skills/43-wentorai-research-plugins/skills/domains/business/market-analysis-guide/SKILL.md
Structured frameworks for market sizing, competitive analysis, and strategic ...
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research market-analysis-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 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.
Apply Porter's framework systematically to map industry structure:
| Force | Key Questions | Data Sources | |-------|--------------|--------------| | Rivalry | How many direct competitors? Market concentration (HHI)? | Crunchbase, SEC filings | | New Entrants | Capital requirements? Regulatory barriers? | Patent databases, regulatory filings | | Substitutes | What alternatives exist? Switching costs? | User surveys, app store data | | Buyer Power | Customer concentration? Price sensitivity? | Industry reports, interviews | | Supplier Power | Input scarcity? Vendor lock-in? | Supply chain databases |
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)
Primary data collection methods for market analysis research:
Secondary data sources to cross-validate:
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.
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.