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To start off, Erik: often when people think about measuring innovation performance, they think of things like the number of patents the company has registered or the new-innovation pipeline. Your latest article focuses on a pair of innovation metrics. Can you say a little bit more about that?
Erik Roth: We get the question about innovation metrics quite often. And when a client asks us that question, they typically are concerned with the activity of R&D and innovation as opposed to the output and the impact of that output on performance. And so often when we address this with clients, they’re interested in scorecards that are measuring the number of ideas, ROI [return on investment] on a specific project, the number of projects, and any assorted metrics trying to look at how well their organization is performing.
What’s interesting is, we rarely see an organization taking a thoughtful approach to how it actually measures the outcome of its innovation in R&D performance over time. In this article, what we explored—actually on the back of a client question—was, what is a really good, simple, and benchmarkable metric that can be used to both assess the performance of R&D innovation in an organization and compare it to other companies so that a CEO can understand whether or not their investments in R&D are productive relative to their competitive context and also are achieving their performance objectives over time?
Brown: Why do you think no one has used these two metrics before?
Roth: I think no one has used these particular metrics before because a lot of innovation-measurement activity or -measurement focus has been largely on what I’ll describe as “upstream” activity. That’s the inputs into what makes innovation happen. We see a lot of quantification of the number of ideas and the size of the portfolio. Oftentimes an organization will get very caught up with patents and the number of patents that they are filing. While all of those are interesting inputs into innovation R&D, they don’t necessarily understand or measure the monetization of those investments in your R&D and innovation activity.
And as companies have explored ways to try and understand how to measure the output, or the outcome of their R&D investments, they’ve struggled largely because there are not a lot of common metrics across industries or across organizations that capture what our two metrics capture, which is both the investment side and the outcome side in the form of profit margin for the resulting impact of what R&D investments and innovation investments may have.
Part of the reason we think that’s the case is, one, companies don’t typically release a lot of information about their R&D investments, so there are very few commonly described metrics. And two, the belief has always been, if you really want to understand how to measure R&D and innovation activity, you have to have so much internal proprietary knowledge around what activities are going on, what capabilities are associated with those activities, and the nature of the projects themselves.
I think in many ways the reason why no one has used these is because the belief had been that it was just too complicated; it was just too hard to do.
Guttorm Aase: That’s what we found really appealing about these metrics. This is really a methodology that allows you to benchmark using three simple metrics that are typically available from publicly reported data, which is quite unique in this context. It really only takes a view of R&D spending, of gross margins, and of the shares of sales of spend coming from new-product sales. And those are usually available. That makes it very easy to get a sense of how you’re doing from a performance standpoint versus building these complex internal models that Erik mentioned.
Brown: Thank you. Can you please share a brief overview of the two metrics and how they are constructed?
Guttorm Aase: There are essentially two conversion metrics that we look at. One is the ratio of how your R&D spending is converting into new-product sales. It’s just the ratio of those two numbers. And we look at new-product sales over a time period of a number of years, which can vary by industry. Typically, you’ll see new-product sales measured over a five-year period or a three-year period. And we’re looking at the ratio of those two numbers across each other. That gives you a number that says, for each dollar of R&D spending, how many new-product sales am I getting on average? So that’s the first metric.
The second metric is our product-to-margin conversion metric, which looks at each dollar of new-product sales and asks, how many new dollars of gross margin am I generating? So that’s, again, just the ratio of gross margin to new-product sales.
Roth: I just want to highlight one thing about what Guttorm mentioned, which is the word “conversion.” It’s a very important aspect of these two metrics in the sense that we’re really trying to look at a way to capture the ROI from these investments, not from a traditional net present value project-level analysis, but to really understand, does the investment convert into meaningful profit for the overall entity over time?
Brown: So you’re really looking at the entity or the enterprise—more the portfolio of innovation—and what the productivity is in the portfolio or what’s coming out of that portfolio.
Sri Swaminathan: Yes, that’s right. We find these quite useful as portfolio measures. And they can provide really interesting insights for companies on how they’re performing versus their peers in the same industry. We’ve tested this now in the chemicals industry. We’ve looked at the consumer-goods industry, in the industrial sector, and the pharmaceuticals industry, and we see these relationships holding across various sectors.
Roth: This portfolio look is really important, because what we find is that companies just get metrics wrong. They consistently measure at a project level instead of a portfolio level, even though they talk about portfolio. Having had so many of these conversations, what we really wanted to do is make sure the portfolio view is really embedded in what we’re looking at.
This came out of a challenge question from a CEO. We were sitting in a client meeting one day, and the CEO turned to us and said, “You know, I’ve looked at this metrics thing so many times. There’s nothing out there. Why don’t you guys come back and try to prove that there is some simple way that we can actually measure innovation and R&D output that’s reliable and benchmarkable. Because, you know what, I need to go to ‘the Street’ and understand and communicate to my investors that we’re actually doing a better job relative to what we’ve done historically.”
We came back—I’ll never forget that meeting—and said, “We’ve got two metrics.” There was a general sense of disbelief, because this particular CEO had looked at this many, many, many times. And he’s well known for his activities across many companies. He was surprised that there was something so obvious and useful that was right in front of everybody’s nose, so to speak.
And so this notion of getting it wrong and trying to correct and get it right with simple ways of measuring is a little bit of what was underlying our approach or our intent.
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Guttorm Aase and Sri Swaminathan are associate partners in McKinsey’s New York office, Sean Brown is McKinsey’s global director of communications for strategy and corporate finance and is based in the Boston office, and Erik Roth is a senior partner in the Stamford office.