Berkus, Scorecard and Risk Factor Summation: Valuing a Pre-Revenue Startup

Introduction: how do you value a startup that has no revenue yet?

How do you put a price on a company that has no revenue, no track record and no reliable financial plan? That is the question hundreds of seed-stage founders and the business angels considering backing them ask themselves every week. Classic financial methods, discounted future cash flows or market multiples, require data that these young companies simply have not produced yet. As early as the 1990s, North American business angel networks therefore developed qualitative scorecards capable of attributing a reasonable value to a team, an idea and a few early traction signals, without waiting for the first audited accounts.

"Fewer than one startup in a thousand meets or exceeds the revenue projections it announced.", Dave Berkus, creator of the eponymous method, in the 2024 update of his methodology published by the Angel Capital Association.

This quote sums up the whole tension of seed investing: founders' spreadsheets are, by construction, unverifiable assumptions, and an investor who based their decision on these projections alone would be taking a disproportionate risk. Three answers to this problem eventually became standard practice among American, then European and Swiss business angel networks, each with its own logic: the Berkus method, which attributes a capped amount to each risk-reduction factor; the Scorecard method, which adjusts an average regional market valuation using a series of weightings; and the Risk Factor Summation, which starts from a reference valuation and adjusts it point by point across twelve risk categories. These three methods rarely converge on an identical figure, which is precisely what makes them a discussion tool rather than a universal calculator.

This article details, in order, the definition of each method, their shared origin in the practice of American angels, why they are used at the seed stage, their step-by-step construction, the situations in which one should be preferred over another, the profiles to consult, their advantages and limitations, the five most frequent mistakes, two fully worked cases (a French SaaS startup and a Swiss startup), a ten-question FAQ, and an operational summary for founders and investors alike.

Secure your fundraising before negotiating the term sheet

Before presenting a valuation to a business angel or a seed fund, it is almost always more effective to have the reasoning reviewed by a third party independent of both sides. Hectelion supports founders going through a fundraising process in building and defending their pre-money valuation, whether it rests on Berkus, Scorecard, Risk Factor Summation or a combination of the three. Book a 30-minute call to have your valuation scorecard stress-tested before sending it to your investors, or to discuss the best method given your company's stage.

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Definition: what are Berkus, Scorecard and Risk Factor Summation?

All three methods respond to the same problem, the absence of usable financial data for pre-money valuation, through three distinct calculation logics. The Berkus method attributes, to each of five qualitative factors (idea, prototype, quality of the management team, strategic relationships and commercial launch), a capped amount generally set at $500,000 per factor in its original version, for a theoretical maximum of $2 million before commercialisation and $2.5 million afterwards. The sum of the amounts attributed gives the pre-money valuation directly, with no reference to a local market.

The Scorecard method, formalised by angel investor Bill Payne, instead starts from an average market valuation, observed on comparable fundraising rounds in the startup's region and sector, then adjusts it using a multiplier built on six to seven weighted factors (strength of the team, market size, product and technology, competitive environment, distribution channels and partnerships, need for additional financing, and sometimes a residual "other" factor). A neutral score on every factor brings the multiplier back to 1 and the final valuation to the chosen market average.

The Risk Factor Summation also starts from a reference valuation but adjusts it additively, factor by factor, across twelve risk categories (management, stage of development, regulatory risk, manufacturing risk, business/marketing risk, risk of additional financing, competitive risk, technology risk, litigation risk, international risk, reputation risk, and potential for a lucrative exit), each scored from minus two to plus two, with every point of score corresponding to a fixed monetary bracket. All three methods are qualitative, quick to implement and designed to fill the gap left by missing financial data, never to replace that data once it exists.

Origin: why do these three methods come from American business angel networks?

All three methods share a common origin in North American seed-investing practice, documented mainly by the Angel Capital Association (ACA) and its training arm, the Angel Resource Institute. The Berkus method takes its name from Dave Berkus, an investor and business angel who has taken part in more than eighty seed deals, whose first version appeared in a book published in the mid-1990s by Howard Stevenson, a professor at Harvard Business School. Berkus himself revised it in 2009, 2016 and then 2024, systematically reminding readers that it is only a starting point and that its caps must be adapted to the region and period under consideration.

