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Market researcher

Skill NickCrew/Claude-Cortex/skills/market-researcher

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Use this skill when sizing a market, analyzing competitors, designing customer surveys, segmenting audiences, or synthesizing research into market insights. Trigger phrases: 'size the market for', 'analyze our competitors', 'who is our target customer', 'design a survey to understand', 'TAM/SAM/SOM for'. Not for building financial models, writing pitch decks, or conducting UX usability research.

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SKILL.md

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Market Researcher

Overview

This skill guides structured market research—from estimating market size using TAM/SAM/SOM through primary and secondary research design, customer segmentation, survey construction, and competitive landscape analysis. It turns ambiguous market questions into defensible, data-backed conclusions that inform strategic decisions about where to play and how to win.

When to Use

  • Sizing a new market opportunity before investing in product development
  • Analyzing competitor strengths, weaknesses, and positioning
  • Designing a customer survey to understand needs or validate assumptions
  • Segmenting a customer base to find the most valuable groups
  • Preparing market analysis for a pitch deck, board presentation, or strategic plan
  • Understanding why customers choose or leave a product

When NOT to Use

  • Conducting usability testing or user interviews about product UX (use ux-researcher skill)
  • Building detailed financial models or revenue projections (use a finance skill)
  • Writing the full pitch deck (use pitch-deck-writer skill)
  • Real-time social media monitoring or sentiment analysis

Quick Reference

Research TaskMethodTime Required
Market sizingTAM/SAM/SOM (top-down + bottom-up)2–8 hours
Competitor analysisFramework + web research4–12 hours
Customer needs8–12 in-depth interviews2–3 weeks
Hypothesis validationSurvey (n=200+)1–2 weeks
Customer segmentationSurvey + cluster analysis2–4 weeks
Positioning mapPerception survey or desk research1–3 days
Secondary researchReports, databases, news2–8 hours

Instructions

Step 1: Define the Research Question

Before gathering data, write a single crisp research question:

  • "What is the addressable market for AI-powered legal contract review in the US?"
  • "Why do users cancel within 30 days of signing up for our product?"
  • "How does our product compare to competitors on features and pricing?"

Then list 3–5 sub-questions that, if answered, would answer the main question.

Step 2: Market Sizing — TAM / SAM / SOM

Definitions:

  • TAM (Total Addressable Market): Total revenue available if you captured 100% of the market
  • SAM (Serviceable Addressable Market): The portion you can realistically target given your business model, geography, and product
  • SOM (Serviceable Obtainable Market): What you can realistically capture in 3–5 years

Two approaches to triangulate:

Top-Down (use industry reports):

TAM: Find total industry revenue from analyst reports (Gartner, IDC, Statista)
     Example: "Global legal tech market: $29B (2024)" → TAM = $29B

SAM: Apply your segment filters
     "AI-specific legal tech, US only, mid-to-large law firms" = 15% of global market
     SAM = $29B × 15% = $4.4B

SOM: Apply your achievable market share
     "Realistic 3% capture in 5 years" → SOM = $4.4B × 3% = $132M

Bottom-Up (use unit economics):

# Count the buyers × their spend

Target customers: US law firms with 50+ attorneys = 8,000 firms
Average annual contract value (ACV): $25,000
Total SAM = 8,000 × $25,000 = $200M/year

SOM: Win 500 firms in 5 years → 500 × $25,000 = $12.5M ARR

Best practice: Use both approaches; if they're within 2× of each other, your estimate is credible. If they diverge more, investigate why.

Data sources for market sizing:

  • Gartner, Forrester, IDC, Grand View Research (paid)
  • Statista, IBISWorld (paid, often available via library)
  • Census Bureau, BLS, SEC filings (free)
  • LinkedIn Sales Navigator (estimate company counts)
  • Crunchbase, PitchBook (funding and revenue signals)
  • Job posting counts (proxy for company growth in a segment)

Step 3: Primary vs Secondary Research

Secondary research (desk research — start here):

  • Industry analyst reports (Gartner Magic Quadrant, Forrester Wave)
  • Competitor websites, pricing pages, job postings, press releases
  • App store reviews of competitor products
  • Reddit, Twitter, G2, Capterra, Trustpilot — customer voice
  • Government databases (Census, USPTO, SEC EDGAR)
  • Academic papers, conference proceedings

Primary research (you collect — for validation and nuance):

MethodBest ForSample Size
In-depth interviewsDeep understanding of motivations8–15
Online surveysQuantifying preferences, segmentation200–1,000+
Focus groupsConcept testing, early ideation2 groups of 6–8
Observational/ethnographyUnderstanding actual behavior5–10 sessions
A/B testsValidating specific hypotheses1,000+ per variant

Step 4: Survey Design

A good survey:

  1. Takes < 10 minutes (15 questions max)
  2. Asks one thing per question
  3. Progresses from general to specific
  4. Uses consistent rating scales (always 1–5 or always 1–7; never mix)
  5. Ends with demographics and open-ended "anything else?"

