Market Research

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

1. Map the landscape: Direct, indirect, potential entrants

2. Mine reviews: G2, Capterra, App Store — recurring complaints = opportunities

3. Track signals: Job postings (what they're building), pricing changes, feature launches

4. 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• -Outdated sources: Tech markets shift in months, not years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