market-research

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: market-research
description: Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
version: 2.9.0
author: claude-code-skills
license: MIT
tags: [research-ops, market-research, tam-sam-som, market-sizing, survey, sampling, segmentation, competitive-intelligence]
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# market-research

Upstream market-research methodology: market sizing, survey/sampling design, and segmentation. The discipline here is **method + assumptions**: a TAM is never a single number, a survey is never powered only in aggregate, and a segment is never a demographic slice.

## Purpose

Market-research analysts, product marketers, and strategy teams need rigorous evidence *before* anyone optimizes a campaign or sets a strategy. This skill structures three methodology decisions:

Three deterministic tools:

1. `market_sizer.py` — Computes TAM/SAM/SOM by **both** top-down and bottoms-up methods side-by-side, reports the divergence, and flags failed triangulation. 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` — Scores candidate segments against Kotler's five criteria and enforces a substantiality + accessibility gate; a slice that is too small or unreachable is dropped.

## When to use

Invoke this skill when:

- A board or exec asks "how big is this market?" and you need a defensible, triangulated answer.
- You are fielding a survey and need a sample size that holds up per segment, not just overall.
- You have a list of candidate segments and need to know which are real markets vs demographic slices.
- You are synthesizing competitive intelligence and need a methodological backbone.

**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill/campaign-analytics`), build demand-gen / paid-media plans (`marketing-skill/marketing-demand-acquisition`), set positioning / GTM strategy (`marketing-skill/marketing-strategy-pmm`), or set pricing (`commercial/pricing-strategist`).

## Workflow

1. **Write the brief** — Fill `assets/market_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).
2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down/bottoms-up delta before quoting anything.
3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.
4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile <same>`. Drop segments failing the substantiality/accessibility gate.
5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.

## Scripts

| Script | Purpose | Profiles |
|---|---|---|
| `scripts/market_sizer.py` | TAM/SAM/SOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |
| `scripts/sample_size_planner.py` | Survey n + FPC + per-segment minima | n/a (parameter-driven) |
| `scripts