Customer research

Skill alebgl77/claude-inc/skills/customer-research

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.From its SKILL.md

Install

npx -y skills add alebgl77/claude-inc --skill customer-research

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.0 KB, 617 tokens by cl100k_base, as published. Nobody here has run it

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

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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most research analysis skills give in 617 tokens

Counted across 1,213 of the 2,113 authors here whose files we hold, read 2026-09-06

Cite sources for every important claimin 47 of 1213, across 38 files
Separate facts from inferences and recommendationsin 21 of 1213, across 12 files
Write findings to a markdown filein 19 of 1213
Label every insight with a confidence levelin 18 of 1213, across 8 files
Read product marketing context before asking questionsin 18 of 1213, across 8 files
Rank themes by frequency and intensityin 16 of 1213, across 6 files
Establish research mode before proceedingin 16 of 1213, across 6 files
Segment survey responses by customer tier or tenurein 16 of 1213, across 6 files
Categorize support tickets before analyzingin 16 of 1213, across 6 files
Weight research sources from the last twelve monthsin 16 of 1213, across 6 files
Use at least five data points per segmentin 15 of 1213, across 5 files
Extract verbatim quotes for all research findingsin 15 of 1213, across 5 files

Said here and by no other author read

Ingest the provided corpus
Extract verbatims into four buckets
Cluster themes by frequency
Write JTBD statements for top clusters
Build a message-market map
Flag gaps and surprises

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.