Descriptive Findings (30 Companies)

When you need this method

Debates about defensibility in B2B software tend to orbit technology and features. The empirical question is: where do the provable moats actually sit when a larger group of funded or acquired companies is scored systematically and blind to outcome? The answer shifts what leadership teams and investors should pay attention to.

Approach

1. 1Use the finding as a prior for your own analyses: in this population, defensibility most often arises through demand (frequently regulation-driven) and through compound (switching costs, retention).
2. 2Expect little blind-provable strength in the economics layer; for private companies it was rarely demonstrably positive.
3. 3Scrutinize technology claims accordingly: a feature edge without distribution or retention effect is rarely a moat.
4. 4Treat the finding as a market statement; for the individual company, the constraint scan remains the tool.
5. 5Update the prior as new scored cohorts come in.

Limits and counter-indications

The finding rests on 30 German B2B SaaS companies from a specific time window; it is a structural statement about that population, not a universal rule. It does not discriminate between individual companies and does not replace case-by-case analysis. The weak visibility of the economics layer partly reflects data availability at private companies, not necessarily actual weakness.

How to measure impact

Distribution of strongest layers across a scored group of companies, such as the share with provably strong demand, compound or economics layers.

Related methods

• Constraint Detection (Five-Layer Scan)
• Five-Layer Vocabulary as a Checklist
• Depth-to-Multiple Pattern