An interactive web platform for strategic valuation of Italian small and medium enterprises (SMEs). Answers the question: "What is my business worth, what drives that value, and how can I grow it?"
You might ask: why not simply value / max_value? For example, 16% / 40% = 0.4.
The answer is that business value does not grow linearly with margins. The difference between a 5% and 10% EBITDA margin is enormous in terms of business health. The difference between 25% and 30% is far less significant. Thresholds capture this real non-linearity — which is why we follow the Altman Z-Score model, which uses exactly the same approach.
Client Concentration and Founder Dependency are inverse measures because they measure risk, not quality. Higher values are worse for the business:
- Concentration 55% → normalized score 0.3 (high risk)
- Concentration 15% → normalized score 1.0 (low risk)
Without inverting, a company with 90% of revenue from a single client would receive a high score — the opposite of what the model should communicate.
The honest answer: there are no "scientifically certified" weights for this specific model. No one has them. Even professional business valuation models use weights derived from a mix of literature, empirical experience, and defensible methodological choices.
What matters is not having the "right" weights — it's being able to justify them with coherent logic.
| Variable | Weight | Why |
|---|---|---|
| EBITDA Margin | 30% | The most direct measure of operating profitability — the foundation of any valuation |
| Revenue CAGR | 25% | Growth trajectory directly impacts the multiple a buyer is willing to pay |
| Recurring Revenue | 25% | Recurring revenue = predictability = reduced risk for the buyer. Equals CAGR in importance |
| Client Concentration | 20% | It's a risk, not a strength — lower weight than the others but a critical signal |
Why these four? We follow Altman Z-Score logic: select a few high-signal variables instead of many redundant ones. As your professor said: "The smartest model is not the one with the most variables, but the one that selects the most relevant ones."
| Variable | Weight | Why |
|---|---|---|
| Digital Maturity | 40% | The enabling condition — without digitalization there is no automation or scalability |
| Tech Investment | 35% | The concrete, measurable signal of the company's technological commitment |
| Scalability | 25% | Partly dependent on digital maturity — lower weight to avoid double-counting |
| Variable | Weight | Why |
|---|---|---|
| Founder Dependency | 40% | The single most penalizing factor in due diligence — a buyer wants a business that works without the founder |
| Management Structure | 35% | Managerial team = autonomous execution capacity |
| Client Portfolio Quality | 25% | Proxy for the quality of commercial relationships managed by the team |
Logic behind the 40/35 split: inspired by literature on business transferability — the central theme in pre-exit valuations.
| Variable | Weight | Why |
|---|---|---|
| Network Strength | 60% | The only variable that directly measures the network — it must dominate |
| Client Concentration | 40% | Used as a proxy: depending on few clients also means weak relational networks |
The most effective answer to a jury:
"The weights are not arbitrary — they follow three hierarchical principles. First: objective and verifiable variables outweigh declared ones. Second: factors that directly impact business transferability outweigh contextual factors. Third: we avoided double-counting by assigning lower weights to variables already indirectly captured by others."
The three categories now modify the Live Score Preview. Here's what changed:
- Tech Focus (green) — proxy for Technological Capital; responds to Digital Maturity (weight 40/65) and Model Scalability (weight 25/65), normalized to available weights (excluding tech_investment, which is not a form input)
- Relational Focus (purple) — proxy for Relational Capital; responds to Network Strength (60%) and Client Concentration (40%), mirroring the backend formula exactly
The normalization thresholds are empirically calibrated on a sample of 4,184 Italian SMEs (ATECO 62 sector) extracted from AIDA — Bureau van Dijk, April 2026. The median ROS (Return on Sales) for the sector is 5.79%, significantly lower than generic literature assumptions — this demonstrates why a model calibrated to the Italian context is necessary.
- Backend: Python, FastAPI
- Frontend: React 18, Vite, Tailwind CSS, Framer Motion
- Data: AIDA / Bureau van Dijk (Italian SME benchmarks)
- Database: Supabase (PostgreSQL)
See CLAUDE.md for detailed architecture and setup.