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

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

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.

voice-of-customer-miner

NPX Install

Tags

SKILL.md Content