For traditional businesses—like a local bakery or a manufacturing plant—figuring out « what it’s worth » is a matter of looking at past tax returns and physical assets. But how do you put a price tag on a company that has no revenue, no factory, and perhaps only a few lines of code and a dream?
The central problem is that traditional valuation methods cannot be applied to startups. Financial experts call this « Information Asymmetry »—the founders know the risks, but the investors are in the dark. To bridge this gap, Venture Capitalists (VCs) across the globe have had to innovate.
Here is a look at the main methods VCs use to decide which startups get funded and at what price.
1. The Human « Signaling » Approach (North America & Europe)
In the early stages (Pre-seed and Seed), VCs aren’t just buying a business; they are buying a team. Since there are no financial statements to audit, investors look for « Signals. »
The Integrated Strategic Model focuses on the « Human Capital. » VCs look at the founders’ education, their previous industry experience, and whether they have successfully started a company before. As noted in a major study, « Attractiveness of the industry and quality of the management team significantly and positively affect valuation » (Miloud et al., 2012). In simple terms: a great jockey on a fast horse is worth more than a great jockey on a slow one.
2. The Milestone Scorecard (Asia & Global)
In emerging markets like Indonesia, where economic data can be less predictable, VCs often use the Berkus Method or the Scorecard Method.
Instead of guessing future profits, these models assign a dollar value to specific achievements. If you have a working prototype, that’s $500,000 in value. If you have a world-class board of advisors, that’s another $500,000. This « notation » system allows investors to be objective. Research highlights that these methods help close the « Valuation Gap »—the difference between what a founder thinks they are worth and what the market is willing to pay (Hayatul & Rahadi, 2023).
3. The « Tryptyque » of Trust (France & Europe)
In Europe, particularly in the French ecosystem, notation models focus on reducing risk through a three-pillared approach: Human, Market, and Governance.
The goal here is to create a « standard » for quality. By having a third party « note » or rate a startup on these three areas, the time it takes for an investor to do their homework (Due Diligence) can be reduced by more than 50% (Abdesslam & Le Pendeven, 2022). It shifts the conversation from « I think this is a good idea » to « This company meets the professional standards for governance and market depth. »
4. Real-Time Data & The « SURF » Score (Fintech)
The newest innovation in funding doesn’t rely on pitch decks at all. In the Fintech world, notation models are becoming dynamic.
Models like the SURF Score (Sustainability, Underwriting, Risk, and Financial) use API links to a company’s bank account and accounting software to see exactly how much money is coming in and out every day. This allows for a « Continuous Audit, » where the company’s « note » changes in real-time based on actual performance (Bouheni et al., 2025).
5. The Real Options Approach (High-Tech & AI)
For complex sectors like Artificial Intelligence or Biotech, VCs use the Real Options Method (ROM). This treats a startup like a « financial option. » The VC isn’t just buying the company today; they are buying the right to invest more later if the company hits a specific technical milestone. This model values flexibility—the ability of a founder to « pivot » or change direction when new technology emerges (Akkaya, 2020).
A Work in Progress
While these methods—ranging from human-centric signals to real-time algorithmic scoring—have allowed the venture capital industry to thrive, they are still incomplete.
No single model can perfectly predict the future. Valuation remains a mix of « Art » and « Science. » As the global economy becomes more digital and intangible assets (like brands and code) become more important than physical assets, these notation models require constant improvement, better data transparency, and more rigorous standards to ensure that investments remain both sound and safe for the long term.
Key Words for
- Information Asymmetry: When one party has more information than the other (the core problem VCs solve).
- Signaling Theory: Using observable qualities (like an MBA or a prototype) to prove unobservable quality.
- Unit Economics: The direct revenues and costs of a single customer (e.g., LTV and CAC).
- Due Diligence: The « homework » an investor does to verify a startup’s claims.
References
- Abdesslam, M. and Le Pendeven, B. (2022) ‘Les enjeux de la notation des start-up en phase de démarrage’, Revue internationale P.M.E., 35(1).
- Akkaya, M. (2020) ‘Startup Company Valuation: The State of Art and Future Trends’, International Business Research, 13(9).
- Bouheni, F. B. et al. (2025) ‘Credit Sales and Risk Scoring: A FinTech Innovation’, Journal of Risk and Financial Management, 4(3).
- Hayatul, Y. and Rahadi, R. A. (2023) ‘Startup Valuation Methods: A Literature Review’, Himalayan Journal of Economics and Business Management, 4(1).
- Miloud, T., Aspelund, A. and Cabrol, M. (2012) ‘Startup valuation by venture capitalists: an empirical study’, Venture Capital, 14(2-3).
IA
