Here at GroundControl, we’ve seen that some managers don’t (initially) like to add startups to their portfolios when trying our product. Instead we see them taking a lot of time figuring out on their own how they should structure and measure their innovation program. While it’s completely understandable that they don’t want to throw their startups into deep with the risk of wasting their time, we do think that it’s important that startups get involved as soon as possible. 

Our platform is heavily built around the principles of innovation accounting to measure innovation success. Applying innovation accounting is a vital step towards professionalizing innovation. Yet, it is only possible to measure aspects that are actually happening. Thus, these KPIs only work if startups are actively learning by running experiments, and if they’re kept track of in a structured fashion. 

This problem doesn’t just apply to our platform, but applies to how innovation managers try to professionalize innovation as a whole. It is easier to overthink how we might structure and improve our program than it is to actually take (small) incremental steps to reach that point. If we give in to this tendency, we risk spending months (if not years) without moving innovation forward. 

Let’s use an analogy for the sake of argument: When building and testing a solution, you’ll only gain relevant data and learn if it works the way you’ve intended, once you let your users engage with it. The same applies to measuring innovation. You want to learn as fast as possible how your innovation program performs and how startups (your users) are interacting with it. Where does it do well and where does it require improvement? 

When we consider it as such, it seems obvious that the only way to gather and analyze data is to facilitate and structure interactions that generate it in the first place. Yet many of us would rather overthink what we’re going to measure and how we’re going to do so. If we were to run into a startup that would spend weeks thinking about how they would measure an experiment before actually running it, wouldn’t we be concerned? Precisely. 

From the perspective of startups, Tristan Kromer appropriately calls this the Rudder Fallacy. It’s based on the idea that the rudder of a boat only works when you’re moving. If you try to steer without moving at all, you’re going nowhere. Yes, chances are that you’re heading the wrong way initially. But you’ll only know by doing so, and you’ll only be able to correct your steering if you’re moving somewhere in the first place. The same logic applies to managing innovation. 

So our question becomes: how can we sail this ship in an unknown direction without the risk of sinking it? Or: how can we start structuring and measuring innovation without the risk of wasting the time of those involved?

The latter question is tough to answer. You’ll only learn the true answer for your company by doing. However, there are a few ‘rules’ that we believe should always be applied in ‘sailing your ship’:

• You’ll have a lot less of a headache if you can measure universal aspects of innovation objectively. Doing so requires the startups in your portfolio to have a unified way of working. Applying the same principles to all startups in how they run experiments, learn, and through what stages they go through is a prerequisite for effective measurement and, consequently, professionalizing innovation. 
• Instead of worrying about all the aspects that you would like to measure, and how you would measure them, start by focussing on the few KPIs that are always applicable. The three KPIs that we always start off with focus on experiment velocity, learning velocity, and team happiness. To make your life easy, our platform is already keeping track of these for you (that is if your startups are keeping track of their progress, of course!)
• Most importantly, a top-down approach to control success or progress with your startups doesn’t work. It would swamp them with irrelevant questions. It is far more effective to recognize what kind of data is relevant for you to be able to steer towards a better programme or to make better decisions. Then you can gather data that your startups already generate (e.g. by running experiments and learning about their business model). Subsequently you can abstract that data into insights that help you in making better decisions. 

Fortunately, as you might’ve already noticed, our platform helps you in applying all of these ‘rules’. If you have any additional questions or concerns regarding this, feel free to contact us. We’ll be more than happy to help you through this and set up GroundControl to guide your moonshots.