Measuring R&D

Eamon Fenwick
Aug 23, 2017 · 3 min read

Working with businesses from start ups / scale ups through to multinationals, a common query from management teams and R&D leaders is gaining insight as to how their technology development & innovation programs compare from an investment and outcomes perspective. As outlined below, even comparing common R&D metrics for a single company year on year presents a number of issues.


While the more obvious comparatives are R&D intensity (R&D expenditure divided by turnover), patent / IP filings, newly generated revenue, university collaboration projects and capital investment all compared across financial years, industry sector competitors, common geography and company size / market capitalisation, three discrete issues need to be considered:

  • These are largely lagging indicators
  • The everchanging definitions of innovation and R&D, which often vary across industries and over time
  • Margin and revenue profiles across industries.

Increasingly, best practice with regard to key indicators is considering the causative factors, or “power” indicators, present at the beginning of the R&D lifecycle, which are reasonably well correlated with longer term technology capability development. This is analogous to the data driven approach seen in many scale up businesses in acquiring users, tracking MAU, LTV etc.

In the context of R&D, metrics such as the recruitment and retention of technology talent, and the breadth of this recruitment (competitors, academic background, international experience) is a key power indicator, useful in considering the strength of R&D pipelines.

In terms of R&D definitions, the use of government guidelines (such as the eligibility criteria for different innovation incentive programs) provide a very arbitrary measure of R&D investment — many of these definitions have policy (or politically) driven eligibility carve-outs. While more general guides such as the OECD’s Frascati manual address this in part, business model innovation stemming from R&D investment generally will not be captured. Further, differing accounting, tax and commercial drivers can skew how R&D and its outputs are defined and measured.

Lastly, metrics such as the commonly used R&D intensity percentage (annual R&D expenditure divided by annual company turnover) are complicated due to the above definitional reasons, along with the margin and revenue profile differences seen across industries.

Compare a high margin software business with a low margin large consumer goods company. In the former, a double-digit R&D intensity percentage would be expected, while in the latter, the R&D intensity may be better expressed in basis points making comparison difficult. Also, in the case of high growth versus incumbent businesses, and using similar aggregate R&D investment as a baseline, intensity metrics will vary sharply year on year in a high growth business, making long terms comparisons difficult even for the same enterprise.

Using this measure of R&D is further complicated when considering that this given software business may consider only base development costs as R&D, while in consumer goods R&D could comprise New Product Development team expenses through to operational staff (when considering R&D in the applied manufacturing context).

While the measurement of R&D has moved on from the classic economic metric of “patents per capita” or “aggregate R&D expenditure”, it is critical to consider the financial and operating models exhibited by companies operating in different sectors. These differences have a material impact on the R&D metrics outlined above, requiring consideration of both the inputs and outputs of R&D programs, rather than only distilling a single, condensed R&D measure.

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Eamon Fenwick

R&D Partner, Technology sector @ Deloitte. Working with clients across R&D strategy, operations and financing. @eamonf

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