Dissecting startup failure rates by stage

A few weeks ago in an article titled “How much runway should you target between financing rounds?”, we discovered that the conventional wisdom of targeting 12–18 months of runway between financing events could be a factor that leads to startup failure. We’re delighted with the positive response to that piece (thanks for the shout-out Crunchbase!), and we sincerely hope it helps entrepreneurs make more informed decisions when assessing their cash runway.

The results of that analysis further sparked our curiosity around startup failure, which is often generalized to be nine out of ten. If we consider that statistic for a moment — it implies that a startup always has a 90 percent likelihood of failure. Without even diving into probability theory and statistical philosophy, I just don’t think that’s actually true. Your startup’s likelihood of success should evolve as your specific circumstances change.

We explore this general idea by assessing the startup failure rate from a sequential point of view, which allows us to evaluate whether a startup’s likelihood of failure is static or changes based on how far along it has progressed in the venture capital funnel.

Analytical Framework

We look at startup failure from two distinct lenses — (1) failure to raise the next sequential round of capital, and (2) failure to exit. For the former, we identify companies that raised a k round and failed to raise the following k+1 round. In layman’s terms, that means we segment by all companies that successfully raised a Seed round but failed to raise a Series A. Then we take all companies that raised a Series A but failed to raise a Series B — independent of whether or not they had previously raised a Seed stage round, or ever skipped a step and raised a round of capital classified as a Series C— and so on — until the Series G stage. For the latter, we identify the companies that raised a k round and ultimately failed to exit via an IPO or acquisition.

A potential pitfall of this type of analysis is Survivor Bias — where only companies that are successful are accounted for — which is ultimately…