Question type guide:

  • Multiple choice (single): When answers are mutually exclusive ("Which best describes your role?")
  • Multiple choice (multi-select): "Which of these tools do you use?" (check all that apply)
  • Likert 1–5: Agreement, satisfaction, frequency
  • Ranking: "Rank these features from most to least important" (max 5 items)
  • NPS (0–10): "How likely are you to recommend us?"
  • Open-ended: "What is the biggest challenge you face with X?" (use sparingly, 1–2 max)

Sample survey structure:

Section 1: Screener (1–2 questions to qualify respondents)
Section 2: Current behavior and pain (3–4 questions)
Section 3: Product/solution fit (3–4 questions)
Section 4: Competitive usage and preferences (2–3 questions)
Section 5: Willingness to pay / pricing (1–2 questions)
Section 6: Demographics (2–3 questions)

Step 5: Customer Segmentation

Segment your market on dimensions that predict purchase behavior:

B2B segmentation dimensions:

  • Company size (employees, revenue)
  • Industry vertical
  • Geography
  • Tech stack / sophistication
  • Buying process (self-serve vs sales-led)
  • Use case (primary job to be done)

B2C segmentation dimensions:

  • Demographics (age, income, education)
  • Psychographics (values, lifestyle, attitudes)
  • Behavioral (usage frequency, purchase history, NPS)
  • Geography

Segmentation output template:

SegmentSizeDescriptionPrimary NeedChannelACV
Enterprise Legal2,000 firms500+ attorneys, dedicated ITCompliance automationSales-led$80K
Mid-Market Legal6,000 firms50–500 attorneys, cost-sensitiveTime savingsPLG + inside sales$20K
Solo/Small Firm50,000 firms<50 attorneys, price-sensitiveAffordable AI assistanceSelf-serve$2K

Step 6: Competitive Landscape Analysis

Analyze 5–8 direct and indirect competitors across:

Feature matrix:

FeatureYour ProductCompetitor ACompetitor BCompetitor C
Feature 1
Feature 2
Pricing$X/mo$Y/mo$Z/mo$W/mo
Target segmentMid-marketEnterpriseSMBMid-market

Positioning map (2×2 matrix with two dimensions):

  • X-axis: Price (budget → premium)
  • Y-axis: Ease of use (complex → simple)
  • Plot each competitor as a dot; find whitespace = your opportunity

SWOT analysis per competitor:

  • S: What do they do best? (customer reviews, investor narratives)
  • W: Where do they fall short? (negative reviews, high churn signals)
  • O: What market trends help them?
  • T: What could hurt them (you, regulation, substitutes)?

Examples

Example 1: Size the US Online Education Market

Research question: What is the market size for AI-powered corporate learning platforms in the US?

Top-down approach:

Global corporate e-learning market (2024): $50B (Grand View Research)
US share: ~35% → $17.5B US market
AI-enhanced segment: ~20% of corporate e-learning → $3.5B SAM
Target: Mid-to-large enterprises (1,000+ employees) = 40% of market → $1.4B

Realistic 4-year market capture at 2% = $28M ARR

Bottom-up approach:

US companies with 1,000+ employees: ~19,000 (BLS data)
Estimated 25% currently buying L&D platforms: 4,750 companies
Average L&D platform spend: $80K/year
Total SAM: 4,750 × $80K = $380M (conservative; AI premium not modeled)
SOM at 1.5% capture: ~70 companies → $5.6M ARR in Year 3

Synthesis: Top-down gives $28M, bottom-up gives $5.6M—roughly a 5× gap. Investigation reveals the top-down estimate includes training content production budgets, not just platform software. Adjusting the top-down scope brings both estimates to $15–25M TAM for a standalone AI platform. Credible SOM: $5–10M ARR by Year 4.


Example 2: Analyze Competitor Positioning for a Project Management Tool

Research question: How does our new project management tool compare to Asana, Monday.com, and Linear?

Research methods used: Competitor websites, G2/Capterra reviews (top 50 for each), App Store reviews, job postings (signal for engineering investment), pricing pages.

Findings summary:

DimensionOur ToolAsanaMonday.comLinear
Target userDeveloper teamsMarketing/opsAny teamEngineers
Core strengthGitHub integrationWorkflow automationCustomizationSpeed & simplicity
Pricing (team plan)$12/user/mo$13.49/user/mo$12/user/mo$8/user/mo
Key complaint (G2)"Missing Gantt view""Too complex""Expensive at scale""Too dev-focused"
AI features✅ native⚠️ limited⚠️ limited

Positioning gap identified: No competitor strongly serves mixed teams (engineering + product + design) with deep GitHub integration + non-developer accessibility. This is the whitespace.

Recommendation: Position as "the project management tool for product teams that ship software"—bridging engineering (GitHub) and business stakeholders (no-code views, status reports).

Best Practices

  • Triangulate market size with two methods (top-down + bottom-up) and explain any large gaps
  • Primary research validates secondary research; never rely on one source alone
  • For surveys, pilot test with 5 people before full launch; fix confusing questions
  • When analyzing competitors, focus on customer reviews for weaknesses—competitor websites only show strengths
  • Segment by behavior, not just demographics; two people with the same age can have very different buying behavior
  • Make assumptions explicit: "We assume 15% of the market is addressable given our current integrations"
  • Research findings should lead to a recommendation, not just a data dump

Common Mistakes

  • Reporting TAM as the investment opportunity (it's not; SOM is)
  • Conflating total industry spending with the addressable software market
  • Survey bias: leading questions, or surveying only existing happy customers
  • Treating competitor feature lists at face value without talking to their customers
  • Doing only secondary research for important decisions (desk research has survivorship bias)
  • Forgetting to validate willingness to pay—a large market of people who won't pay is worthless
  • Confusing market size with market demand (a market can be large but already saturated)

Tips & Tricks

  • G2 and Capterra reviews are gold mines—read the 3-star reviews for honest trade-offs
  • LinkedIn company search filters (industry + size) let you count companies in a segment for free
  • App store reviews sorted by "most recent, 1–2 stars" shows a competitor's current problems
  • The "jobs to be done" (JTBD) framework is the best mental model for understanding why customers buy
  • Always ask survey respondents "why?" after a rating—open text explains the number
  • Job postings reveal where competitors are investing: 10 new ML engineer listings signals an AI product push

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