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.
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.