Generally, estimating the value of a businesses is based on revenue and profit forecasts. For start-ups, these founders usually forecast these figures in a business plan. However, during the early stages of a start-up, such forecasts usually don’t exist. Founders only have a business idea and no business plan. The Berkus model circumvents this problem of quantifying the value of a start-up, which cannot be estimated using the traditional approaches.
Young start-ups often fail within 2 years of their incorporation. According to Investopedia, in 2019, approximately 90% of the the start-ups failed.
Lack of funding, corporate governance issues, incorrect product-market fit, ineffective marketing, and poor partnerships were the common reasons for start-ups’ failures.
The model
The American venture capitalist and angel investor Dave Berkus created the Berkus model as a reaction against the poor track record of start-up companies to meet their financial targets. Dave felt that he could not rely on the financial projections for startup valuation. Often, a start-up’s management forecasts aggressive revenue growth and unrealistic profit margins. Investment banks perform sensitivity / risk analysis on a company to assess its ability to repay loans before lending capital.
Likewise, the Berkus model approaches startup valuation from a risk perspective. This model assumes that a start-up’s management often forecast a rosy picture that does not reflect the real market situation.
According to Dave Berkus himself, the Berkus Method:
assigns a number, a financial valuation, to each major element of risk faced by all young companies — after crediting the entrepreneur some basic value for the quality and potential of the idea itself
This start-up valuation model assigns a value to the business idea and the start-up’s key success factors, or risk factors. Each factor counts for a maximum of US $500,000. In its original form, the Berkus model allows for a maximum valuation of US $2.5 million, including revenues, or US $2 million, excluding revenues. As Dave Berkus states:
The original matrix is too restrictive and should be a suggestion rather than a rigid form
Risk factors
The key value drivers represent risk areas that can make or break the start-up. A start-up should carefully manage these risks to be successful. The value of the start-up increases when it de-risks the key risk areas. Hence, risk reduction and company valuation go hand-in-hand. Therefore, the Berkus model uses both qualitative and quantitative factors to calculate valuation based on five factors, namely:
- Sound idea (represents the basic value)
- Prototype (reduces technology risks)
- Quality Management (reduces execution risks)
- Strategic relationships in its core market (reduces market risk), and
- Product roll-out or sales (reduces production risks)
The Berkus model assigns a maximum added value of US$ 500,000 for each of these value drivers. This explicitly indicates that these are maximum values that can also be decreased.
| Driver | Risk factors | Added value (in US $) |
| Viable business model | Base value | 500,000 |
| Available prototype | Reducing technology risks | 500,000 |
| Management team capabilities | Reducing implementation risks | 500,000 |
| Strategic relationships | Reducing market risks | 500,000 |
| Existing customers / sales | Reducing production risks | 500,000 |
| Maximum company value: | Reducing investment risks | US$ 2,500,000 |
Example
The Berkus model sets the hurdle number at $20M (in the 5th year in business) to:
provide some opportunity for the investment to achieve a ten-times increase in value over its life
Below is an assessment of a fictitious pre-revenue startup. This example illustrates the general rules of the Berkus model:
| Value driver | Added Value (in US $) |
| Viable business model | US $ 250,000 |
| Available prototype | US $ 350,000 |
| Management team capabilities | US $ 450,000 |
| Strategic relationships | US $ 200,000 |
| Existing customers / sales | US $ 50,000 |
| Maximum company value: | US $ 1,300,000 |
In the above example, with US $500K as the maximum value per category, I assigned the greatest value to the capabilities of the management team (US $450K) for the founders‘deep domain expertise. Logically, as the investor undertakes a lot of risks, the startup’s management team must be fully capable of achieving long term success. The startup’s prototype (US $350K) is sound. This minimizes technology risks. Ultimately, this startup achieved a pre-money valuation of approximately US $1.3 million.
Adaptations
Recently, Berkus stated that:
The original matrix is too restrictive, and should be a suggestion rather than a rigid form. The model should allow for higher maximum value on elements not listed in the matrix.
For example, the pre-money valuations might be higher in Silicon Valley than in New York. According to AngelList Valuation Data, the average pre-money valuation in Silicon Valley is $5.1M compared to New York City’s $4.6M. Correspondingly, this model can be easily accommodate the altered scenarios.
In Berkus’ own words:
Pre-revenue, I do not trust projections, even discounted projections
