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Our Scoring System at NextView (aka How We Evaluate Companies)
A few weeks ago, I published a post on how we make decisions and vote at NextView. Usually when we record our votes, we also score each company based on five attributes. This isn’t used as a “scorecard” where companies need to clear a certain quantitative bar to get through our process. But it’s a summary of the attributes we tend to think about as a team, and a record of how each one of us rated our investments based on these attributes. The five attributes are:
- 10X better, faster, cheaper potential
- Monopoly potential
- PMF Risk
We tend to think of a team similar to the concept of founder/market fit. How well suited is the team’s background for the task required to build this company? There are two dimensions to this. The first is domain. Most companies benefit from having some native domain experience in the industry they are attacking. There are sure to be certain relationships, regulatory know-how, or other insider knowledge that will accelerate a company’s learning and early progress. If a founder lacks domain experience, having certain holes filled in the early team is a positive signal.
If a company is in a highly complicated industry and no one on the founding team has knowledge of the domain, that’s a concern. Conversely, a team made up entirely of industry insiders can be worrisome too, because often it’s industry outsiders that are able to push beyond industry norms and offer a truly disruptive solution.
The second dimension of team/market fit has to do with actual capabilities. A team of great domain experts or very senior team members may struggle mightily when tasked with the early stage challenges of building and selling a disruptive product. We like to look at companies with a blank sheet of paper and ask “what is the biggest lift for this business in the next 1-2 years?” Is there someone on the team who might be world class at addressing this risk?” You might have a very well pedigreed team with great domain experience that actually is not well suited for the challenge at hand.
Given the stage we invest in, there is also the dimension of completeness. Is there a somewhat complete team in place, or is there going to be a fair bit of time and effort expended in the first few months post-investment to bring the right people on board? This risk is baked into our analysis because most companies we fund are on a clock and will eventually need to raise money again in 12-24 months and hit some specific milestones in order to do that successfully.
10X Better, Faster, Cheaper / Monopoly Potential
We’ve actually written several blog posts on this concept, so I’ll gloss over it. Generally, we are looking for companies that are JDCC. Have a jaw dropping customer value proposition with a competition crushing business model. In other words, something that is 10X better, faster, or more convenient, and a business with accumulating returns to scale.
We invest at the seed stage, and so product/market fit is always a significant risk. But some companies present greater PMF risk than others for various reasons. In some cases, a category is quite crowded with low switching costs, so creating something that really resonates with the market is super hard. The classic case here is consumer social where it is often a bit of a crapshoot to predict that kinds of products will achieve early traction (and it’s even difficult to determine which will sustain significant traction over a long period of time).
Other companies we look at have much lower PMF risk, either because they have more demonstrable proof points or because there is unique core technology that enables a known customer need in a vastly superior way. For us, we tend to take a portfolio approach at the way we evaluate this sort of risk. We try to have a blend of companies across the risk spectrum (although all with a seed stage point of entry) and we balance this level of risk with the pricing that the market is willing to bear at different points in time. We will occasionally titrate up or down our initial investment sizes based on this level of perceived risk as well.
While we are most inspired by great founders bringing to life jaw-dropping products, we tend to believe the adage that “markets win”. We’ve seen extraordinary teams really struggle to build value in challenging markets, and we’ve seen companies overcome many mis-steps thanks to tremendous market tailwinds.
There is one interesting observation however when I look back at this data. We rarely disagree significantly as a team about the relative attractiveness of a market in the moment. We can usually be pretty intellectually honest about what we see in terms of market dynamics, the relative power of various players, and the short term trajectory. The disagreement comes in projecting how the market will change. Some of our best investments have been in companies that benefited from a significant market shift that came to fruition over 5+ years.
In some cases, founders were making a very specific bet about this shift. But in others, some other event gave a market another gear (most companies that are experiencing tailwinds from Covid are in this bucket). It’s a constant reminder that markets win, but that markets also change. And VC’s tend to do best when they make the right calls in periods of uncertainty and change.
What is not included in our scoring system is “deal”. Although we are quite disciplined about our pricing and ownership targets as a firm, we try to be guided primarily by companies and founders and not by the terms to the deal. We tend to get to “yes” independent of terms, and then try to negotiate terms that we think are fair and provide the right incentives for all players.
We also don’t use these attributes as a way to “get a deal through” our process. As I described before, our model is largely conviction based and allows for a fair bit of disagreement and uncertainty. But we find that this sort of system gives us a common language to talk about the most important elements of a company and also gives us an objective data set to test the quality of our own judgement when we look back at the data with more information.