How we screen companies (a look at our scoring rubrics)
We believe in a high degree of transparency. There is no reason that entrepreneurs should have to guess at what types of companies would appeal to us: we should save everyone time and open the black box. As previously mentioned, we explicitly prefer “technical teams working on breakthrough products.” With this post we want to achieve an even higher level of transparency for anyone interested, and show the exact rubrics that we use to assess the companies that we see. However, at the same time, there is a giant caveat:
We are not programmatic in our investment decisions.
These rubrics just serve as a structured way for us to collect information and to have a common point of reference when we discuss various aspects of companies as a team. This structure also has a helpful side effect: it allows us to look back and analyze the decisions that we make over time and, hopefully, find insights from the data.
There are two companion pieces to this post that should be helpful:
- “How we make decisions”: a detailed look at how our fund actually makes investment decisions
- Do our predictions actually predict anything: a look at how our portfolio evaluation rubric translates into later portfolio performance
We primarily use two rubrics during our early information collection process:
- Company fit: basic information about how closely the company fits to Foothill’s sweet spot (stage, founder profile, etc)
- Company value: how highly should we value the company, based on the management team strength, technical brilliance, market forces, etc
Company fit:
This matrix is used as a filter during our initial screening process — it is not predictive of future value.
Some of the categories are obvious: we focus on seed stage and so assign more points to seed deals. We also believe that we are better at evaluating highly technical businesses, so give extra points to businesses where technology is core to the business.
Some may be less obvious to people outside of our fund: we prefer companies that have the ability to successfully leverage a second country for various advantages: primarily access to a lower cost talent pool and to a significant market (that is often helpful in the early development days). We see this most often in China-US teams, but we have also seen the playbook leveraged successfully in Vietnam, Ukraine, Armenia, etc.
Valuation scorecard:
After we invest in a company, we fill out this scorecard. Like the Fit Scorecard, it is not predictive of future value.
All of the variables in the Foothill Fit translate over to this valuation scorecard, but we add more variables related to the management team and competitive landscape. This graphic displays all the variables collected as well as how we categorize them. Clearly shown in this rubric are the different scales for each variable. Because we have many more management-related variables, we initially weighed them on a lower scale than categories such as the “Size of opportunity,” for example. However, the management score sums up to a maximum of 35 whereas the other individual variables vary between 10 and 18.
We initially weighed these variables based on intuition — that management is the most important factor in company success, with the technical level of the product and its core technology also being relatively important factors. However, as we collect more data, we plan to improve and tune these weights over time.
While this scorecard collects a lot of information, it is not a predictor or a direct indication of company success. Rather, this scorecard allows us to evaluate certain aspects of a company in a standardized manner for our portfolio. You may notice some gray areas on this rubric, such as founder’s risk or executive management experience, which may be harder to quantify and more prone to bias. We emphasize that this tool provides one of many angles to evaluate our companies.
If you’re curious about how this scorecard rubric ties into our standard investing operating practices, read our article here that ventures into how we can meaningfully apply the data from this scorecard to give us some interesting insights about our portfolio performance.
