I have always liked to write. But when writing became a big part of my job, it sort of lost its charm.
I am going...
Is it possible to bring Billy Beane’s approach to investing in startups? Someone wrote a tongue-and-cheek post about this on GigaOm late last year titled “The secret algorithm one VC firm uses to pick entrepreneurs.” It was somewhat funny but missed the point — what if it’s not about the team? What if the statistics you need to pay attention to are the ones that show traction of the actual product? Obviously this assumes there is a first version of the product so this does not really apply to the earliest stages of investing.
Therein lies the rub, how to use data to inform an investment decision when the only data one has available is the team? I agree that once a startup reaches the stage where they are shopping VC’s, there is usually enough data around usage patterns and user growth to reasonably evaluate the prospects of a startup.
But what if a startup is pre-product or just launched? That is the challenge for seed stage investors and for all the talk about quantitative metrics and evaluation models, the earliest stages are still more about trust and taking a leap of faith that the team and the vision and the product are the right combination.
The real question though is does this metrics-driven approach even make sense in the innovation sector? Yes, you want to identify winners and increase the number of successful exits. However, in a “homerun” driven business model where the winners tend to initially appear as outliers, does a metrics-driven approach actually mute the possibility of investing in the biggest opportunities? While there are no answers here, it is an interesting question to ponder.