Market Analysis Guide
A comprehensive skill for conducting rigorous market analysis in academic and applied research contexts. This guide covers quantitative market sizing, competitive landscape mapping, and strategic positioning frameworks grounded in peer-reviewed methodologies.
Market Sizing Methodologies
Market sizing is the foundation of any credible market analysis. There are two primary approaches, and robust research typically employs both for triangulation.
Top-Down Approach (TAM/SAM/SOM)
Start with the total addressable market and narrow systematically:
TAM (Total Addressable Market)
-> SAM (Serviceable Available Market)
-> SOM (Serviceable Obtainable Market)
Example calculation:
TAM = Global higher-education EdTech spend = $340B (2025, HolonIQ)
SAM = AI-powered research tools segment = $12B
SOM = Realistic capture in Year 3 = $120M (1% of SAM)
Bottom-Up Approach
Build estimates from unit economics:
# Bottom-up market sizing
users_in_target_segment = 8_000_000 # global PhD + postdoc researchers
adoption_rate = 0.05 # 5% in first 3 years
avg_revenue_per_user = 180 # USD/year
bottom_up_estimate = users_in_target_segment * adoption_rate * avg_revenue_per_user
# Result: $72,000,000
Always cite the data sources for each assumption. Use government statistics (e.g., NSF, Eurostat), industry reports (Gartner, McKinsey), and published academic datasets.
Competitive Analysis Frameworks
Porter's Five Forces
Apply Porter's framework systematically to map industry structure:
SWOT and TOWS Matrix
Go beyond basic SWOT by constructing a TOWS matrix that generates actionable strategies:
Strengths (S) Weaknesses (W)
Opportunities SO strategies WO strategies
(O) (use S to exploit O) (overcome W via O)
Threats ST strategies WT strategies
(T) (use S to counter T) (minimize W, avoid T)
Data Collection and Validation
Primary data collection methods for market analysis research:
- Structured interviews with industry experts (N >= 12 for saturation)
- Survey instruments validated with Cronbach's alpha >= 0.70
- Conjoint analysis for preference and willingness-to-pay estimation
- Web scraping of pricing pages, job postings, and product changelogs
Secondary data sources to cross-validate:
- Statista, IBISWorld, Grand View Research for market reports
- USPTO/EPO patent filings for technology trajectory analysis
- PitchBook/Crunchbase for funding and M&A activity
Reporting and Visualization
Present findings using clear, reproducible visualizations:
import matplotlib.pyplot as plt
import numpy as np
segments = ['Segment A', 'Segment B', 'Segment C', 'Segment D']
sizes = [45, 28, 18, 9]
colors = ['#3B82F6', '#EF4444', '#10B981', '#F59E0B']
fig, ax = plt.subplots(figsize=(8, 6))
ax.barh(segments, sizes, color=colors)
ax.set_xlabel('Market Share (%)')
ax.set_title('Competitive Landscape by Segment')
plt.tight_layout()
plt.savefig('market_share.png', dpi=300)
Always include confidence intervals or sensitivity ranges for quantitative estimates. A well-structured market analysis report should contain an executive summary, methodology section, findings with visualizations, and a limitations discussion.