Ux user interview questions: great questions for feature discovery that reveal what users really need

When it comes to feature discovery, asking the right ux user interview questions can make the difference between building something users tolerate and something they genuinely love. Great questions illuminate real needs—not just nice-to-haves.

This guide digs into strategies for uncovering hidden motivations and unmet needs, going far deeper than any static form can. With AI-powered conversational surveys, you can trigger natural, probing dialogue that surfaces insights impossible to get with multiple-choice forms. We'll walk through JTBD-style questions, how to configure AI follow-ups, and using persona-based analysis to turn responses into product direction.

Understanding what users really want with JTBD questions

The Jobs-to-be-Done (JTBD) framework is the secret weapon for meaningful feature discovery. Instead of fixating on what users say they want, JTBD digs into the job they're trying to get done—the underlying outcomes and pains that guide real-world behavior. Companies like Airbnb and Amazon have leveraged this approach for breakthrough innovation, and it delivers results: the Outcome-Driven Innovation method (founded on JTBD) has an 86% success rate, compared to the traditional innovation process's 17% [1].

Progress questions: These go deep on what users are really trying to achieve and what holds them back. Asking about progress helps you understand not just pain points, but also the desired change that motivates adoption. For example: "What has changed recently that made you look for a solution?" or "What are you trying to accomplish that you can't do easily right now?"

Push questions: These find the friction—what's so frustrating or inefficient about current tools that your users want something better? Aim for: "What was the last straw with your previous solution?" or "What annoys you most day-to-day about your current process?"

Pull questions: These pinpoint what draws users to new tools or features, revealing what’s genuinely enticing. Try, "What excited you when you first heard about [feature/product]?" or "What made you try this solution instead of another?"

Anxiety questions: These dig up everything that could make adoption risky or stressful. Uncover doubts with questions like: "What concerns did you have before switching?" or "What hesitations do you still have about fully adopting this?"

Need help designing these questions? Use the AI survey generator to quickly create JTBD-inspired interviews that fit your context, letting AI suggest nuanced follow-ups for your core discovery goals.

Essential ux user interview questions for uncovering hidden needs

Effective feature discovery isn’t about asking “What features do you want?” Instead, it’s about exploring pain, context, and motivation with questions that open up rich conversation.

Here’s a playbook of proven question types and exact example prompts for analyzing surveys and digging deep. Feel free to adapt them to your AI survey builder for conversational flow:

Context-gathering questions: Map out the existing workflow and environment. These ground your understanding in how users really work—vital for meaningful insights.

What does your current workflow look like when trying to accomplish [task]? Can you walk me through each step?

Problem-identification questions: Pinpoint true frustrations, inefficiencies, or blockers. This is where great products are born.

What’s the most frustrating part of your current process? Can you share a recent example?

Solution-exploration questions: Move beyond assumptions—validate ideas before you build. Dig into what resonates and what doesn’t.

If you could wave a magic wand and instantly fix or improve something, what would it be? Have you tried finding a workaround?

Priority-assessment questions: Discover what users actually care about most, so you can focus on features that make a difference.

Of all the challenges you’ve mentioned, which one would you want solved first? Why?

Automatic, contextual follow-up questions transform the AI survey from a static form to a lively, iterative discovery conversation—essential for surfacing nuance and unexpected insight. With automatic AI follow-up questions, you ensure nothing slips through the cracks, as the survey adapts in real time to each response.

Configuring AI to probe deeper during feature discovery

With AI surveys, you set the rules—and AI does the digging. Think of the AI interviewer as an experienced UX researcher: it knows how (and when) to ask “why,” clarify, and surface context that static surveys simply miss. That’s why a whopping 73% of UX professionals report AI positively impacts user research [2].

Clarification prompts: When you get vague or ambiguous answers, these nudge the user for precision. “Can you elaborate on what you mean by ‘easy to use’? What makes something easy for you?”

Use case exploration: Ask the AI to request concrete, real-life examples. “When you say you struggle with scheduling, can you tell me about the last time this caused a problem?” This turns wishy-washy statements into actionable scenario data.

Constraint identification: Probe hidden limits and workarounds. “Have you developed any shortcuts to avoid this bottleneck? If yes, how much time do they save you?”

When configuring your AI survey, give clear instructions on follow-up behavior to maximize discovery:

If a user mentions a workaround, ask how often they use it and how they feel about it. If they seem unsure or hesitant, ask what would make them more comfortable with a new solution.

When a user shares a feature wish, probe for real-life situations where this feature would have changed their outcome or saved time.

Set up these follow-up flows quickly in the AI survey editor—just describe your probing rules in plain language, and the AI adapts in real time. No scripting required.

Segmenting feature insights by user persona

The best product teams never treat all users the same—because feature needs change dramatically by segment. One size fits all always fits nobody. That’s why breaking down discovery insights by persona is crucial.

With analysis chats, you can filter and chat with AI about responses for any user segment—surfacing patterns, priorities, or pain points unique to each group. According to recent research, 74% of UX experts say AI-driven analytics lead to more actionable insights than traditional methods [2]. Here’s how to approach segment analysis:

Power user analysis: Filter for advanced users (frequent, heavy, or expert) and ask the AI to find unmet needs or suggestions only they share.

Analyze only responses tagged “power user.” What workflows or features do they use that others don’t? What suggestions come up repeatedly?

New user analysis: Focus on onboarding friction or adoption barriers by filtering for first-timers or recent signups.

Show me pain points described by new users in their first two weeks. What gets mentioned as confusing or hard to find?

Specific role analysis: Segment by job title, department, or other role data to find opportunities for targeted solutions.

Filter responses by “sales manager.” What unique feature requests or frustrations are shared within this role that differ from the general audience?

Use the AI survey response analysis chat to dive into these perspectives in parallel, rapidly uncovering insights. Creating multiple chats for different angles means you never miss the nuances that drive great product-market fit.

Turning discovery insights into action

Once you’ve collected discovery data, here are some quick tips for making it actionable:

• Prioritize feature ideas that solve the most widespread or urgent pains across segments.

• Stack rank features against actual user quotes and scenarios, not just requests.

• Commit to continuous discovery—don’t make it a one-off project, but a habit with evolving, regularly-updated surveys.

Here’s a quick comparison to help frame how AI-powered methods stack up against traditional interviews:

If you’re not running these conversational AI surveys, you’re flat-out missing what your users really need—and probably leaving feature opportunities (and market share) on the table. Take action now: create your own survey using these techniques and start transforming insights into product moves your users will actually notice.