/competitor-analysis

description: Deep competitive moat analysis, market position assessment, and industry dynamics for one company against its direct rivals. To map an industry's supply / value chain and find chokepoints, use industry-map

Competitor Analysis

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest market data for the ticker:

1. **Fetch current price** — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data. 2. **Confirm key figures** — recent earnings, revenue, key ratios (P/E, P/S, etc.) as applicable to this skill. 3. **State your data source** — fill in the `Data & Sources` header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. 4. **Flag stale data explicitly** — if live data is unavailable, display this warning before proceeding:

> ⚠️ **Live data unavailable.** The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.

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📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the [Data & Accuracy](https://yennanliu.github.io/InvestSkill/data-and-accuracy.html) page and the first thing `result-validator` looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>

• `Retrieval: model memory` must be paired with `Confidence: LOW` — memory is a placeholder until confirmed against a primary source.
• Mixed sources: list each with its own as-of date rather than blending them.
• Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
• Data the user pasted is reported as `pasted by user`; do not upgrade its confidence beyond what the user's own source supports.

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Conduct deep competitive moat analysis to assess whether a company has a durable competitive advantage, how wide that moat is, and what the competitive dynamics of its industry mean for long-term investment returns.

Overview

Competitive analysis answers the fundamental question: **"Does this company have a durable competitive advantage, and how wide is its moat?"** This directly determines the appropriate valuation premium or discount vs. the sector.

A company with a wide, widening moat deserves a premium P/E and P/FCF multiple because its excess returns on capital are durable. A company with no moat, or a narrowing moat, should trade at or below sector multiples regardless of near-term earnings momentum. Understanding the moat is the single most important determinant of a stock's long-term investment return — more important than any individual quarterly earnings figure.

This skill provides a structured, repeatable framework for moat identification, industry attractiveness scoring, competitive benchmarking, and innovation positioning. Output feeds directly into `/dcf-valuation` (to set appropriate WACC and terminal growth rate) and `/fundamental-analysis` (to contextualize ROIC and margin trends).

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1. Moat Identification Framework

Five Sources of Economic Moat (Morningstar Framework)

**1. Network Effects** Value increases with each additional user or participant in the platform or network.

• Examples: Visa, Mastercard (payment networks), Meta (social graph), Airbnb (marketplace), Microsoft 365 (collaboration network)
• Test: Does adding users benefit existing users? Does the network become more valuable as it scales?
• Signs of network effects: Organic user growth with low CAC, high retention as network grows, winner-take-most dynamics

**2. Cost Advantages** Structural ability to produce goods or services at lower cost than competitors.

• Sources: Scale advantages (fixed cost leverage), process innovation (proprietary manufacturing), geographic advantage (proximity to inputs), unique asset access (mining rights, owned infrastructure)
• Examples: Costco (buying scale + lean operations), Amazon (logistics scale), Nucor (mini-mill process innovation)
• Test: Can competitors replicate this cost structure at competitive cost? What would it cost to build?

**3. Intangible Assets** Brands, patents, regulatory licenses, or proprietary data that competitors cannot easily copy.

• Brand: Can the company charge a premium price based on brand perception alone? (Apple, LVMH, Coca-Cola)
• Patents: How many years of exclusivity remain? Is the IP portfolio broad or narrow?
• Regulatory licenses: Are licenses scarce, non-transferable, or expensive to obtain? (broadcast licenses, pharmaceutical approvals, financial licenses)
• Proprietary data: Is the data asset self-reinforcing and competitively irreplicable?
• Test: Can the company charge premium prices, or does the intangible give exclusive market access?

**4. Switching Costs** High cost — financial, operational, or psychological — for customers to change providers.

• Financial switching costs: Contractual lock-in, migration costs, retraining costs
• Operational switching costs: Deep workflow integration, data portability limitations
• Psychological switching costs: Brand loyalty, habit formation, trust
• Examples: Oracle (ERP deeply embedded in operations), Salesforce (CRM data and workflow integration), Adobe (creative suite skill investment)
• Test: What % of cust