| name | Market Research |
| slug | market-research |
| description | Size markets, analyze competitors, and validate opportunities with practical frameworks and free data sources. |
Core Framework
Before any research: Clarify the question. "How big is the market?" is useless. "How many [target customers] in [geography] would pay [price] for [solution] annually?" is actionable.
Market Sizing
| Method | Use When | Approach |
|---|
| Bottom-up | You have unit economics | Customers × Price × Frequency |
| Top-down | Exploring new markets | TAM → SAM → SOM with clear filters |
| Comparable | Similar products exist | Competitor revenue × market share estimate |
Free data sources: Government census, industry associations, public company filings (10-K), Statista free tier, Google Trends, LinkedIn Sales Navigator (company counts).
Red flag: If your SAM equals your TAM, you haven't thought hard enough about who actually buys.
Competitor Analysis
- Map the landscape: Direct, indirect, potential entrants
- Mine reviews: G2, Capterra, App Store — recurring complaints = opportunities
- Track signals: Job postings (what they're building), pricing changes, feature launches
- Assess moats: Network effects, switching costs, data advantages, regulatory capture
For detailed competitor frameworks, see competitor-analysis.md.
Customer Validation
Before building: Talk to 20+ potential customers. Use Jobs-to-be-Done or Mom Test frameworks.
Signals that matter: LOIs, prepayments, waitlist-to-signup conversion, repeat usage
Signals that don't: "I'd use that," survey enthusiasm, social media likes
For interview scripts and survey templates, see validation.md.
Common Traps
- Confirmation bias: Seeking data that supports your thesis
- Survivorship bias: Only studying successful companies
- Outdated sources: Tech markets shift in months, not years
- Vanity metrics: Downloads, followers, page views without retention/revenue
Boundaries
- No financial advice: Cannot recommend investments based on market research
- No guarantees: All market forecasts are probabilistic
- Escalate to professionals: For statistically valid primary research, regulated industries, or deep competitive intelligence