The Scorecard method was first written up by Bill Payne, a business angel at Frontier Angels, in May 2001, then detailed in a book in 2006. Adopted by the Ohio TechAngels under the name "Bill Payne Method" in 2008, it was renamed the "Scorecard Method" in 2010 at a workshop of the Angel Capital Education Foundation, the forerunner of today's ACA, which has since published several successive revisions, the latest dated 2019.

The Risk Factor Summation was described by Bill Payne in 2011 as the fifth in a series of articles on valuing pre-revenue companies, explicitly attributing it to the Ohio TechAngels, who had formalised the twelve-factor grid. The idea of basing a venture capital valuation on a battery of individually scored risk factors nonetheless has an older academic precedent in the QED Report on Venture Capital Financial Analysis published by James L. Plummer in 1987, a work cited the following year by Harvard professor William Sahlman in his own research on the structure of venture capital deals. These three methods subsequently crossed the Atlantic as European angel networks grew, and today feature among the scorecards taught in Switzerland, notably as part of investor training by the SICTIC network, whose Swiss Angel Investor Handbook devotes an entire chapter to best practice in valuing pre-revenue startups.

Why use these qualitative methods at the pre-revenue seed stage

• The absence of reliable financial data makes classic discounted cash flow or market multiple methods unworkable, since they assume an operating history the startup does not yet have.
• These qualitative scorecards force a structured discussion between founders and investors around the real risk factors (team, technology, market, regulation), rather than around an unverifiable table of projections.
• They are quick to apply, often in under an hour, which suits the pace of a seed-stage deal flow where dozens of files must be sorted every month.
• They provide a common language widely shared across business angel networks, which eases discussion when several investors co-invest in the same round.
• They give founders an objectified negotiating basis, letting them anticipate an investor's objections by working in advance on the weakest factors in their file.

How the Berkus method is built

• Step 1: set the cap per factor, traditionally $500,000 in the original version, but to be adapted to the average valuation observed locally divided by the number of factors retained.
• Step 2: assess the strength of the idea itself, independently of its execution, by asking whether it addresses a real and addressable problem.
• Step 3: assess the existence and maturity of a prototype or minimum viable product, which reduces the project's technology risk.
• Step 4: assess the quality of the management team, in particular its ability to execute the plan presented and to attract missing skills.
• Step 5: assess the strength of strategic relationships already established (industrial partners, letters of intent, distributors), which reduce commercial and competitive risk.
• Step 6: assess the commercial launch or first sales, which reduce financial and operational execution risk.
• Step 7: add up the five amounts obtained to get the pre-money valuation; Dave Berkus himself has recommended, since his 2024 revision, treating this list of factors as a suggestion to be adapted rather than a rigid grid.

How the Scorecard method is built

• Step 1: determine the average (or median) pre-money valuation of pre-revenue startups recently financed in the region and, where available, in the sector of the startup being assessed.
• Step 2: list the comparison factors retained, in the most common version: strength of the management team (up to 30% of the weighting), size of the market opportunity (up to 25%), product and technology protection (up to 15%), competitive environment (up to 10%), distribution channels and strategic partnerships (up to 10%), need for additional financing (up to 5%), and a residual "other" factor (up to 5%).
• Step 3: score each factor by comparing the target startup to an average company in the same sample, a neutral score being worth 100%, a higher or lower score reflecting a positive or negative gap against the norm.
• Step 4: weight each score by the corresponding factor's weighting, then add them all up to obtain the sum of weighted factors.
• Step 5: multiply the average reference valuation by this sum of factors to obtain the target startup's adjusted pre-money valuation.
• Step 6: document every scored gap with concrete evidence (a signed contract, a client reference, a filed patent) so that the investor can challenge each line of the calculation rather than the overall result.

