Voice of customer research is the foundation of successful feature discovery, but asking the right questions is what separates good products from great ones.
In this article, I’ll unpack the best questions for feature discovery—the kind that reveal hidden needs and validate ideas—plus why well-timed, contextual feedback beats old-school forms every time.
Open-ended questions that reveal hidden feature needs
Open-ended questions are my go-to for uncovering unmet needs because they let customers speak freely—no boxes to tick or prompts to bias answers. They invite stories, frustrations, and even surprising workarounds I might never have considered. Here are a few I use most:
“What’s the most frustrating part of [current process]?”
This question surfaces pain points in the customer’s own language—gold for discovering areas ripe for improvement.
“If you had a magic wand, what would you change about [product area]?”
By encouraging customers to dream, you get a glimpse into their ideal solutions, not just fixes to today’s problems.
“Describe a time when [product] didn’t meet your expectations.”
Detailed stories here reveal emotional triggers and unmet use cases.
“Is there something you wish our product could do—no matter how ambitious?”
This prompt opens up the floor to untapped opportunities or wild ideas worth validating.
Sometimes, customers hint at pain but don’t elaborate. That’s where AI-powered follow-up questions come in, probing deeper the way a sharp interview would. Tools like automatic AI follow-up questions let you instantly dig into specifics and context, boosting both richness and accuracy of your findings.
Timing matters: Capture this feedback right after customers finish important actions in your product. You’ll get fresher, more specific insights—often details they’d forget by the next day. It’s a proven way to raise response quality and make every answer actionable.
78% of customers base purchases on the quality of their experience, so surfacing these open conversations is foundational to building what truly matters.[1]
Contextual questions for validating feature concepts
Not every idea you discover deserves a spot on your roadmap. Validation questions help you learn whether a proposed feature actually solves a real customer problem, instead of being a “nice-to-have.”
I approach this from a “jobs-to-be-done” angle—focusing on what customers aim to accomplish, not just which buttons they click. These are my must-ask validation questions:
“How are you currently solving [specific problem]?”
Customers often use clever workarounds. Understanding these gives me clarity on pain level and urgency.
“When would you use this new feature in your workflow?”
This pinpoints relevance and likelihood of real adoption.
“What would success look like for this feature?”
I love this question—it uncovers acceptance criteria, what ‘done’ feels like, and helps prioritize features that offer tangible wins.
“What concerns might you have about this feature?”
Grumbling here signals obstacles or areas needing further thought.
Type | Purpose | Examples |
Discovery | Uncover new opportunities and unmet needs | “What's the biggest struggle with your workflow?” |
Validation | Test and prioritize concepts based on real use cases | “How are you solving this now?” |
Context is everything. With an AI survey builder, you can tailor surveys to specific customer segments—so power users, first-timers, or churned customers see questions geared exactly to their experiences. It’s the difference between guessing and knowing.
In-product surveys get extra mileage here—they pop up when a feature is new or after a task is completed, so feedback is anchored in recent experience, not fuzzy memory. That’s proven to boost both accuracy and engagement. In fact, AI and chatbot-based surveys can yield up to 80% completion rates, blowing away traditional forms.[3]
Behavioral triggers that capture feature insights
One of the biggest levers I’ve found is asking questions at just the right moment. Well-placed behavioral triggers don’t just raise completion rates—they match questions to intent, surfacing the why behind every click or tap. Here are the triggers I lean on for feature discovery surveys:
After users abandon a workflow
Prompt: “What made you stop before finishing?” uncovers blockers and missing features.
When users repeatedly use workarounds
Prompt: “Is there something you wish our product handled directly?”—find workflow friction here.
Following support ticket submissions
Prompt: “Was there something missing that led you to contact support?” identifies feature gaps in real time.
After feature trial periods end
Prompt: “What would have made you use this feature more?” gets you insight into adoption barriers.
Specific’s in-product conversational surveys are built for this. You can trigger chat-based questions automatically and nudge users when they’re actually thinking about a problem—which matters, since behavioral timing has a direct impact on survey engagement and insight depth.
Conversational surveys feel natural in these moments because they echo how any product manager or researcher would check in during user testing: a brief, empathetic question in context, not a giant form out of the blue. AI can even adapt follow-ups immediately, based on what a user just did or said, capturing unique “aha” moments as they happen.
AI surveys triggered by behavior can drive 40% higher response rates than generic email surveys, while users are 2.5x more likely to leave detailed answers in conversational flows.[3]
Turning customer conversations into feature roadmaps
Let’s be honest: collecting loads of feedback is only half the battle. The real challenge—and opportunity—is synthesizing all those candid conversations to find the patterns, signals, and must-build features.
AI-powered analysis helps me move from chaotic notes to clear priorities, even with hundreds of open-ended responses. Here are example prompts I use when digging into feature discovery data:
What are the top 5 unmet needs mentioned by power users?
Which proposed features got the most enthusiastic responses and why?
You don’t have to choose just one focus, either. With AI survey response analysis, you can spin up multiple analysis threads—one for user onboarding, another for workflow blockers, a third for pricing feedback—each exploring a different angle, each learnable over chat.
Theme extraction is a superpower here: conversational responses are loaded with nuance, and AI catches recurring pain points or aspirations that rigid forms miss. It’s a shortcut to insight—especially when execs want quick wins and you want evidence-backed priorities.
Finally, AI-generated summaries make it easy to share topline findings with engineers and stakeholders, keeping everyone aligned on what users really want.
Customer-centric businesses that actively analyze and act on feedback see up to a 25% increase in profitability, and 83% of customers say they feel more loyal to brands that resolve their issues based on feedback.[2]
Start discovering what your customers really need
The right questions, at the right moments, can transform feature discovery—uncovering not just what customers say, but what they truly mean and want.
Conversational AI surveys routinely capture 3–5x more actionable details than old-school forms, and help teams zero in on features that move the needle.
With Specific’s AI, you can craft questions and follow-ups that surface hidden needs—as soon as users show them. Create your own survey and turn everyday feedback into breakthrough products.
Every day without these conversations is another day spent building features customers might not want. Voice of customer research, especially through in-context, conversational surveys, is how teams build products people truly love.