Stats for Startups: Request for Analyses

Charge
Charge VC
Published in
2 min readJun 4, 2020

The core product of Stats for Startups is better benchmarking for common startup KPIs. Last week we shared all of the ways that folks interested in collaborating can get involved. This week, we’re publishing a list of preliminary analyses that we are considering.

Preparing for data science

Our goal here is four-fold:

  1. To illustrate the types of analyses we will be digging into.
  2. To be guided toward prior work.
  3. To understand which analyses are most in demand.
  4. To solicit ideas for analyses that we hadn’t considered.

So without further ado, here is our initial Request for Analyses:

Who writes first checks and how is the market evolving?

Formation capital. Who does true formation capital come from? What do friends and family rounds look like? How much capital do founders put into companies themselves before raising? What is the right structure for that?

Angels. Who makes angel investments? What do they do (HNWI, Entrepreneur, Operator, Engineer, Designer, Banker, Lawyer)? What do they do it?

Preseed rounds. What is a pre-seed round? How is it different than an angel or Friends and Family round? How common are they? How big are they?

Institutional pre-seed. What is a pre-seed fund? How do they do? Who runs them? How many lead rounds? What types of companies do they invest in? What metrics are they looking for? What types of founders?

The capital behind the capital. Who invests pre-seed funds? Why?

Downstream financing. What are seed funds looking for pre-seed funded companies to have achieved? Does having a pre-seed investor help you raise your next round? What the follow on ratio for angel and pre-seed rounds?

What’s next. How has this market changed in 2020? Has the pandemic drastically impacted it? Will raising first checks become drastically more difficult? How will it evolve?

And critically…

Demographics and access. Who are entrepreneurs behind these companies? Where and what backgrounds do they come from? How does funding breakdown across gender, race, and socioeconomic lines? This data will be used to slice and dice all of the questions above.

We’ve probably got a few years of analysis in the list above, but please let us know what looks most ripe and/or what is missing!

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