How the Risk Factor Summation method is built

• Step 1: select a reference valuation, generally the same average regional market valuation used for the Scorecard method.
• Step 2: review the twelve risk categories commonly used: management, business development stage, legislative or political risk, manufacturing or supply-chain risk, business and marketing risk, risk of additional fundraising, competitive risk, technology risk, litigation risk, international risk, reputation risk, and potential for a lucrative exit.
• Step 3: score each category from minus two (highly unfavourable risk) to plus two (highly favourable risk relative to a comparable startup), zero reflecting a neutral situation.
• Step 4: convert each score point into a fixed amount, in brackets of $250,000 or $500,000 depending on the version used by the investor or angel network, taking care to apply the same bracket to all twelve factors.
• Step 5: algebraically add up the positive and negative adjustments and apply them to the reference valuation to obtain the final pre-money valuation.
• Step 6: keep in mind that management risk remains, in the practice of the Ohio TechAngels who formalised the method, the most discriminating factor in the final calculation.

When to prefer Berkus, Scorecard or Risk Factor Summation

Comparison table for illustrative purposes; the caps, weightings and brackets cited vary by investor network and must be adapted to the local market before any operational use.

Who to turn to for applying these valuation methods

Three profiles most often get involved in applying these scorecards: the business angel or seed fund who uses them to sort deal flow and set a negotiating ceiling, the founder who must understand the logic to defend their own estimate, and the independent adviser, valuation firm or accountant, who can check the arithmetic consistency and relevance of the factors chosen before it is presented to both sides. Choosing the right party depends above all on the independence sought: an adviser paid by one side of the round will always find it harder to objectively challenge the other side than a firm with no economic interest in the outcome of the negotiation.

Hectelion acts as a Franco-Swiss boutique business valuation firm, specialised in particular in startup valuation for pre-revenue companies, due diligence and financial structuring, on transactions ranging from CHF 2 to 500 million, from the first valuation of a young seed-stage company to the structuring of a family business transfer. Its dual Franco-Swiss expertise and its economic independence from traditional financial intermediaries, aligned with IVSC standards, make it a neutral party for validating a Berkus, Scorecard or Risk Factor Summation scorecard before it becomes the basis for a term sheet negotiation.

Advantages: speed, structure, negotiation

• Speed of execution: a scorecard can be filled in under an hour once the qualitative elements of the file are gathered, which suits the volume of deal flow handled by a business angel network.
• Structure comes from the discipline they impose: rather than debating an overall figure, founders and investors discuss factor by factor, which makes the negotiation more concrete and less emotional.
• Negotiation follows directly from that structure: a founder who knows exactly which factor is pulling their score down can work on that specific point (sign a letter of intent, hire a key profile, finish a prototype) before their next financing round, rather than renegotiating an abstract figure.

Limitations: subjectivity, local data, lack of empirical validation

• Subjectivity is acknowledged by the creators of all three methods themselves: Bill Payne already noted in 2011 that no investor will ever value the same company identically twice, and the score given to each factor remains, by construction, a qualitative judgment rather than a verifiable calculation.
• Dependence on local data particularly affects the Scorecard and Risk Factor Summation methods: their reliability rests entirely on the relevance of the chosen average reference valuation, which varies sharply from one region to another and from one year to the next, and which is often harder to document in France or Switzerland than in the United States, where the Angel Capital Association publishes regular statistics.
• The lack of rigorous empirical validation means that none of the three methods has been tested against a representative sample of real exits to verify its predictive power: they remain useful market conventions for structuring a discussion, not validated econometric models.

What average valuations are observed in France and Switzerland for calibrating Scorecard and RFS

Both the Scorecard and Risk Factor Summation methods rely on an average reference valuation, which immediately raises a practical question: what figure should be used in France or Switzerland? Public venture capital barometers, whether the EY Venture Capital Barometer or the joint France Digitale and EY publications, mainly measure the total volume of fundraising and the average amount per deal across all maturities combined, without systematically isolating a pre-money valuation specific to the pre-seed and seed stages. In Switzerland, the Swiss Angel Investor Handbook published by SICTIC documents valuation best practice without, however, publishing a reference average updated every year.

