The Complete Guide To Measuring Innovation Performance

Dan Toma remembers an engineering company that judged its innovation strength by the number of patents it filed. His question was simple: what happened to those patents afterward? A growing stack of intellectual property did not explain how the company would turn its technical work into something customers would buy. Counting was easy. Understanding the value required a different conversation.

In this episode of Innovation Rockstars, the co-founder of OUTCOME and co-author of Innovation Accounting challenges leaders to make that distinction. He wants innovation to be accountable. He also thinks organizations can spend too long designing measurement systems before doing anything worth measuring. For R&D leaders, his advice is practical: start the work, make its stages visible and introduce measures that help people decide what happens next.

Start working before perfecting the dashboard

Toma enjoys the complexity of large organizations. An engineer by training, he is drawn to difficult problems and to the reach that established businesses have. Their resources, customer relationships and technology can give a successful innovation considerable impact. Getting those advantages to support uncertain new ventures is the challenge.

Measurement becomes part of that challenge when leaders expect a new initiative to report like the established business. A company that has manufactured cars for decades understands its economics in a way that a newly formed venture cannot. Asking both for the same financial indicators creates a misleading comparison.

Toma imagines asking someone who started a company last week for a return on investment or customer lifetime value. They would have little useful to report. Ask how many customers they have spoken with who confirmed the problem, and there may already be evidence worth discussing.

That evidence does not prove the venture will succeed. It does help an investor or decision-maker understand whether further exploration is justified. The useful measure changes as the question changes.

Build measurement into the work gradually

One of the most candid moments in the conversation comes when Toma revises his own advice. Previously, he would have said an organization needed to reach a certain maturity before implementing innovation accounting. Now he worries about the practical effect of suddenly introducing a large collection of indicators to people who have been working without them.

His preference is to add measures as the organization develops. Begin by knowing which ideas are in the portfolio. Then understand their relationship to strategic priorities. If the organization has defined development stages, show where each initiative sits.

This is modest information, but it supports more demanding questions later. Tracking movement through stages makes it possible to examine conversion rates. Recording how initiatives develop helps reveal the time required to reach the market. The early data becomes a foundation for a more informed conversation with finance.

Toma's impatience with waiting for a perfect system comes through clearly:

“We're building the tracks while the train is rolling.”

He is not arguing for an absence of governance. He wants measurement to grow alongside actual innovation work, with enough structure to learn from what happens.

One number cannot do every job

A single headline indicator can be tempting, particularly when leaders want a simple explanation of performance. Toma warns that it can also create false confidence or make the system easy to game. Innovation work has different purposes, including new revenue, lower costs and reduced emissions. An indicator has to make sense for the purpose being pursued.

It also has to serve the person making the decision. A CFO, an innovation director and a team testing an idea need connected information, but they do not need identical dashboards. Financial measures become more useful as a venture develops and its business becomes more predictable.

For organizations with a track record, Toma discusses looking at the share of revenue generated by recently launched products. He also asks what it costs to bring a product to market and how much the organization spends on initiatives that fail.

“How much are we paying for every failed product on average?”

That question matters because the cost of failure can reveal something about the process and its governance. An organization needs to know more than whether an initiative stopped. It needs to understand the resources consumed before that decision. For an R&D portfolio, this creates a reason to examine how evidence reaches decision-makers throughout development.

Give the numbers somewhere to go

Before implementing an elaborate accounting system, Toma asks for two foundations: a clear product lifecycle and people who regularly make portfolio decisions. Teams should understand the stages between an initial idea and an offering in the market. Decision-makers should know when they are expected to review progress.

The name of the forum matters less to him. Innovation board, venture board or committee can all work if the group actually makes decisions. He takes the same view of terminology more broadly. Organizations do not have to use the language in his books to run an effective innovation practice.

With enough reliable historical data, leaders can start using measures to inform future investment requests. Toma describes that as an advanced capability, rather than a promise that a newly formed team can make. Estimates still carry uncertainty.

His personal approach to technology is similarly hands-on. He tries unfamiliar apps, even if he deletes them two minutes later. When this interview was arranged, he already knew the recording tool because he had experimented with it earlier. Trying things gives him something concrete to judge. The same instinct runs through his advice to innovation leaders: get the work moving, pay attention to the evidence and improve the decisions as you learn.