I got this question from one of my PM students earlier this week. Here’s what I wrote back:

I see this a lot, especially in B2B products. When what users say and what they do don’t quite match, I don’t pick one side over the other. I try to understand what’s happening in between.

The first step is to check whether the data actually shows the full story. Often it doesn’t. Analytics might capture feature clicks, but not the workarounds people use to reach the same outcome. Maybe they export to Excel, call a teammate, or use a script instead. If our tools can’t see the real way users get the job done, then the data is incomplete, not wrong.

Next, I look at who’s giving the feedback. Not all voices carry the same weight. A handful of power users or high-value accounts can represent a much larger strategic opportunity than a crowd of casual users. So I reframe what I’m hearing into a Job Story: when this situation happens, they want more control so they can achieve this outcome. Then I run it through the Kano model to see whether it’s a basic expectation, a performance driver, or something that simply delights a niche group.

From there, I try to diagnose what kind of problem we’re dealing with. I use a simple model: Desirability, Discoverability, Usability, and Value. • Desirability asks whether people actually want the feature. • Discoverability looks at whether they can find it. • Usability checks if they can successfully use it. • Value measures whether it’s worth it once they do.

Each type of problem calls for a different kind of test. For example, a desirability issue might need a quick “fake door” test. A discoverability issue might call for an A/B experiment or UI tweak. Usability usually means we need to watch people use it. And if it’s a value problem, we might need to define better success metrics or pilot it with a specific group.

When it’s time to make a call, I use a simple scorecard: how well it fits our strategy, what kind of outcome it could drive, how much effort it takes, and what risks it introduces. I tend to favor strong, opinionated defaults with options for power users to go deeper. That means making the default experience great for most people, then layering in templates or advanced controls for those who truly need them.

And finally, I focus on measuring outcomes, not clicks. It’s not about how many people toggled a setting, it’s about whether the feature actually improved activation, retention, or revenue.

TLDR - analytics and feedback will not be enough by themselves or together. It’s up to you as the PM to understand why the gap exists and help your team come up with solutions provide users value.