When conducting user research, choosing between interviews vs surveys often feels like a trade-off. Interviews give you deep insights through probing questions, but they’re time-consuming and tough to scale. Surveys collect lots of responses quickly, but generally miss out on nuanced, specific feedback because they can’t ask “why” or clarify ambiguity.
Now, a new class of conversational AI surveys bridges the gap, offering the best of both worlds—depth and scalability. In this article, I’m sharing the best questions for user research, showing how you can use conversational AI to capture the rich insights of interviews in a fraction of the time.
The traditional interview vs survey dilemma
Interviews let you dive deep. You can follow up in real time, ask for clarification, and pivot based on what’s interesting—you get the story behind the answer. But you’re limited by time and resources; you can’t talk to hundreds of people one-on-one.
Surveys, by contrast, are fast and scalable. Everyone gets the same set of questions, and you can analyze trends quickly. But you miss the chance to ask “why?” or explore surprising ideas, so the data feels thinner.
Interview strengths | Survey strengths |
|---|---|
Probe for details in real time | Scalable to large groups |
Clarify ambiguous answers | Easy to compare results |
Explore unexpected topics | Automated data collection |
This impacts your research quality: interviews are rich but inefficient, while surveys deliver quantity over quality. In both cases, you may be forced to compromise—unless you’re using a modern AI survey builder, which can ask smart, real-time follow-up questions at scale. These tools combine the structure and reach of surveys with the probing power of an interviewer, substantially improving both depth and breadth of your user insights. In fact, AI-driven conversational surveys are already being adopted fast—McKinsey reported that 78% of organizations use AI in at least one business function, with user research on that list. [1]
Best questions for user research: Interview probes vs survey formats
Let’s talk about practical questions you should ask—and how the format changes your results. For each research scenario, I’m showing:
Interview probe: the classic “conversation” approach
Survey version: a flat, traditional survey question
AI survey follow-up: how a conversational AI survey captures the richness of an interview in survey form
Discovery Research Questions
What are your biggest challenges with [product/service]?
Interview: "Tell me more about the challenges you face. Can you give an example?"
Traditional survey: "What are your biggest challenges with [product/service]? (Open-ended)"
AI survey: Asks the open question, then follows up: "Why is that challenging for you? How does it affect your daily work?"
What solutions have you tried before?
Interview: "How did those solutions work for you?"
Traditional survey: "Which alternative solutions have you tried? (List or open text)"
AI survey: If a solution is listed, probes: "What was your experience with [specific solution]?"
AI survey follow-ups keep nudging for the context and ‘why’, recreating the benefit of an in-person interview.
Churn Analysis Questions
What prompted you to consider leaving [product/service]?
Interview: "What happened that made you start to think about leaving?"
Traditional survey: "What made you consider leaving? (Open-ended or select)"
AI survey: After the initial answer, may ask: "Can you walk me through what happened?" or "Was this a sudden decision or gradual?"
Is there anything we could have done to keep you?
Interview: "What would that have looked like?"
Traditional survey: "What might have changed your mind? (Open-ended)"
AI survey: If a suggestion is given, asks: "Can you expand on that? How important is it to you?"
Feature Validation Questions
How would [new feature] impact your use of [product/service]?
Interview: "How might your workflow change if that existed?"
Traditional survey: "Would [feature] motivate you to use our service more? (Yes/No/Open-ended)"
AI survey: If positive, follows up: "Which parts would be most valuable to you?" If negative: "Is there something missing or unnecessary for your needs?"
What concerns do you have about [feature]?
Interview: "Have you used anything similar elsewhere?"
Traditional survey: "What concerns (if any) do you have about [feature]? (Open-ended)"
AI survey: After a concern is listed, asks: "How significant is this concern for you?" or "Tell me about any issues you’ve faced with similar tools."
If you want to generate or customize these questions instantly, try the AI survey generator—it’s as simple as chatting with an expert researcher.
