When planning UX research, choosing between an interview vs survey approach—and selecting the best questions for UX research—can make or break your insights.
Interviews allow for deep exploration but are time-intensive, while surveys scale better but traditionally lack depth.
This article will show how to craft questions for both formats, and how conversational surveys can bridge the gap.
Understanding the interview vs survey question dynamic
Choosing between an interview or survey for UX research isn't just about logistics—it's about how you approach questioning itself. Moderated interviews let you dig into users' stories, pivot directions, and clarify ambiguities on the fly. In contrast, traditional surveys usually rely on rigid question flows and fixed wording, giving you scale but not necessarily context.
Let me show you the key differences at a glance:
Interview Questions | Survey Questions |
|---|---|
"Can you describe a time when you found our product difficult to use?" | "On a scale of 1-5, how difficult is our product to use?" |
"What features do you wish our product had?" | "Which of the following features would you like to see added to our product?" |
Open exploration: In interviews, I might start with "Tell me about..." and then let the user's story unfold, asking new questions as details emerge. In surveys, prompts must be more specific and self-contained, since I can't probe in real time.
Follow-up flexibility: Interviews let me change course mid-conversation, diving deeper or clarifying confusion on the spot. Traditionally, surveys offer little flexibility—the path is static. This limits discovery of the unexpected. And this isn't just theory: Poor question design is a top reason for unusable UX research results, according to Nielsen Norman Group research, which found weak question phrasing leads to ambiguous, low-quality feedback that can misguide design teams [1].
Best questions for UX research: dual-format examples
Great UX research questions surface deep understanding, whether delivered in a live interview or through an AI-powered conversational survey. Here’s how I'd translate common UX research objectives between formats:
Feature discovery:
Interview: "Walk me through how you currently handle [task]."
Survey: "What's your biggest challenge with [feature]?"
Pain point identification:
Interview: "Tell me about a time when our product frustrated you."
Survey: "Which of these issues have you experienced? (with follow-up: Can you describe what happened?)"
Usage patterns:
Interview: "Show me how you typically use [feature]."
Survey: "How often do you use [feature]? What do you use it for?"
Value perception:
Interview: "If our product disappeared tomorrow, what would you miss most?"
Survey: "What's the most valuable part of our product for you?"
Notice the microcopy shifts: interviews invite storytelling; surveys ask for concise, focused feedback. Yet with the right approach—especially using conversational AI—I can draw out rich stories in both formats.
It’s worth mentioning: Open-ended survey questions, when worded well and paired with intelligent follow-ups, can elicit qualitative insight rivaling interviews. As Gartner notes, 81% of organizations using AI-driven analytic tools report uncovering deeper customer needs and emotions compared to classic methods [2].
How conversational surveys bridge the gap
Modern AI survey tools—like those built with AI survey generators—blur the lines. They combine scale with probing, letting you replicate that “tell me more” moment from human interviews, but at survey scale. This is where automatic AI follow-up questions come in: the AI listens to each user's response, then asks smart follow-ups, drilling for clarity, context, or emotion.
Dynamic depth: Unlike traditional forms, conversational surveys react to the user’s answer. If someone signals a pain point, the AI dives deeper: “Can you share more specifics?” or “What would have made that experience better?”
Natural tone: The AI adapts its language. Instead of robotic phrasing, it chats like a peer, making users more comfortable to open up, which—according to Forrester’s research—increases the length and richness of text responses by up to 42% [3].
Here’s how this feels in practice:
Initial question: "What's your biggest challenge with [feature]?"
User response: "It's hard to navigate."
AI follow-up: "Could you share a specific instance when navigation was challenging?"
Instead of just capturing a complaint, you get context, examples, even suggestions—making it a true conversational survey. For more on tuning dynamic follow-ups, see how Specific does automatic AI follow-up questions.
Crafting UX research surveys with precision targeting and tone
Specific’s in-product conversational surveys don’t just ask questions—they adapt to your audience, product, and timing. This is a game-changer for UX research:
Targeting the right moments: Trigger surveys exactly when insights matter—after a user explores a new feature, completes a workflow, or closes a support ticket. Targeting not only boosts response rates, but also ensures feedback is fresh and context-rich.
Tone variations: The same question can come across as executive-formal or chatty-fun. For enterprise users, I might ask:
“We value your feedback. Could you share your thoughts on our new feature?”
For consumer apps, a more casual vibe works:
“Hey there! What do you think of our new feature?”
I can also tune follow-up depth per question. For critical moments—like onboarding—I might enable extra probing; for quick polls, I set “one and done.” This flexibility helps you get rich stories where you need them, and avoid fatigue elsewhere.
And if you want to try this fluid survey-making, the AI survey generator lets you build tailored surveys with a simple conversation—no form builders or logic trees needed.
Analyzing qualitative insights at scale
You’ve collected responses—now what? Traditionally, interview analysis means transcripts, thematic coding, searching for patterns by hand. It's insightful, but slow and manual. Conversational survey platforms like Specific fundamentally change this: AI-powered survey response analysis summarizes each answer, finds themes across hundreds of responses, and lets you interact with your data as easily as you’d chat with GPT.
Instead of staring at endless open-text, you can run flexible queries to extract insights for product, UX, or CX. Here are some example prompts you can use to analyze results:
Finding feature gaps:
What features are users asking for that we don't currently offer?
Understanding user segments:
Group responses by user type and summarize their different needs
Improving onboarding:
Summarize users' reported confusion during onboarding and suggest improvements
With this approach, you can analyze qualitative feedback as you collect it, making your UX team as nimble as your users expect.
Making the switch: from interviews to scalable conversations
Ready to move from human-moderated interviews to rich, conversational surveys? Here’s my playbook for making the leap:
Start with your interview guide: Use it to draft questions, then adapt for conversational survey prompts.
Test tone and follow-up depth: Send your survey to a small group to calibrate AI language and follow-up logic.
Use targeting to replace screeners: Filter respondents in-product, so you’re always reaching the right users at the right moment.
Ready to transform your UX research? Create your own survey and start collecting interview-quality insights at scale.