In the absence of an official statistic isolating the pre-seed or seed stage, French seed-stage practitioners most often cite a range of a few million euros for the pre-money valuation of a first round, with wide dispersion depending on the sector (a deeptech or AI startup typically being financed at a higher valuation than a services startup), already-demonstrated commercial traction, and the reputation of the investors already committed to the round. This range remains, by construction, a market benchmark to be adjusted case by case, never an official figure set in a barometer.

In Switzerland, the ecosystem tracked by SICTIC and documented in the Swiss Angel Investor Handbook likewise lacks a public valuation average updated by stage. The order of magnitude commonly cited by investors in the network for a seed round remains, as in France, a few million Swiss francs of pre-money valuation, a benchmark to be refined sector by sector rather than a uniform standard, with deeptech or cleantech generally trading above a consumer services startup, as illustrated by the worked case presented above.

In both cases, these orders of magnitude remain well below the $4 to $5 million cited by Bill Payne for certain particularly active American regions (see Mistake 1 below), which confirms that applying the Angel Capital Association's North American benchmarks without adjustment to a French or Swiss file mechanically leads to overestimating the average reference valuation used for Scorecard or Risk Factor Summation.

The 5 mistakes to avoid

Mistake 1: Applying American caps without adapting them to the local market

The $500,000 per factor amounts in the Berkus method, or the average valuations of $4 to $5 million cited by Bill Payne for certain American regions, do not match the valuation levels observed in France or Switzerland for comparable seed rounds. Applying these figures without adjustment mechanically leads to overvaluing the startup, which complicates negotiation with local investors used to other benchmarks.

Mistake 2: Confusing pre-money and post-money valuation in the calculation

All three methods produce a pre-money valuation, that is, the value of the company before the raised funds come in. Adding the amount invested then gives the post-money valuation, which is used to calculate the percentage of equity given up. Confusing the two leads to a miscalculated dilution and to recurring tension when signing the term sheet.

Mistake 3: Using a single method without cross-checking results

Berkus, Scorecard and Risk Factor Summation rest on sufficiently different logics to produce widely differing results for the same company, as shown by the two worked cases presented further below. Relying on a single method, without cross-checking at least two approaches, deprives the negotiation of a consistency benchmark and exposes it to a figure that reflects only one angle of analysis.

Mistake 4: Scoring factors without documented evidence

A +2 score on the "strategic relationships" factor is only worth something if it rests on a verifiable document (a signed letter of intent, a pilot contract, an identifiable client reference). Giving optimistic scores without supporting evidence weakens the credibility of the entire scorecard as soon as the investor asks the first verification question.

Mistake 5: Ignoring that the method stops being relevant once significant revenue appears

Dave Berkus explicitly points this out: his method is no longer suitable once the company has enough revenue history to project its financial trajectory. Continuing to use these qualitative scorecards once recurring revenue already exists amounts to deliberately ignoring more reliable information, to the detriment of valuation accuracy.

Case 1: a French SaaS startup valued between EUR 1.2 and 2.79 million at seed stage

Take the example of a Lyon-based B2B SaaS startup, publisher of an expense-management tool, at the seed stage, with a minimum viable product tested by three pilot clients and emerging recurring revenue of around EUR 8,000 of MRR. It is looking to document its pre-money valuation ahead of a first round with regional business angels.

Using the Berkus method, with an illustrative cap of EUR 400,000 per factor (rather than the $500,000 of the American version, to reflect a more modest French seed market), the startup obtains the following amounts per factor:

• Idea: EUR 300,000
• Prototype: EUR 350,000
• Management team: EUR 300,000
• Strategic relationships: EUR 150,000
• Commercial launch: EUR 100,000

The sum gives a pre-money valuation of EUR 1,200,000.

Using the Scorecard method, with an illustrative average valuation of EUR 2,500,000 for comparable B2B SaaS seed rounds in France, the scores obtained are as follows:

• Team strength: +0.20 (weight 30%, i.e. +0.0600)
• Market size: +0.30 (weight 25%, i.e. +0.0750)
• Product and technology: 0 (weight 15%, i.e. 0)
• Competitive environment: -0.20 (weight 10%, i.e. -0.0200)
• Strategic relationships: +0.10 (weight 10%, i.e. +0.0100)
• Need for additional financing: -0.10 (weight 10%, i.e. -0.0100)

The sum of weighted factors reaches +0.1150, i.e. a multiplier of 1.1150. The adjusted valuation stands at EUR 2,500,000 multiplied by 1.1150, i.e. EUR 2,787,500.

