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alebgl77alebgl77/claude-inc/skills/customer-research/SKILL.md

customer-research

Skill21 starsChanged 4 months agoMITView on GitHub

Mines reviews, interviews, support tickets and forum threads for voice-of-customer insight — verbatim pains, desires, objections and triggers, clustered into themes with JTBD statements and a message-market map. Use when the user says "analyze these reviews", "what do customers actually want", "synthesize this feedback", or before writing any positioning or copy.

66 other projects use a version of this.

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  • +7 more from Cesarjoquin

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 59 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before positioning, landing copy, or ads — copy written without voice-of-customer is guessing

    line 15

“Common” is measured: kept by at least 6 in 10 of the 59 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Customer Research — Voice of Customer

"Synthesise user voice"

When to use

  • "Here are 200 reviews — what do customers actually care about?"
  • "Synthesize these interview notes / support tickets"
  • "What objections keep coming up?"
  • Before positioning, landing copy, or ads — copy written without voice-of-customer is guessing
  • Works on pasted text or files; optional upgrade: web search to mine public reviews and Reddit threads

Workflow

  1. Ingest the corpus: pasted text, CSV/exports, or files. Note the source mix and any sampling bias (e.g. only angry customers write tickets).
  2. Extract verbatims into four buckets: pains, desires, objections, buying triggers. Keep the customer's exact words — never paraphrase at this stage.
  3. Cluster into themes per bucket; count frequency so loud-but-rare doesn't beat quiet-but-common.
  4. Write JTBD statements for the top clusters: "When {situation}, I want to {motivation}, so I can {outcome}."
  5. Build the message-market map: their words → your copy blocks (headline candidates, bullet candidates, objection-handling lines) — quoted or lightly compressed, never marketing-speak.
  6. Flag the gaps: pains competitors' messaging ignores, plus anything surprising that contradicts current positioning.
  7. Hand off: name the top theme a copywriter should lead with, and the one objection every asset must answer.

Output format

## Voice of Customer — {corpus, n items, sources}

### Themes by frequency
| Bucket | Theme | Freq | Best verbatim |
|--------|-------|------|---------------|

### JTBD (top 3)
1. When ..., I want to ..., so I can ...

### Message-market map
| They say (verbatim) | Use it as |
|---------------------|-----------|
| "..."               | Headline / bullet / objection-handler |

### Gaps & surprises
- ...

### Handoff
Lead with: {theme}. Must answer: {objection}.

Quality bar

  • Every theme backed by ≥ 2 verbatims, quoted exactly
  • Frequencies counted, not vibed
  • Sampling bias of the corpus stated up front
  • JTBD statements contain a real situation, not a demographic
  • Map entries are usable copy blocks, not categories
  • At least one finding that challenges the current positioning (or explicit "none found")

Example

Ask: "Analyze these 80 G2 reviews of my scheduling tool." Produced: theme table (top pain: "double-booked because integrations lag", 19 mentions), 3 JTBD statements, a message-market map with 8 ready-to-use lines, gap ("nobody markets to the assistant persona who actually configures it"), and the handoff note.

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