Every city with economic development policy focusing on entrepreneurship needs a way to measure and demonstrate return on investment. Every dollar spent on supporting startups needs to be justified to the civic organizations, grant-makers, private economic development corporation boards, and the citizens in that community.
The two most commonly-used metrics are Jobs Created and Funding.
These are good metrics, but they are part of the long game.
Lots of communities have read and subscribe to Brad Feld’s Boulder Thesis, which hypothesizes that it’s going to take our communities 20+ years to develop a vibrant and self-sustaining startup ecosystem. Measuring job creation and funding rounds is part of this long term strategy.
But if your ecosystem is less than 10 years old, your startup landscape probably consists of a lot of very, very, very early stage companies (compared to the ones that are just normal early stage companies — the ones with employees and funding). These companies are too early to have employees. They’re legitimately too early for funding.
So how do we define and measure their progress?
Currently, many startup ecosystems measure the vitality of their community by the number of startups in existence. Some of the more sophisticated communities are probably also tracking which of those startups have increased employee count and/or raised funding.
This is a crude but useful baseline understanding of what is happening in the community. If an ecosystem goes from five startups to 10 startups, they’re making progress! However, this is not nearly enough information to understand if the economic development efforts being poured into the ecosystem are effective.
What if in the course of one year, three of those five startups from the previous year raised funding and moved to San Francisco while eight new founders launched their startups and are in the process of trying to land their first customers? This gives a much more nuanced view of what is happening in the community — both in terms of “progress” and — more importantly- in how and where the ecosystem should double down on resources.
What we need is a way to assess and diagnose the health of our startup ecosystems. We need a more fine-tuned metric of startup progress.
This is one of the most powerful use cases for the Fluency Score.
Most startup communities attract and support startups throughout their lifecycle, from idea on a napkin through growth and maturity to an “actual business”. Many incremental improvements need to happen in those early startups, way before they have revenue, employees or outside funding. The Fluency Score is the best product on the market to precisely measure that progress and report on the impact a program has made to help that startup improve.
For example, if a community facilitates a startup bootcamp or accelerator program for pre-revenue stage companies, they could easily pour money and resources into a batch of companies and at the end of 8 or 12 weeks, most of those companies will still be pre-revenue. However, they have probably changed in meaningful ways, especially when looking at how their business model has developed during that time.
Using traditional reporting methods, it might look like those 10 companies did not make progress.
With the Fluency Score, the organization can precisely report that they helped their batch of companies improve their business model validation by 23% in eight weeks.
Economic development leaders that are committed to helping founders can apply this same methodology to ensure the money and resources invested into the startup community are doing the good work everyone wants them to do.
More importantly, it can be optimized. Once we know what results to expect from the community’s current programs and resources, we can evaluate and experiment with these inputs to improve outcomes. We can look for ways to increase efficiency (increase in startups going from idea to growth stage) and velocity (increase in startup moving through these stages faster).
Why does all of this matter?
It matters because our startups need thriving ecosystems that support them through the messy and chaotic process of creating value out of thin air. Lots of people will say startup success is largely driven by luck.
Luck is what it looks like when you don’t know how to measure, experiment, and optimize to get the results you want.