Using the Risk Factor Summation, with the same reference valuation of EUR 2,500,000 and a bracket of EUR 125,000 per score point (half the North American convention of $250,000, to reflect more modest seed round amounts in France), the scores obtained are as follows:

• Management: +1
• Development stage: 0
• Legislative risk: 0
• Manufacturing risk: +1
• Business risk: -1
• Fundraising risk: -1
• Competitive risk: -1
• Technology risk: +1
• Litigation risk: 0
• International risk: 0
• Reputation risk: +1
• Exit potential: 0

The sum of points is +1 (four positive points, three negative points), i.e. an adjustment of +EUR 125,000. The final valuation stands at EUR 2,500,000 plus EUR 125,000, i.e. EUR 2,625,000.

The three methods therefore converge on an interval of between EUR 1.2 and 2.79 million: Berkus, which rests on no market data, remains markedly more conservative, while Scorecard and Risk Factor Summation, anchored on the same regional reference valuation, give results close to one another, around EUR 2.6 to 2.8 million.

Case 2: a Swiss cleantech startup valued between CHF 1.55 and 3.41 million at seed stage

Take the example of a Vaud-based cleantech startup developing an energy-storage monitoring solution for industrial sites, at an advanced seed stage, with a prototype validated under real conditions at two pilot industrial clients, a signed letter of intent with a cantonal electricity distributor, and emerging revenue of around CHF 15,000 per month.

Using the Berkus method, with an illustrative cap of CHF 400,000 per factor, the startup obtains the following amounts per factor:

• Idea: CHF 350,000
• Prototype: CHF 400,000 (cap reached thanks to real-condition validation)
• Management team: CHF 350,000
• Strategic relationships: CHF 300,000
• Commercial launch: CHF 150,000

The sum gives a pre-money valuation of CHF 1,550,000.

Using the Scorecard method, with an illustrative average valuation of CHF 3,000,000 for comparable deeptech or cleantech seed rounds in Switzerland, the scores obtained are as follows:

• Team strength: +0.10 (weight 30%, i.e. +0.0300)
• Market size: +0.20 (weight 25%, i.e. +0.0500)
• Product and technology: +0.30 (weight 15%, i.e. +0.0450)
• Competitive environment: 0 (weight 10%, i.e. 0)
• Strategic relationships: +0.20 (weight 10%, i.e. +0.0200)
• Need for additional financing: -0.10 (weight 10%, i.e. -0.0100)

The sum of weighted factors reaches +0.1350, i.e. a multiplier of 1.1350. The adjusted valuation stands at CHF 3,000,000 multiplied by 1.1350, i.e. CHF 3,405,000.

Using the Risk Factor Summation, with the same reference valuation of CHF 3,000,000 and a bracket of CHF 150,000 per score point, the scores obtained are as follows:

• Management: +1
• Development stage: +1
• Legislative risk: +1 (favourable cleantech subsidies in Switzerland)
• Manufacturing risk: -1 (sourcing of a hardware component)
• Business risk: 0
• Fundraising risk: -1 (relatively large CHF 2 million round at the current stage)
• Competitive risk: 0
• Technology risk: +1 (patent filed)
• Litigation risk: 0
• International risk: -1 (no validation outside Switzerland)
• Reputation risk: +1 (industrial client reference)
• Exit potential: 0

The sum of points is +2 (five positive points, three negative points), i.e. an adjustment of +CHF 300,000. The final valuation stands at CHF 3,000,000 plus CHF 300,000, i.e. CHF 3,300,000.

The three methods here converge on an interval of between CHF 1.55 and 3.41 million: as in the first case, Berkus remains the most conservative in the absence of any market reference, while Scorecard and Risk Factor Summation, anchored on the same regional reference valuation, give results close to one another, between CHF 3.3 and 3.41 million.

