# Competitive Moat Analysis > Identifies which of the seven structural moats a business actually has, pressure-tests each claim against funded copycats and incumbent pivots, and delivers a moat scorecard with one moat to invest in deliberately. Use when someone asks "what's our moat", "is our product defensible", "a well-funded competitor just launched - what protects us", "investors keep asking about defensibility", or "which moat should we build first". Do NOT use for tracking what competitors are shipping and saying - use competitive-intelligence instead - or for finding unserved market segments - use white-space-analysis instead. - Skill: `skillmedev/competitive-moat-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add skillmedev/competitive-moat-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/skillmedev/competitive-moat-analysis/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: SkillMedev (https://skillmd.com/u/skillmedev) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/skillmedev/competitive-moat-analysis --- # Competitive Moat Analysis A moat is a structural reason an advantage compounds rather than erodes. Most "moats" founders cite - features, a head start, proprietary data - are things a funded competitor copies in a quarter, and the costly mistake is building a fundraise or a strategy on one of them. This skill separates real moats from wishful thinking and names the one moat worth building deliberately. ## Operating procedure Run taxonomy before testing, and testing before measurement - you cannot stress-test a moat you have not precisely named, and you should not bother measuring one that fails the adversary test. ### Step 1: Gather inputs 1. The claimed advantages - everything the founder or team believes protects them, verbatim. 2. Business model and stage - who pays, how much, how customers onboard and leave. 3. Competitive set - the funded startups and the incumbents, with rough war chests. 4. Retention data if available - gross retention by cohort age; label estimates as guesses. 5. Pricing power evidence - has a price increase ever stuck without churn spiking? ### Step 2: Map each claim to the seven real moats Every claimed advantage must land in exactly one of these buckets or be reclassified as "feature, not moat": 1. Network effects - each user makes the product more valuable to others. Defensibility test: does value scale with n, with n-squared, or not with n at all? A social graph is n-squared; a leaderboard is barely n. 2. Switching costs - leaving is expensive in time, data, or risk. Test: what, concretely, would a customer lose by churning on Monday morning? 3. Scale economies - unit costs fall with volume to a level a smaller rival structurally cannot reach. 4. Counter-positioning - incumbents cannot copy the model without damaging their existing business. The strongest moat against giants. Test: name the specific revenue line the incumbent protects by NOT copying you; if you cannot name it, the counter-positioning is imagined. 5. Cornered resource - exclusive access to talent, IP, data, or supply. Exclusivity must be contractual or structural, not just current. 6. Brand - customers pay more or choose faster because of trust, not features. Test: demonstrated pricing power, not awareness metrics. 7. Process power - an organizational capability rivals cannot replicate even knowing how it works. Rare; Toyota-class. Default skepticism. ### Step 3: Run the adversary tests For each surviving claim, run all three: - The funded copycat: a competitor with $50M and your full feature list launches tomorrow. What still protects you? If the answer is nothing, it was a feature, not a moat. - The incumbent pivot: the market leader decides you are a threat. Which moats survive their distribution advantage? (Usually only counter-positioning and genuine network effects.) - The time test: does the moat strengthen or weaken with each passing quarter? Real moats compound; a head start decays. ### Step 4: Measure what survived - Network effects: correlation between cohort size (or network density) and retention. Retention that is flat regardless of network size means no network effect. - Switching costs: gross retention of multi-year cohorts. Multi-year gross retention above ~90% annually is consistent with real switching costs; below 80%, the switching-cost claim is dead on arrival. - Scale: model your cost curve against a sub-scale competitor's at their volume, not yours. - Counter-positioning: the named incumbent revenue at risk, in dollars. - Brand: a price increase that stuck. Anything else is marketing spend, not brand. ### Step 5: Score and choose Rate each of the seven types absent / emerging / strong with the evidence and the adversary-test result. Then pick the single moat to invest in over the next year. If every row reads "absent," say so plainly - that is the most important strategic finding, not a failure of the analysis. Sequence the model to create network effects or switching costs early, before competition arrives. ## Worked artifact: scorecard contrast Bad: ``` Moats: first mover, great team, AI-powered, proprietary data, strong brand. ``` Every item fails Step 2: none is one of the seven, "proprietary data" has no flywheel stated, and "brand" has no pricing-power evidence. Good: ``` MOAT SCORECARD - B2B data platform, Series A Network effects EMERGING Shared benchmarks improve with each customer; retention +6pts for customers in 3+ benchmark pools vs solo. Survives copycat (data can't be bootstrapped fast) - weak vs incumbent w/ install base. Switching costs STRONG 3yr cohorts at 94% gross retention; migration = 6 months of pipeline history lost. Survives all three tests. Scale economies ABSENT Cloud costs linear with usage. Counter-positioning EMERGING Incumbent charges per-seat ($240M line); our usage pricing cannibalizes it. Named revenue: yes. Cornered resource ABSENT No exclusive contracts. Brand ABSENT No demonstrated pricing power yet. Process power ABSENT Default. INVEST: deepen switching costs - ship workflow embeds that make Monday-morning churn cost a quarter of rework. Re-test in 12 months. ``` ## Deliverable Produce a moat scorecard: each of the seven types rated absent / emerging / strong, with the evidence, the metric from Step 4, and the result of all three adversary tests - concluding with the one moat to invest in deliberately over the next year and the specific mechanism for deepening it. ## Do NOT - Do not accept a head start as a moat; being first only matters if it is being converted into one of the seven. - Do not claim brand without pricing power to prove it - awareness is not a moat. - Do not treat proprietary data as a moat by default; it only qualifies if the data improves the product in a flywheel competitors cannot bootstrap. - Do not let more than two moats be rated "strong" without exceptional evidence; real strong moats are rare, and a scorecard full of them means the tests were soft. - Do not skip the "none yet" verdict to spare feelings - a false moat in a board deck or fundraise narrative is worse than an honest gap. ## Quality bar - Every claimed advantage is either mapped to one of the seven types or explicitly reclassified as a feature. - Every non-absent rating cites evidence a skeptic could check, and every "strong" rating survived all three adversary tests. - Counter-positioning claims name the specific incumbent revenue protected by not copying. - The scorecard ends with exactly one moat to invest in and the concrete mechanism. - Guessed numbers are labeled as guesses. ## Escalation For monitoring what competitors actually ship and say, route to competitive-intelligence; if the moat verdict feeds a fundraise story, pair with fundraising-narrative.