voice-of-customer-miner

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

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.

1. 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.

1. 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. Build an opportunity solution tree from the top hypothesis (`opportunity-solution-tree`)
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.

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.