“Great Insights!” Isn’t a Metric

How to measure what actually changed because of your research and what to do when nothing did

Hi, I’m Nikki. I run Drop In Research, where I help teams stop launching “meh” and start shipping what customers fight for. I write about the conversations that change a roadmap, the questions that shake loose real insight, and the moves that get leadership leaning in. Bring me to your team.

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You’ve run a study. You’ve got a deck. There are insights. Quotes. Maybe even a juicy clip or two. And yet something’s off. No one’s acting. Your team smiles politely, mumbles something about “circling back,” and then your findings drift into the digital void.

We’ve all been there.

It’s not that your work wasn’t good. It’s that no one, including you, had a clear way to know if it worked. That’s where success metrics come in.

We’re not talking about KPIs like "# of insights generated" or "level of excitement in the room." We’re talking about hard, clear signals that your research did something.

This article breaks down:

• What success metrics actually mean in user research

• How to pick the right ones

• How to track them (without turning into a PM)

• How to use them to grow your influence, your visibility, and your budget

And yes, B2B, B2C, and internal impact examples are all here.

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Define What “Success” Means Before You Start

(No, “We learned a lot” doesn’t count)

If you’re anything like I was in my first few years, you probably run a research project, wrap it up, send the findings around, and then stare at your Slack hoping someone tells you it was useful.

If you wait until the end of a project to figure out what success looks like, you’ve already lost the chance to shape it. Success isn’t the presentation. It’s what changes afterward.

Here’s how to figure out what success actually means for your research, before you even write your first interview question.

Step 1: Ask the only question that matters

Before you build a screener or open a FigJam, ask your main stakeholder one thing:

“What decision are you trying to make?”

If they say something vague like “We just want to understand our users better,” push again. That’s not a decision. That’s a vibe. Keep digging. Say:

• What’s something you’re stuck on right now?

• What are you debating in your team meetings?

• Is there something that feels risky about what you’re building?

You’re looking for a moment of clarity. A real fork in the road. A choice they need to make but don’t feel confident making yet.

B2C example:

You’re talking to the product lead for a mobile meditation app. They say they want to understand their users better. You ask again:

“Is there a feature you’re debating?”

They say they’re unsure whether to invest in sleep stories or morning focus playlists next quarter. Boom. There’s your decision.

B2B example:

You’re working with a SaaS platform for accountants. The PM wants to “make the dashboard more intuitive.” You ask:

“What do you need to decide?”

They say they’re debating whether to hide advanced filters by default. That’s your research target.

Internal/organizational example:

You’re working inside a company on the design system team. A designer says, “We’re getting feedback that the spacing tokens are confusing.” You ask:

“What do you need to decide?”

They say, “We don’t know if we should rename the tokens or run workshops to teach them.”

Great. Now we’ve got something specific.

Step 2: Turn the decision into a success question

Once you’ve found the real decision, translate it into a success question. Think of this like writing the headline for your project before it starts. It should sound like this:

• Did we clarify the decision?

• Did the team feel confident picking a direction?

• Did the research unlock a concrete next step?

If you want to go further, you can even write it as a fill-in-the-blank sentence:

“This research will be successful if it helps the team ___________.”

Let’s use the examples from above.

B2C – Meditation app:

“This research will be successful if it helps the team choose between sleep stories and morning playlists for Q3 prioritization.”

B2B – Accountant dashboard:

“This research will be successful if it gives the product team enough confidence to hide advanced filters by default, or shows that it would hurt workflows.”

Internal – Design system:

“This research will be successful if it helps the design system team decide whether to rename tokens or invest in onboarding.”

Step 3: Write it down in plain language

Put this success statement somewhere visible.

Don’t just keep it in your head. Don’t bury it in a Google Doc that no one will read. Write it at the top of your research plan. Pin it in your team’s Slack channel. Repeat it every time someone asks what you’re working on.

And during the readout? Put it on slide one. Then repeat it on slide ten. Then end with it on slide twenty. Seriously, people forget. You’re not being annoying. You’re making it stick. Here’s what that might look like in real life:

Slack message you can send at kickoff

“Hey everyone, quick recap before we start sessions. This research will be successful if it helps the team decide between focusing on morning playlists or sleep stories for Q3. I’ll be shaping the sessions around that question, so if anything shifts, let me know now!”

Top of your research plan

Study Goal: Help the team choose the next focus area for content (sleep vs morning). Success = clear recommendation backed by user evidence + action taken in Q3 planning.

Slide 1 of your readout

This study’s success = helping the team pick a direction.

Based on what we heard, here’s what we recommend and what to do next.

If you’re still stuck…

Ask yourself:

• “What would make this study feel worth it a month from now?”

• “If I wasn’t here, what decision might they make blindly?”

• “What’s the worst-case scenario if no one listens to this research?”

The answer to any of those questions will help you write a better success metric.

Pick the Right Type of Metric

Most metrics user researchers are told to track are either impossible to prove or completely disconnected from the actual work.

• “Number of studies run.” Great. And?

• “Number of hours spent interviewing users.” Who cares?

• “Number of insights shared.” Shared where? With whom? Did anyone do anything?

That’s why I only use three types of success metrics now. These are the ones that consistently tell me whether the research mattered. You only need to pick one per study. Just one. That’s it. But pick the right one.

Type 1: Decision Metrics