Prioritizing User Research When Everything Feels Important (Glaringly Obvious)

I dropped a clip from my conversation with Jeanette Fuccella, who created a practical 2x2 matrix to help teams right-size their research effort. It’s a helpful way to figure out how much research you actually need…especially when everything feels like a priority.

This was my 5-6th interview, so I’m getting my footing… but it’s tough to get the right feel.

In the episode, Jeanette shares how her framework helps teams decide when to go deep, when to move fast, and how to have better conversations around research.

“It started as a self-preservation tool. There are always more research questions than time.”

She breaks research down by two things:

  • Problem Clarity – how well you understand the user’s problem
  • Risk – what happens if you get it wrong

This creates four research modes:

  • Ship It & Measure (High Clarity, Low Risk)
  • Design Heavy (High Clarity, High Risk)
  • Research Light (Low Clarity, Low Risk)
    Research Heavy (Low Clarity, High Risk)

In my experience, many teams don’t skip research because they don’t care. They skip it because they don’t know how much is enough.

Jeanette’s matrix gives teams a shared way to make those tradeoffs, without overthinking it or defaulting to “no time, no research.”

And it fits perfectly with how we use UX metrics. If you can measure how clear the problem is (or how well users understand your idea) you’ll know when quick feedback is enough and when it’s worth investing more.

:speech_balloon: Discussion

How do you decide when it’s worth doing deeper research?

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Did a follow up post on this, focusing on vibe coding and how it changes the order of research…

Does vibe coding just push the problem back to a design heavy exercise? Curious on people’s thoughts. @mschindler, I know you’ve written a bunch about these areas.

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I think that this is still pretty relevant today. @MoData, I wonder if you have any thoughts about Jeanette’s 2x2 research prioritization matrix?

As a data nerd, I don’t love how the top two quadrants lean towards deprioritizing research when the problem clarity is high. Feels more like the problem clarity is being assumed rather than validated. Also not the biggest fan of vibe coding, so I think my aversion to this is because I have a logic-heavy, research-driven mindset.

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Love this! So many teams skip research not from neglect but from not knowing how much is enough. Clarity-based metrics make that decision so much easier.

We shared this grid again.

Worth considering whether this is even more or less important in AI design. Curious @Doug_Curtis, your thoughts, as this is how we overlapped!

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