That Time I Shipped a Feature Nobody Actually Wanted

I was so proud of it. Three months of development, countless stakeholder meetings, and what I thought was rock-solid user research backing a feature that was going to be a game-changer. The rollout went smoothly, no bugs, clean implementation—everything a PM dreams of.

Then I checked the usage analytics two weeks later.

8% adoption. Eight percent. Of our most active users, only 8% had even tried the feature. Of those who tried it, most used it once and never came back. I stared at my dashboard feeling like I'd just been punched in the gut.

The Feature That Wasn't

Without getting too specific about the product, let's call it a "smart scheduling assistant." The idea came from what seemed like a perfect storm of validation. Our customer success team kept mentioning scheduling conflicts in user feedback. Sales was getting questions about calendar integration. I'd personally experienced the pain point myself—trying to coordinate meetings across time zones while juggling different calendar systems.

The user interviews seemed to confirm it. When I asked about scheduling challenges, people lit up. "Oh my god, yes, it's such a nightmare," they'd say. "I spend so much time going back and forth on email." Classic pain point validation, right?

I put together user stories, wireframes, the whole nine yards. Leadership loved it. Engineering was excited about the technical challenge. We even had a few beta users who seemed enthusiastic during early demos.

But here's the thing about customer discovery—there's a massive difference between agreeing that something is a problem and actually changing your behavior to solve it.

Where I Went Wrong

Looking back, my mistakes were painfully obvious, but they felt so subtle in the moment:

I asked leading questions. "How much time do you spend coordinating schedules?" practically begs for a complaint. Of course people are going to tell you it's a pain point when you frame it that way. What I should have asked was broader: "Walk me through how you typically set up a meeting with someone external."

I confused vocal frustration with actual priority. People complain about scheduling the same way they complain about traffic or long grocery store lines—it's universally annoying, but that doesn't mean they're actively seeking solutions. The real question isn't "Is this annoying?" but "Is this annoying enough that you'll change how you work?"

I fell in love with my own problem. Because scheduling was a genuine pain point for me personally, I assumed others shared my level of frustration. I was projecting my own needs onto the user base instead of staying objective about what they actually needed.

I didn't test behavior, just opinions. Every piece of validation I collected was hypothetical. "Would you use this?" "Does this seem helpful?" I never asked anyone to actually change their current workflow, even temporarily, to test whether they'd really adopt a new solution.

The Uncomfortable Conversations

The worst part wasn't the low adoption numbers—it was the conversations afterward.

"I thought you said users were asking for this," my engineering lead said during our retrospective. He wasn't being accusatory, just genuinely confused. And he was right. I had said that. I'd presented the feature as user-driven when really, it was assumption-driven.

The customer success team was diplomatic but puzzled. "We're still getting the same scheduling complaints," one of them mentioned. "It's like the feature doesn't exist for most users."

That stung because it was true. We'd built something adjacent to the real problem. Users were complaining about scheduling, but what they really meant was they were frustrated with specific people who were bad at responding to emails, or with clients who kept changing meeting times. Our "smart scheduling assistant" didn't solve those human problems—it just added another step to their workflow.

What I Should Have Done

The fix wasn't better user research—it was different user research. Here's what I learned to do instead:

Test micro-behaviors before building macro-solutions. Instead of asking "Would you use a scheduling assistant?", I should have said, "Here's a simple scheduling link tool—can you try using it for your next three external meetings and tell me what happens?"

Follow the breadcrumb trail. When someone says scheduling is a pain point, the next question shouldn't be "What would solve this?" It should be "Show me the last time this happened." Then you dig into the specifics of that exact situation.

Look for people already hacking solutions. The users who would actually adopt a scheduling feature are probably already using workarounds—shared calendars, booking links, assistant coordination. Find those people first.

Separate nice-to-have problems from painful problems. A painful problem is one people are already spending time or money trying to solve, even imperfectly. A nice-to-have problem is one they complain about but haven't tried to fix.

The Real Lesson

The hardest part of this whole experience wasn't admitting I'd misread the market—it was accepting that good intentions and solid processes can still lead to wasted effort. I'd followed all the "right" steps: user interviews, stakeholder alignment, iterative development, data-driven decisions. But I'd optimized for validation instead of truth.

The feature didn't get killed immediately. We spent another month trying to boost adoption with better onboarding, email campaigns, and UI improvements. Nothing moved the needle significantly. Eventually, it became one of those features that exists in the product but that nobody talks about—a monument to good intentions and poor discovery.

These days, I'm much more paranoid about the difference between what people say they want and what they'll actually use. I push harder on specifics during user interviews. I look for evidence of people already trying to solve a problem, not just evidence that a problem exists.

And I've gotten comfortable with that uncomfortable moment in user interviews when someone says, "Actually, now that I think about it, this isn't really that big of an issue for me." That's not a failed interview—that's discovery working exactly as it should.

The feature nobody wanted taught me that customer discovery isn't about confirming your ideas—it's about killing the bad ones before they become someone else's problem to maintain. Sometimes the most valuable thing you can discover is that you shouldn't build something at all.