I recently came across a concept, described by Dustin Dolginow of Atlas Ventures, called “Product Metabolism“. Dustin’s insight was that the speed at which startups iterate on their product, their “product metabolism”, should be a key performance indicator (KPI), and startups with a higher product metabolism should realize more success than those with a lower metabolism. However, he didn’t give any specifics on how to measure metabolism, which got me thinking.
Of course, as a proponent of Agile Marketing, I also feel that the measurement should cover more than just product iterations – my goal would be to measure innovation metabolism, whether that innovation takes place in product, marketing, sales or any other function of the company.
I think the best way to measure innovation metabolism is to count the number of completed cycles through what Eric Ries calls the “build-measure-learn” feedback loop or more specifically, what Ash Maurya calls “experiments” in his Lean Canvas.
Each experiment consists of four parts:
The experiment begins with a hypothesis and a bright line criterium for proving or disproving the hypothesis. For products, a hypothesis might look something like “Adding feature X will result in a 2% improvement in conversion rate from trial to paying customers within 4 weeks after introduction.” For marketing, a hypothesis might look like “Introducing marketing program Y will result in 200 more top of the funnel leads per week within 4 weeks of introduction.” The Lean Canvas makes it very easy to propose and track these experiments.
The product feature or the marketing program is built and implemented.
The results are measured and the data gathered to prove or disprove the hypothesis.
A decision is made to keep the feature or get rid of it; continue the marketing program (and possibly expand upon it) or discontinue it, and the next hypothesis is formulated. The loop completes and the next loop begins.
Measuring Innovation Metabolism
A team that can complete six parallel (or sequential) loops through the build-measure-learn experiment loop in a given amount of time has a higher “innovation metabolism” than a team that completes only 3 loops. This is also useful for comparing a team’s performance over time – are the number of loops increasing as we learn more and get better at execution, and perhaps as we add people, or is our “innovation metabolism” slowing down because we’re becoming bureaucratic and taking fewer chances?
Yes, but . . .
The obvious argument against this way of measuring innovation metabolism is that some hypotheses will be more successful and have more impact on the business than others, and some innovations are more important than others. Einstein may have had only two major innovations in his lifetime, but what important innovations they were!
That’s correct, but I think the success and the impact on the business are measured by other KPIs. This KPI is all about speed – speed of taking chances, speed of learning, speed of action.
What do you think? Is this the right way to measure “innovation metabolism”? Any other suggestions?