/cs-market-research
Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always
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What this command does when you run it.
Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always
Command definition
cs-market-research.mddescription: Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always show method + assumptions. Direct invocation of the market-research skill.
argument-hint: "<market context: total market value, customer count, price, survey params, candidate segments>"
/cs:market-research — TAM/SAM/SOM + survey sampling + segmentation
Run the `market-research` skill on this input:
**$ARGUMENTS**
Three-tool workflow
1. **`market_sizer.py`** — Compute TAM/SAM/SOM by BOTH top-down (total market value × fractions) and bottoms-up (customers × price × adoption) methods side-by-side. Reports divergence and flags failed triangulation. Industry tuning via `--profile`. Never returns a single number.
2. **`sample_size_planner.py`** — Survey sample size from confidence, margin of error, and expected proportion, with the finite-population correction and per-segment minimums (a survey powered overall is not powered per reported segment).
3. **`segmentation_scorer.py`** — Score candidate segments against Kotler's measurable / substantial / accessible / differentiable / actionable criteria. Enforces a substantiality + accessibility gate; drops demographic slices that are too small or unreachable.
Output
- TAM/SAM/SOM both ways + triangulation flag + assumptions
- Survey n (overall + per-segment floors)
- Segment scores with TARGET / WATCH / DROP verdicts
- Top 3 next actions
Hard rule
**A market size always travels with its method (both ways) and assumptions — never a single unsourced number.**
First run + optimization
- **Onboard first:** `python3 skills/market-research/scripts/onboard.py` (market profile, survey confidence, margin of error, sizing method) — saved config pre-configures every tool. `--show` lists the questions.
- **Optimize (opt-in):** only if the user asks to reconcile the sizing/run a loop, hand off to autoresearch via `skills/market-research/scripts/ar_evaluator.py` (`tam_divergence`, lower is better).
Distinct from
- `marketing-skill/campaign-analytics` — that measures a live campaign. This is upstream methodology.
- `marketing-skill/marketing-strategy-pmm` — that sets positioning/GTM. This sizes and segments the market.
- `commercial/pricing-strategist` — that sets price. This sizes the market.
Read more
description: Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always show method + assumptions. Direct invocation of the market-research skill. argument-hint: "<market context: total market value, customer count, price, survey params, candidate segments>"
/cs:market-research — TAM/SAM/SOM + survey sampling + segmentation
Run the `market-research` skill on this input:
**$ARGUMENTS**
Three-tool workflow
1. **`market_sizer.py`** — Compute TAM/SAM/SOM by BOTH top-down (total market value × fractions) and bottoms-up (customers × price × adoption) methods side-by-side. Reports divergence and flags failed triangulation. Industry tuning via `--profile`. Never returns a single number.
2. **`sample_size_planner.py`** — Survey sample size from confidence, margin of error, and expected proportion, with the finite-population correction and per-segment minimums (a survey powered overall is not powered per reported segment).
3. **`segmentation_scorer.py`** — Score candidate segments against Kotler's measurable / substantial / accessible / differentiable / actionable criteria. Enforces a substantiality + accessibility gate; drops demographic slices that are too small or unreachable.
Output
- TAM/SAM/SOM both ways + triangulation flag + assumptions
- Survey n (overall + per-segment floors)
- Segment scores with TARGET / WATCH / DROP verdicts
- Top 3 next actions
Hard rule
**A market size always travels with its method (both ways) and assumptions — never a single unsourced number.**
First run + optimization
- **Onboard first:** `python3 skills/market-research/scripts/onboard.py` (market profile, survey confidence, margin of error, sizing method) — saved config pre-configures every tool. `--show` lists the questions.
- **Optimize (opt-in):** only if the user asks to reconcile the sizing/run a loop, hand off to autoresearch via `skills/market-research/scripts/ar_evaluator.py` (`tam_divergence`, lower is better).
Distinct from
- `marketing-skill/campaign-analytics` — that measures a live campaign. This is upstream methodology.
- `marketing-skill/marketing-strategy-pmm` — that sets positioning/GTM. This sizes and segments the market.
- `commercial/pricing-strategist` — that sets price. This sizes the market.
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Repo: alirezarezvani/claude-skills
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