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# 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/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. ```bash 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). ```bash /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 ```bash 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`.