Using big data for investment decisions

“For instance, we often ask startups for their analytics logins during due diligence and compare what we find there to patterns from our successful investments. This morning we will be meeting to discuss how to systematize this effort and support it with some actual technology so that we can more easily discover and track new companies.”

This is from a post by Albert Weissman on how USV uses analytics techniques to decide on investments. Go ahead and read the whole thing.

500 startups use a similar philosophy: using data on their portfolio companies, especially the early investments, to figure out in which ones to follow on/double down in. Here’s an interesting Post Dave wrote about it.

I like the way both 500startups and USV think about it. In my opinion it is incredibly valuable to use in the series A/B stages of investing, when early patterns are arising. While it can sound like “hey, we just look at the data”, the actual implementation depends extremely deeply on the experience of the team (where, obviously, nobody needs to worry about USV or 500). You need a lot of insight into the data to decide what the patterns and developments actually mean.

In the end, the question is if it will allow you to avoid more type 1 or type 2 errors, i/e. does it serve to avoid investing in the wrong ones, or to avoid missing the right ones? As the usual tricky questions like access to deal flow, ability to join rounds, etc. still apply, this is probably an approach that is most useful for well connected and already successful investors.