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We built a survey to learn what our users needed most. We launched it, shared it, and waited for insights to roll in.
But what we got back was… underwhelming. Sparse replies. Vague answers. Conflicting signals.
Sound familiar?
Surveys are supposed to help you make better decisions. But more often than not, they leave you with more questions than answers.
After years of running research for early-stage products and global brands alike, I’ve seen this play out over and over—good intentions lost to poor execution. But instead of blaming the users or the methods, we need to take a hard look at how we’re approaching surveys in the first place.
Here’s why our survey didn’t work—and what we’ve learned about fixing it.
We thought we were collecting meaningful feedback. But what we actually got was shallow sentiment—data that looked solid on a dashboard but had no depth.
For example:
That told us nothing actionable.
It wasn’t until we ran follow-up interviews that we discovered what “okay” actually meant: “confusing and inconsistent.” Users didn’t know how to explain their experience in a form, so they defaulted to vague language.
Lesson: If your questions only scratch the surface, don’t be surprised when the answers do too.
Our survey sat ignored in people’s inboxes—with no clear payoff for respondents. So most ignored it.
Why do surveys get ignored?
One client—a fintech app—sent a 22-question NPS follow-up to SMB users. Fewer than 3% replied.
But when we:
…completion increased to 13%.
Takeaway: Getting people to respond is hard. Work hard on timing, format, and incentives.
We caught ourselves writing questions that assumed too much or steered answers.
Examples:
These aren’t neutral—they’re marketing disguised as research.
We also saw confusion:
That one caused more head-scratching than clarity.
Lesson: Remove assumptions, adjectives, and jargon. Write like you're genuinely curious—not fishing for validation.
We asked:
“What did you think of the dashboard?”
We got:
“It’s fine.”
End of story.
It wasn’t the user’s fault. It was ours. We asked without context.
Instead of:
🛑 “What did you think of the dashboard?”
Try:
✅ “When was the last time you used the dashboard? What were you trying to do, and how did it go?”
You’ll get fewer filler words—and more real stories.
Even a well-written survey can flop if it hits the wrong people—or lands at the wrong moment.
We’ve sent product feedback surveys to:
Result? Useless or nonexistent responses.
Fix it with behavioral triggers:
Right person + right moment = better signal.
We stopped blasting the same survey to everyone—and wondered why half the responses didn’t make sense.
Now, we tailor each survey to match where someone is in their journey:
Examples:
We also personalize incentives:
Result: Higher response rates, better data, and more trust.
Instead of sending a long survey weeks later, we now embed 1–2 question surveys at key touchpoints—when the experience is fresh.
Here’s what that looks like:
Behavioral tools like Intercom, Mixpanel, Hotjar help automate this based on what users actually do.
Impact: Higher response rate, better clarity, and no memory gaps.
We couldn’t talk to every user. But we didn’t have to.
With UserCall, we set up AI-moderated voice interviews to automatically follow up with key segments.
How it works:
Especially useful for:
Result: We finally started hearing the story behind the numbers—without booking a single call.
Surveys are great for scale—but they rarely explain why users behave the way they do.
We now layer in three levels of follow-up:
This mixed-methods approach lets us:
We ran a survey expecting insights—and got vague responses, low completion, and more questions than answers.
Turns out, the problem wasn’t the audience. It was how we approached it.
When you combine survey scale with smarter timing and qualitative depth, you stop guessing—and start making decisions with confidence.