A word from our Managing Director

"In almost every seed-stage file I see, the founder and the investor come to the table with two valuation figures with no connection between them, each based on intuition rather than on an explicit method."

"Berkus, Scorecard and Risk Factor Summation never give a definitive figure, but they give a common language: once both sides discuss factor by factor rather than an overall figure, the negotiation becomes much faster and much less adversarial."

"Our role at Hectelion is not to impose a valuation, but to verify that the scorecard chosen, whatever it may be, rests on documented assumptions and on a relevant market reference, before it becomes the basis for a negotiation.", Aristide Ruot, Ph.D., Founder and Chief Executive Officer, Hectelion SA.

FAQ: the 10 essential questions about Berkus, Scorecard and Risk Factor Summation

Introduction: what to know before the questions

The ten questions below cover the most frequent points of friction observed by Hectelion when reviewing valuation scorecards presented by seed-stage founders or their investors, in both France and Switzerland.

Q1: What is the essential difference between the Berkus method and the Scorecard method?

The Berkus method builds the valuation in absolute terms, by adding up capped amounts independent of any reference market, whereas the Scorecard method builds the valuation in relative terms, by adjusting an average regional valuation observed on comparable startups. The former suits a company with no comparison data available, the latter requires that a sample of comparable seed rounds can be identified locally.

Q2: Why does the Risk Factor Summation use twelve factors rather than five or seven?

The Ohio TechAngels, who formalised this method, wanted to force investors to consider a broader set of risks than those covered by Berkus or Scorecard, in particular exogenous risks such as regulatory risk, litigation risk or international risk, which do not always appear in a scorecard centred on team, product and market.

Q3: Can Berkus, Scorecard and Risk Factor Summation be combined in the same fundraising round?

Yes, and it is even recommended by the practice of business angel networks: calculating the three valuations in parallel, as in the two worked cases presented above, provides an interval rather than a single figure, and makes it possible to immediately identify whether one method produces a result abnormally far from the other two.

Q4: Do these methods also apply to a startup that already has some revenue?

They remain usable as long as that revenue stays too limited or too recent to serve as a reliable basis for a financial projection. Dave Berkus himself states that his method stops being relevant once sufficient revenue history exists, at which point methods based on cash flows or on market multiples become more appropriate.

Q5: How should the average reference valuation for the Scorecard method be chosen?

Bill Payne recommends using the median pre-money valuation of pre-revenue startups recently financed in the same region and, if possible, in the same sector, noting that this figure generally does not vary much from one sector to another within the same region. In the absence of reliable public statistics in France or Switzerland, an independent valuation firm can help document a defensible order of magnitude rather than picking one arbitrarily.

Q6: Are the $500,000 caps of the Berkus method still valid in 2026?

Dave Berkus has himself revised his method several times, most recently in 2024, noting that these caps must be reassessed according to the region and period, for example by dividing the local average valuation by the number of factors retained rather than mechanically reusing the historical figure of $500,000.

Q7: Can an investor impose their own weighting in the Scorecard method?

Yes, the weightings published by Bill Payne (30% for the team, 25% for the market, and so on) are a reference convention, not a mandatory standard. Some deeptech-focused investors weight the technology factor more heavily, while commercially oriented investors weight distribution channels more heavily.

Q8: What happens when Berkus, Scorecard and Risk Factor Summation give very different results?

A large gap, for example a factor of two or three between the lowest and highest method, almost always signals that one of the methods was applied without a relevant market reference, or that the qualitative factors were scored inconsistently across the three scorecards. It is then better to revisit the assumptions one by one rather than arbitrarily choosing the method that gives the most favourable figure.

Q9: Are these methods recognised by professional valuation standards such as the IVSC?

The IVSC's international valuation standards do not impose a single method for pre-revenue companies and allow the use of qualitative approaches where financial data is lacking, provided the assumptions used are documented and consistent. Berkus, Scorecard and Risk Factor Summation fit within this logic of methodological transparency, without however constituting valuation standards in the strict sense.

