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Market sizing isn't just about coming up with a number—it's about understanding how you arrive at that number and why your methodology matters. Whether you're pitching to investors, planning a product launch, or analyzing competitive landscapes, the technique you choose shapes the credibility and usefulness of your estimate. You're being tested on your ability to select the right approach for different scenarios, defend your assumptions, and recognize the trade-offs between speed and accuracy, data availability and precision, and broad estimates versus granular insights.
These techniques connect directly to core research concepts: primary versus secondary data collection, quantitative versus qualitative analysis, and strategic decision-making under uncertainty. Don't just memorize what each method does—know when to use it, what data it requires, and how it compares to alternatives. That's what separates surface-level knowledge from genuine analytical skill.
The most fundamental decision in market sizing is whether to start big and work down, or start small and build up. Each approach carries different assumptions about data reliability and market structure.
Compare: Top-down vs. bottom-up—both estimate the same market, but top-down prioritizes speed using secondary data while bottom-up prioritizes accuracy through primary research. If asked to justify your estimate, bottom-up provides stronger defensibility; if asked for a rapid assessment, top-down delivers faster.
These techniques use historical patterns and statistical relationships to forecast market size. They assume past performance offers meaningful signals about future behavior.
Compare: Trend extrapolation vs. ratio analysis—both use quantitative data, but extrapolation looks backward at your own market's history while ratio analysis looks sideways at comparable markets or metrics. Use extrapolation for forecasting growth; use ratios for validating assumptions.
These techniques examine how markets are organized—their value chains, segments, and customer journeys—to derive size estimates. They're particularly useful when you need to understand market dynamics, not just totals.
Compare: Segmentation analysis vs. funnel method—segmentation divides the market by who customers are, while the funnel divides by where customers are in their buying journey. Use segmentation for targeting strategy; use the funnel for conversion optimization and realistic demand forecasting.
When direct data is scarce or markets are novel, these techniques leverage analogies and expert judgment. They're invaluable for emerging markets, new technologies, or highly uncertain environments.
Compare: Comparable market analysis vs. Delphi method—comparables use data from other markets while Delphi uses expert judgment from this market. Comparables work when good analogies exist; Delphi works when expertise matters more than historical patterns.
| Concept | Best Examples |
|---|---|
| Speed over precision | Top-down approach, Comparable market analysis |
| Accuracy through primary data | Bottom-up approach, Funnel method |
| Historical/quantitative projection | Trend extrapolation, Ratio analysis |
| Structural market understanding | Value chain analysis, Segmentation analysis |
| Uncertain or emerging markets | Delphi method, Comparable market analysis |
| Validation and reality-checking | Benchmarking, Ratio analysis |
| Customer behavior insights | Funnel method, Segmentation analysis |
Which two techniques would you combine if you needed a quick initial estimate that you could later validate with primary research?
A startup is entering a market with no historical data but several analogous industries exist. Which technique should they prioritize, and what's the main risk?
Compare and contrast the funnel method and segmentation analysis—how do they divide the market differently, and when would you use each?
You're presenting to investors who are skeptical of your market size estimate. Which approach provides the strongest defensibility, and why?
If trend extrapolation shows strong growth but the Delphi method experts predict disruption, how should you reconcile these findings in your final estimate?