用证据。适用于希望获取客户声音、无需等待访谈的场景。

作者

@deanpeters

分类

产品设计

安装

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name:voice-of-customer-minerargument-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:workflowtheme:market-intelligencebest_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 hypothesesscenarios: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.
  • Honest frequency. Recurring across sources ≠ concentrated in one thread ≠ isolated but</li>
    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

  • 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?
  • 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.
  • 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.
  • 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 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)

  • Generate discovery interview questions from the top theme (discovery-interview-prep)

  • Feed the weaknesses into a competitive battle card (battle-card-builder)

  • Build an opportunity solution tree from the top hypothesis (opportunity-solution-tree)

  • 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.

    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 (Interactive) — 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.