Q10: At what stage should a startup abandon these qualitative methods for DCF or multiples?

As soon as a history of recurring revenue exists over several quarters and allows a credible projection to be built, methods based on discounted cash flows or on sector multiples become more reliable than qualitative scorecards, which remain designed to fill the absence of data rather than to ignore it once it exists.

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Conclusion: three complementary scorecards, never a universal calculator

Berkus, Scorecard and Risk Factor Summation all three respond to the same problem, the lack of reliable financial data at the seed stage, but through three calculation logics different enough that none should be used alone. Berkus suits best a startup with no market reference available at all, because it builds its valuation in absolute terms from capped amounts. Scorecard and Risk Factor Summation, by contrast, require a documented average regional valuation, but offer in return a finer sensitivity to each file's specifics, the former through a weighted multiplier, the latter through a point-by-point adjustment across twelve risk categories.

The two worked cases presented above, a French SaaS startup and a Swiss cleantech startup, show that these three methods can produce significant gaps for the same company, and that Scorecard and Risk Factor Summation generally converge more closely with one another, once they rest on the same reference valuation, than with Berkus, which remains structurally more conservative in the absence of any market anchor. This gap is not a flaw in the methods but a useful signal: it indicates precisely where to focus documentation efforts before the final negotiation.

For a seed-stage founder as much as for a business angel, good practice lies less in choosing the method that gives the most favourable figure than in calculating all three in parallel, documenting each factor with concrete evidence, and having the whole reviewed by an independent third party before sending it to the other side. It is this methodological discipline, far more than the choice of a particular scorecard, that determines the quality of a seed-stage term sheet negotiation.

Article summary

Berkus, Scorecard and Risk Factor Summation are the three most widely used qualitative methods for valuing a pre-revenue startup at the seed stage, all three stemming from the practice of American business angel networks documented by the Angel Capital Association, and now taught as far as Switzerland, notably through SICTIC's Swiss Angel Investor Handbook. The Berkus method adds up capped amounts across five qualitative factors with no market reference. The Scorecard method adjusts an average regional valuation using a weighted multiplier built on six to seven factors. The Risk Factor Summation adjusts that same reference valuation additively across twelve risk categories.

The two worked cases developed in this article, a French SaaS startup valued between EUR 1.2 and 2.79 million and a Swiss cleantech startup valued between CHF 1.55 and 3.41 million, illustrate that these three methods rarely converge on a single figure, and that the gap between Berkus on one hand, and Scorecard and Risk Factor Summation on the other, depends directly on whether a documented local reference valuation exists.

The five most frequent mistakes, unadapted American caps, confusion between pre-money and post-money, use of a single method, undocumented scores, and keeping these scorecards beyond the pre-revenue stage, cover most of the friction points observed by Hectelion when reviewing seed-stage files in France and Switzerland.

These methods never replace a full financial valuation once revenue history allows it, but at the seed stage they remain the most effective common language between founders and investors for structuring a valuation negotiation on documented grounds rather than on intuition.

Sources

• Angel Capital Association, 2024 update of the Berkus method by Dave Berkus himself
• Angel Capital Association, 2019 revision of the Scorecard methodology by Bill Payne
• Equidam, Berkus method explained with a worked example and a table of risks covered per factor
• Equidam, Scorecard method explained with formula, weightings and a complete worked example
• Eqvista, detailed presentation of the twelve factors and the scoring grid of the Risk Factor Summation
• Gust, article by Bill Payne attributing the Risk Factor Summation to the Ohio TechAngels
• Hustle Fund, summary of the five factors of the Berkus method for pre-revenue startups
• Plummer, James L., bibliographic reference of the QED Report on Venture Capital Financial Analysis (1987), the academic precedent of risk-factor-based methods
• Rho, step-by-step guide to the Risk Factor Summation with a scoring example for the twelve factors
• SICTIC, Swiss Angel Investor Handbook, chapter devoted to best practice in valuing Swiss startups

Author

Aristide Ruot, Ph.D.
Founder | Chief Executive Officer, Hectelion SA