market-research

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: 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. 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). 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 Write the brief — Fill assets/market_research_brief_template.md (objective, the decision this informs, sizing approach, sampling plan, assumptions register). 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. Plan the survey — Run sample_size_planner.py --input survey.json . Fund the per-segment floors, not just the overall n. Score the segments — Run segmentation_scorer.py --input segments.json --profile <same> . Drop segments failing the substantiality/accessibility gate. 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/segmentation_scorer.py Kotler 5-criteria scoring + gate b2b-saas, consumer, enterprise, marketplace, hardware, services All three: stdlib-only, --help , --sample , --output {human,json} . Onboarding & customization Run the onboarding questionnaire once before you start — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior. python3 scripts/onboard.py # interactive (also: --defaults, --set key=value, --reset) python3 scripts/onboard.py --show # see the questions + current effective config Answers are saved to ~/.config/research-ops/market-research.json (global) or ./.research-ops/market-research.json ( --scope project ) and are read automatically by config_loader.py . They set the default market profile , the default survey confidence and margin of error , and the default sizing method . CLI flags always override saved config; RESEARCH_OPS_NO_CONFIG=1 ignores it. The four questions: market profile · survey confidence · margin of error · sizing method. Optimize with autoresearch (opt-in) This skill ships an isolated, opt-in bridge to engineering/autoresearch-agent . Only when you ask to "optimize" / "reconcile the sizing" / "run a loop" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. scripts/ar_evaluator.py is the ground-truth evaluator; it prints tam_divergence: <fraction> ( lower is better). /ar:setup --domain custom --name tam-triangulation \ --target market.json \ -- eval "python3 ar_evaluator.py --target market.json" \ --metric tam_divergence --direction lower /ar:loop custom/tam-triangulation Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits market.json , never the evaluator. References references/market_sizing_canon.md — TAM/SAM/SOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies. references/survey_methodology.md — Cochran Sampling Techniques ; Dillman Tailored Design Method ; Groves Survey Methodology ; question-wording bias (Schuman & Presser); AAPOR standards. references/segmentation_and_ci.md — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint/MaxDiff primer. Assumptions The sizer reports both methods but cannot validate your inputs — a top-down "1% of a $40B market" is only as good as the cited source and the serviceable fraction. Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion. Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence. Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information. Anti-patterns A single TAM number with no method. Always triangulate top-down against bottoms-up. Spurious precision. Size to the decision's tolerance; "$3.7142B" implies a confidence you do not have. Powering only the total. Each reported segment needs its own sample floor. Leading or double-barreled survey questions. Pre-test wording against the bias literature. Calling a demographic slice a segment. It must be substantial AND accessible. Distinct from Neighbor Scope Difference marketing-skill/campaign-analytics Attribution, ROAS, CPA, funnel of a live campaign That measures spend deployed ; this is upstream methodology marketing-skill/marketing-demand-acquisition Demand-gen, paid media, channel mix That runs acquisition ; this builds the evidence marketing-skill/marketing-strategy-pmm Positioning, GTM, category That sets strategy ; this sizes and segments the market commercial/pricing-strategist Pricing model + WTP + packaging That sets price ; this sizes the market product-research (sibling) User/product discovery methods That studies users ; this studies the market Quick examples python3 scripts/market_sizer.py --sample python3 scripts/sample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05 python3 scripts/segmentation_scorer.py --sample --output json The sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the "solopreneurs who might want analytics" slice for failing the substantiality and accessibility gates. Forcing-question library (Matt Pocock grill discipline) Walked one at a time by /cs:grill-research-ops or the orchestrator. Recommended answer + canon citation per question. Never bundled. "Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?" Recommended: both; reconcile the delta before quoting a number. Canon: Bessemer / a16z market-sizing; Fermi estimation. "What decision will this market size actually drive — and at what precision does it matter?" Recommended: size to the decision's tolerance, not to a spurious-precision number. Canon: market-model conventions (Gartner/Forrester); decision-driven analysis. "What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?" Recommended: power each reported segment, not only the total. Canon: Cochran Sampling Techniques ; AAPOR standards. "Are your survey questions free of leading and double-barreled wording?" Recommended: pre-test the wording; cite the bias source. Canon: Schuman & Presser; Dillman Tailored Design Method . "Do your segments pass measurable / substantial / accessible / actionable — or are they just demographic slices?" Recommended: drop segments that fail substantiality or accessibility. Canon: Kotler segmentation criteria. Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke market_sizer.py → sample_size_planner.py → segmentation_scorer.py .