How to Measure R&D Efficiency on your P&L
Benchmarking ROI on technical resources
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“Is my R&D spend efficient?”
-Every CFO, like ever.
At startups, +70% of costs tend to walk on two feet. With that in mind, I’ve long tried to quantify the return on our technical resources - specifically engineering and product team members.
Unfortunately, it’s a LOT harder than figuring out the ROI on an incremental sales BDR.
Anecdotally I’ve heard of people using “R&D Payback Periods", a proxy for how long it should take to put a dollar into technical work before you see it show up as revenue. But it’s so dependent on the industry and type of product (SpaceX has a much longer (and larger) R&D payback period than a MarCom company).
What I’ve gravitated towards over time is measuring how much time is spent on certain types of dev activities.
But the problem was I couldn’t find data to determine what “good” looks like… until I remembered there were CFOs who read each week who might. As such, today is the first of what I hope will be a healthy stream of CFO guest posts on topics they’re uniquely qualified to opine on. Joanne Cheng is CFO at software engineering intelligence firm Jellyfish. Armed with proprietary data on how engineers spend their time, she’s here to help us benchmark what “good” R&D investment looks like. Take it away, Joanne:
It’s no secret that CFOs rely on metrics and data to inform their decision-making. For go-to-market teams, the path from effort to outcomes is clear. Sales, marketing, and customer success teams leverage well-established tools and platforms, making it easy for finance leaders to analyze performance metrics and make informed decisions. The math is straightforward: if a Chief Revenue Officer (CRO) requests additional headcount, finance can project the return on investment in terms of pipeline and revenue.
When it comes to R&D, however, the story is different. Historically, engineering data has been challenging for finance leaders to access and interpret. Unlike go-to-market functions, an engineer’s workflow is inherently fluid – shifting between building new features, fixing bugs, and maintaining existing systems. This complexity makes it difficult to link R&D activities directly to the P&L, creating a gap in clarity for CFOs.
Yet, as R&D becomes one of the most important cost centers for modern companies, finance leaders can no longer afford to rely on assumptions. Instead, they need to leverage new tools and data to build a robust R&D resourcing model.
The Outdated Approach: Guesswork and Under-resourced Spend
Without access to precise data, many finance leaders resort to educated guesses when planning R&D resourcing. Commonly used metrics, such as R&D spend as a percentage of revenue, are blunt tools that fail to optimize growth, align with product roadmaps, or maximize ROI.
In one of my previous roles, I tackled this lack of transparency by extracting Jira data to understand where engineering resources were being spent. By collaborating with product and engineering leaders, we reviewed historical data to estimate the hours required for upcoming roadmap initiatives and projected future headcount needs.
While this process involved extensive grouping, categorization, and manual analysis of story points, it still relied heavily on assumptions. Despite its shortcomings, this approach was considered a step up from the typical guesswork many organizations used at the time.
Thankfully, new software tools are making it easier to access engineering data and understand how resourcing changes can impact product roadmap and ultimately company outcomes.
The Modern Solution: Financial Decisions Informed by Engineering Data
Engineering management platforms (EMPs) simplify the process of tracking R&D efficiency by automating many of the processes I was doing manually in previous roles. Using Jira data, an EMP can automatically break down time spent on new features, maintenance, and customer work. Over time, that data makes it much easier for a CFO to recognize the amount of time and effort that needs to be allocated towards different projects.
So where are engineering teams spending their time? According to data taken from the Jellyfish platform, engineering teams on average are spending 49% of their time per year on growth initiatives, 22% on “keeping the lights on” (KTLO) work, another 13% on support and 16% on other miscellaneous tasks.
These are industry averages, but provide helpful benchmarks for determining whether or not your own engineering organization is investing too much or too little in certain areas.
Once you’ve determined where you stand compared to your peers, CFOs should collaborate with product and engineering teams to answer key questions like:
What new initiatives are on the product roadmap?
How many resources (e.g., hours or headcount) will these initiatives require based on historical data?
What is the expected timeline for these initiatives?
An R&D resourcing model also needs to consider when specific initiatives will be completed. Tying specific projects to specific periods makes it easier for finance to forecast when the company needs to hire more aggressively and when it may need to deprioritize some initiatives due to a lack of resources.
The Ultimate Goal: Optimizing R&D Investment for ROI
The ultimate goal for any finance leader is to tie R&D investments directly to business outcomes. How much revenue will a specific project generate? What is its strategic value to the business? Answering these questions allows CFOs to prioritize investments, allocate resources effectively, and maximize ROI.
Attaching revenue projections or other financial outcomes to specific initiatives makes it much easier to discuss which projects should command more investment and which might need to be deprioritized. This should be the ultimate goal for a finance leader when it comes to R&D metrics – tying investments to business value. When you’re able to use data to back up your projections, you can make investments with confidence and eliminate gut feelings or guesswork which results in better ROI.
Key Takeaways
Here are the basic steps any organization can take to make better, data-driven decisions with their R&D resources:
Gather historical data in an engineering management platform: Establish general expectations around how much work your team spends on specific types of projects.
Identify the projects on your roadmap: Work together with your colleagues in engineering and product to lay out the initiatives coming up in the next 12 months.
Factor in maintenance and KTLO work: Make note of how much time your team actually spends building new features as opposed to support and maintenance. Adjust your model accordingly.
Tie staffing decisions to ROI: Estimate the business value of each initiative, then map that back to your headcount projections.
Continue refining your model with data: Check in on the actual data throughout the year and use it to correct your assumptions and improve your projections over the long-term.
By adopting these steps, finance leaders can move beyond traditional guesswork to build a R&D resourcing strategy that delivers measurable business value. As modern tools continue to evolve, the opportunity to optimize R&D investments for growth and innovation has never been greater.
Run the Numbers
Apple | Spotify | YouTube
This is the most impactful episode I’ve done so far on finding, nurturing, and keeping talent.
Jeff Cassidy is a multi-time CFO who’s held the seat across multiple industries, including travel and gaming. We discussed:
If specializing in a sector or business model matters as a finance pro
If the traditional four year college path is dying
How simply “being interesting” is worth more than candidates think
Quote I’ve Been Pondering
“Own the means of production or activation to extract most value, own the means of distribution and be compressed from both sides.”
Try telling a producer for a supermarket or a seller on Amazon that the distributors have no power to squeeze them!