Market Research

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

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

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.

1. "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.

2. "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.

3. "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.

4. "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.

5. "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.

Source: alirezarezvani/claude-skills → research-ops/skills/market-research/SKILL.md