I was recently asked exactly that by someone, and I thought I would share my answer broadly because I think it’s a great question and one I’ve seen junior product and research folks struggle with.

The truth is, there are several ways to ensure your research recommendations lead to real projects with impact. But I think that what you do in this realm is also as important as what you don’t do, so I’ll attempt to cover both. Here is what I do when setting up research operations and subsequently coaching those I’m helping with.

1. Take a stance

Researchers should take a stance. After completing research, they should say “This is how I would use this,” and “This is what I recommend.” This is the easiest thing you can do after completing research, yet in my experience, it’s often the most forgotten. Never assume what your research communicates or suggests should be obvious. It’s your job to articulate that.

2. Stay plugged into what the business actually cares about

You should already be informed on what the business and product strategies are and plugged into the roadmap (a good leader is making sure you understand the roadmap, strategy, and target outcomes front to back). This helps you connect existing research to what’s important to the business properly and propose new research accordingly.

3. Translate the research into actionable pieces

It’s often not enough to simply communicate the research and your stance on it. Researchers should help translate research and insights into actionable user stories, acceptance criteria, and other actionable pieces. As a researcher, you become an expert on a particular problem or opportunity, so you should dig in and help translate it, educating and exciting others along the way.

4. Make it measurable

The items that may be candidates for prioritization are always more likely to be greenlit if the outcome is measurable and people understand why it’s important. For every recommendation in this case, a researcher should provide a Definition of Done and a Target Metric.

E.g.:

  • Reduce signup-to-activation drop-off from X% to Y%
  • Cut support tickets about [specific feature] by 30% within two quarters
  • Lift D30 retention by N points among [segment]
  • Increase task completion rate for [core workflow] from 60% to 80%

5. The naughty list

Do not do the following:

  • We don’t do data dumps that no one will ever read.
  • We don’t recommend “more research” as the primary action to follow research (remember, take a stance).
  • We don’t make suggestions without considering product and team context and constraints.
  • We don’t soften negative findings (even for sensitive executives).
  • We don’t focus on features instead of outcomes.

6. Align to what the business actually measures

I think RICE and Impact/Effort matrices are fine as a floor. They’re easy to teach, easy to apply, and they get research onto a roadmap conversation. But they’re also generic! They don’t know anything about your org’s strategy, and they treat every initiative as if it lives in a vacuum. So, yes, start there, but if you can move into more customized territory, I do recommend it.

If your org has its own prioritization machinery, like OKRs, SMART goals, Rocks, whatever, your recommendations should map to it. Not as a courtesy, but because that’s the language leadership uses to decide what gets resourced. A recommendation tied to a current OKR moves faster than something out of left field, no one has heard of before, and has to work to decipher. A recommendation that sits next to an OKR gets “great, let’s revisit next quarter.”

When I have room to push past RICE, I frame recommendations along three axes:

  • Opportunity or risk? Is this a growth play or a loss-prevention play? The conversation and the urgency are different for each.
  • Which segment will this affect? Start with TAM, SAM, or SOM, and get more specific from there if you can. Also include a rough estimate of how many users or accounts.
  • Estimated impact. Same spirit as RICE’s, but informed by the two answers above.

Sometimes research itself becomes the input that shapes the next outcome. I’ve taught product teams to derive research opportunities and SMART goals from Teresa Torres’ Opportunity Solution Trees, starting from the product outcome and working backward, which kept us aligned without a separate prioritization fight. If your research surfaces a gap nobody’s named yet, that gap can become the next OKR. That’s the highest-leverage version of this work.

Back to the main question

So, how do you ensure your research recommendations lead to real projects with impact?

You take a stance. You stay close enough to strategy that your recommendations land in the language leadership already speaks. You translate insight into the artifacts product teams actually use. You make outcomes measurable. And you stop sending data dumps into the void.

Research is a recommendation, not a report. Treat it like one.