In a previous article, we looked at the role that financial projections (and the associated DCF methods) play in startup valuation, which you can read here. The brief summary is that they provide a valuable perspective on the potential of a company, and the ambition of the founders, but introduce uncertainty by looking at forecasts without understanding the ability to execute.

To address this, we turn to the qualitative methods involved in startup valuation, and how they fill this gap: If the DCF models set the goalposts you are aiming for, and the VC method shows the size of the prize, then the qualitative methods show how likely you are to score.

Verifiable Characteristics

The two qualitative methods used by Equidam, the Scorecard method and the Checklist method, were developed by investors specialized in pre-seed startups, and are designed to address the challenges of early stage valuation.

The purpose of both is to use observable attributes of your startup (quality of the core team, size of the opportunity, level of demand) to make an informed comparison against comparable average and maximum valuations. In fact, simply going through the steps involved in these calculations provides a useful lesson on the drivers of valuation.

This is a complementary perspective to the DCF models discussed earlier, as rather than assumptions about the future, these qualitative methods provide a more objective perspective on strengths and weaknesses present today.

Scorecard Method

Originally devised by William H. Payne of the Ohio TechAngels group, and endorsed by the Kauffman foundation, this method analyzes qualitative traits to determine if a startup is better or worse than the average of comparable companies.

The process has three steps:

  1. Compute the average pre-money valuation of a set of comparable market transactions (excluding outliers),
  2. Score the startup based on six criteria
  3. Calculate the weighted average of these scores to determine the percentage difference between the startup’s pre-money valuation and the average valuation from the first step
Checklist Method

Devised by Dave Berkus, a prominent Californian angel investor, this method treats qualitative traits as intangible building blocks that sum up to the assumed maximum pre-money valuation.

The process involves three steps:

  1. Compute the assumed maximum pre-money valuation a startup can obtain over a set of comparable market transactions (excluding outliers),
  2. Divide this maximum valuation into the method’s five criteria, and allocate portions of these criteria valuations based on how close the startup’s qualitative traits are to the most desirable ones.
  3. Sum the portions to determine the pre-money valuation.

Increasing the Resolution

A strength of these methods, from the perspective of investors, is the ability to which they can codify current market dynamics and investor experience: the values they choose to use for average or maximum valuations, and how each of the parameters are weighted. Their specific area of focus – in terms of region, industry or stage – will have an influence on comparable valuations, as well as whether they believe factors like team or traction are more important.

From the perspective of a founder, what you can offer is a reasonable basis to set these parameters, driven by data. You can make these adjustments yourself, or you can use the Equidam platform which does a lot of the research for you:

  • Average and maximum valuations are based on Crunchbase data covering angel, pre-seed and seed funding rounds of the last 30 months.
  • The weighting of each parameter is calibrated with valuation data from the Equidam platform.
  • All of the above remains customisable via the ‘Advanced Settings’ tab.

While prospective investors may have their own perceptions about these data points, we enable you to start with a reasonable and explainable basis for your valuation.

Conclusion

While these methods provide a useful calibration against the market, referencing average and maximum valuations, it’s also worth considering that as a potential weakness, too. In irrational markets, that irrationality could also be reflected in your own valuation. It also doesn’t accommodate for as much variability in outcome as other methods, as valuations will be concentrated around a particular range.

If your goal is to give prospective investors a sense of how your valuation is aligned with market expectations then qualitative methods can be particularly useful. If you are making the case that your company is likely to be an outlier (unique growth potential, or not exploiting a hype-inflated market) then qualitative methods probably shouldn’t be the primary focus of your valuation.

As with any of the methods we’ve discussed, the goal isn’t to find the one that best fits your scenario. The objective is to combine different perspectives on valuation to provide as complete a picture as possible about the ratio of risk to potential return you are offering.