How AI surveys generate interview-style follow-ups automatically
What makes conversational AI surveys unique is their ability to generate smart, contextual follow-ups—just like a savvy interviewer. Instead of collecting static answers, the survey interprets each response and asks clarifying questions or digs deeper for motivation or specifics.
For example, if someone mentions “setup was confusing,” a conversational AI survey can instantly ask “What part of the setup did you find unclear?” or “Did you search for help when you got stuck?” The AI automatically tailors follow-up questions to clarify, probe “why,” and explore use cases—mirroring the flow of a real conversation.
Survey creators aren’t stuck with one-size-fits-all follow-ups, either. With tools like Specific’s automatic AI follow-up questions feature, you control the depth and type of probing for each part of the survey—for instance, always asking "why," clarifying emotion, or skipping certain sensitive topics. This means surveys feel natural for respondents without losing structure or research rigor, and you get rich data across hundreds (or thousands) of conversations at once.
AI survey prompts for different user research scenarios
Writing effective user research surveys is much easier with modern AI survey builders. Thoughtful, conversational prompts become whole surveys you can launch in minutes. Here are example prompts for three essential scenarios, showing how to frame your intent for powerful results. You can tailor these for your audience or sector, and use editing tools to customize them on the fly.
Product-market fit research: If you need to learn which users get the most value and why:
Create a conversational survey to uncover why our most loyal users rely on [product/service], what challenges it solves for them, and which features they can’t live without. Ask follow-up questions to clarify any vague responses.
Churn analysis: Want to understand what’s pushing users away?
Generate a survey that explores the main reasons users consider leaving our product, what could have made them stay, and what alternative solutions they are evaluating.
Feature request collection: Testing reactions to a new idea?
Draft a conversational survey to validate interest in a proposed [feature], gather suggestions for improvement, and identify any concerns users might have about adoption. Include probing follow-up questions for specifics.
With a platform like Specific’s AI survey editor, you don’t just create the survey—you update and tweak the questions, prompts, and follow-ups by chatting with the system in natural language. The result: every survey is consistent, conversational, and adapts on the fly as people respond.
Analyzing user research data: From manual coding to AI insights
Traditionally, analyzing interview notes means hours of manual coding—labeling responses, identifying patterns, and summarizing feedback by hand. Surveys make it easier to quantify trends, but too often miss the nuance of why users feel the way they do.
AI survey response analysis changes the game. Instead of sifting through open-text one answer at a time, the AI summarizes responses, extracts core themes, and even lets you “chat” with your data—like having a digital research analyst on call. You can ask targeted questions such as, “What are the top three reasons users cite for churn?” or “Which features are most frequently mentioned as reasons to stay?” and get instant, actionable answers.
I’ve found that with tools like Specific’s AI survey analysis, you can:
Get automated themes and summaries after every response set
Filter results by user type or response patterns
Ask natural language questions like:
“How do new users feel about onboarding?”
“Summarize the main objections to our pricing model.”
“List common feature requests by user segment.”
This saves hours and regularly surfaces insights that would be easy to overlook. AI isn’t just saving time—it’s making research teams sharper, surfacing nuanced patterns and priorities while handling the heavy analysis grind. [1][2]
Transform your user research with conversational AI surveys
The best questions for user research use a mix of structure and flexibility—just like the best interviews. With conversational AI surveys, you no longer need to settle for either/or: depth or scale. You get both.
Teams can finally run scalable, high-quality research without wrestling calendars or conducting hundreds of redundant interviews. Whether you need a standalone landing page survey for rapid respondent reach, or want to ask contextually relevant questions right inside your SaaS app, Specific has you covered:
Conversational Survey Pages—perfect for sending one-off research surveys via email, social, or direct links
In-Product Conversational Surveys—embed research directly where your users live for crystal-clear, in-context answers
Experiment with the best of both interviews and surveys. Create your own survey—and see how conversational AI transforms the way you get insights from your users.