When the taxonomy fits
Use it when
- A valuation or thesis assumes returns persist despite competition.
- Management describes a moat without a measurable mechanism.
- The decision needs monitoring indicators after investment.
Do not force it when
- The advantage is temporary execution speed and is underwritten as such.
- The business is too early for persistence evidence.
- Regime change makes historical behavior a poor guide.
How to use the taxonomy
1. Name the mechanism
"Proprietary technology," "data advantage," and "market leadership" are descriptions, not mechanisms. Ask why another firm cannot reproduce the value, access, cost, or trust that customers receive. A data advantage may be a network effect if use improves the product for other users, a switching cost if histories are hard to move, or no moat if a competitor can buy equivalent data.
2. State the claim so evidence can falsify it
A network-effect claim should predict better engagement or retention as network density grows. A switching-cost claim should predict measurable migration burden and resilience through price changes. A scale claim should predict a cost curve unavailable to smaller rivals. A brand claim should predict buyer behavior, not awareness alone. A regulatory claim should survive a funded entrant's realistic path to approval.
3. Test depth, scope, and durability separately
Depth asks how much economic advantage exists. Scope asks which customers, geographies, or workflows receive it. Durability asks how long the mechanism can persist against technology, standards, regulation, supplier change, and competitor investment. A moat can be deep in one local network and absent elsewhere.
4. Write the erosion watchlist
Monitor the mechanism rather than waiting for reported margin to fall. Track multi-homing, migration effort, price realization, cost-curve convergence, win rates, regulatory dockets, and other mechanism-specific signals. Predefine which movement changes confidence and which movement changes the action.
Five defensibility mechanisms
| Mechanism and claim | Evidence test | Leading erosion indicators |
|---|---|---|
| Network effects: each additional participant increases value for others. | Cohort engagement, liquidity, match quality, or retention improves with relevant network density. Separate local density from total user count. | Multi-homing rises, dense clusters churn, or an entrant reaches critical mass in a valuable segment. |
| Switching costs: leaving imposes money, risk, data, workflow, or retraining cost. | Quantify migration effort and failure risk. Test renewal and expansion through price changes, plus integration depth and buyer dependence. | Migration tools improve, data standards emerge, renewal negotiations lengthen, or champions lose budget influence. |
| Scale economies: unit costs fall with volume in a way rivals cannot readily match. | Observe the cost curve by volume and identify the inaccessible input, density, supply, or learning mechanism behind it. | The cost gap narrows, suppliers consolidate, input access commoditizes, or a platform shift resets the cost base. |
| Brand: the name changes buyer behavior, price, or trust. | Compare realized price, unaided consideration, and win rates against functionally similar alternatives, controlling for product and channel. | Discounting rises, head-to-head win rates normalize, or the premium survives only in legacy segments. |
| Regulatory position: approval, license, or compliance infrastructure delays entry. | Price the time, capital, expertise, and probability for a determined entrant to cross the barrier. Read the direction of rule change. | Rules simplify, approvals accelerate, incumbents lobby defensively, or compliance becomes packaged infrastructure. |
A workflow platform claims high switching costs
A private software company reports 94% gross revenue retention and says customers are locked in by integrations and historical records. Diligence finds that a typical migration requires about 120 staff hours, four system integrations, and several weeks of parallel operation. That supports real switching friction.
The claim is not yet established. Retention has not been observed through a meaningful price increase, one integration accounts for most of the migration effort, and a competitor has introduced an import utility. The moat is graded probable and narrow: strongest for mature, multi-integration customers. The watchlist tracks import completion time, competitive win and loss reasons, renewal concessions, and retention after repricing.
Evidence requirements
Prioritize behavioral and economic evidence over management labels. Useful material includes raw retention and expansion cohorts, realized pricing and discount history, win and loss records, migration plans, integration maps, unit costs by volume, customer interviews sampled beyond management references, competitor tooling, and current regulatory material. Link each claim to its originating evidence, note incentives and limitations, and distinguish independent corroboration from repeated reporting.
Failure modes
Moat counting: several shallow labels are treated as stronger than one tested mechanism. Scale as network: a large user base is assumed to make the product better for each user. Retention circularity: retention proves the moat, and the moat explains retention, with no mechanism. Gross margin as durability: a current outcome is mistaken for a protected cause. Customer-selection bias: only enthusiastic references are heard. Static regulation: today's license is valued without examining how the rule may change.
Diligence checklist
- Every defensibility claim maps to a named economic mechanism.
- The mechanism produces a falsifiable prediction in customer or cost behavior.
- Evidence is segmented by the customers and markets where the moat is claimed.
- Depth, scope, and expected duration are assessed separately.
- A funded competitor's credible response is considered.
- Alternative explanations for retention, margin, or growth remain visible.
- Erosion indicators, review cadence, and downgrade rules are pre-registered.
Limitations
Defensibility is a forecast under uncertainty. Early businesses may lack enough history to test persistence, while historical evidence can become stale after a platform, regulatory, or distribution change. Several mechanisms can interact, and a strong moat does not guarantee attractive valuation, governance, execution, or returns. The taxonomy structures judgment; it does not produce an investment recommendation.
Related services, cases, and Insights
Turn the moat claim into a test
We can connect defensibility to observable evidence, valuation drivers, and a monitoring watchlist.