Here are some of the best questions for a user survey about feature requests, along with tips to create an effective survey. You can use Specific to instantly build a conversational survey tailored to your product and users.
Best open-ended questions for user survey about feature requests
Open-ended questions let users share ideas in their own words, often revealing new feature needs or pain points we might miss with rigid checkboxes. The main benefit is that they encourage deep, thoughtful responses, surfacing not only what but also why users crave certain improvements. However, these questions tend to have a higher nonresponse rate—**open-ended items average an 18% nonresponse, with some topping 50%**. That’s why we recommend limiting the number of open prompts and placing them strategically, especially later in your survey for higher completion rates. [1][2]
What is one feature you wish our product had?
Can you describe a recent situation where you wanted to do something in our product, but couldn’t?
Which current feature do you find most limiting, and how would you improve it?
Tell us about a product you use that has a feature you wish we had. Why is it valuable to you?
Are there any repetitive tasks you would like to automate in our tool?
What’s the single biggest improvement that would make your workflow smoother?
If you could wave a magic wand and add anything, what would it be—and why?
How would [Product Name] need to change for you to recommend it more enthusiastically?
What’s the smallest tweak that would make a big difference in your daily use?
Have you needed to use workarounds or external tools? If yes, which ones and for what?
Best single-select multiple-choice questions for user survey about feature requests
Single-select multiple-choice questions are perfect for quantifying user priorities or breaking the ice when open-ended queries feel overwhelming. These questions consistently yield high response rates (as low as 1–2% nonresponse) and help us spot trends fast. It's usually a good idea to start with a closed option and then dive deeper with follow-ups. [1]
Question: What area of the product do you most want us to improve?
Performance and speed
User interface and design
Integrations with other tools
Reporting and analytics
Other
Question: How important is it for us to add new features in the next 6 months?
Extremely important
Somewhat important
Not very important
Not at all important
Question: Which types of features would you use most often if we added them?
Collaboration tools (comments, sharing)
Automation or workflows
Customization/options
Third-party integrations
Other
When to follow up with "why?" When a user picks a choice like “Integrations with other tools,” following up with “Why is this most important for you?” opens the door for richer context and specific use cases. We suggest this after any answer that reveals priorities—often, the why is more actionable than the what.
When and why to add the "Other" choice? Include "Other" when your options can't cover the full range of needs. Adding a follow-up text box here lets users reveal pain points or feature ideas you hadn’t considered—sometimes your biggest insights will come from those unexpected write-ins.
NPS-type question for user survey about feature requests
The Net Promoter Score (NPS) asks users how likely they are to recommend your product to a friend or colleague, using a 0–10 scale. For feature request surveys, NPS is useful because it gives essential context—when users are not promoters, the reason is often tied to missing or weak features. A smart survey will discover not just that a user is a detractor, but specifically which features would change their mind. Try a dedicated NPS survey for user feature requests to get the full picture.
The power of follow-up questions
If you’ve ever read user feedback that left you guessing what someone really meant, you’re not alone. That’s where automated follow-up questions shine. Instead of static forms, Specific uses AI to probe answers in real time, clarifying answers, digging deeper, and capturing fuller context—as if a skilled human was conducting the interview. This approach saves massive time (no more email threads to clarify what “improve workflow” means) and makes every response actionable.
User: “I just want more integrations.”
AI follow-up: “Which integrations would make the biggest impact for you, and how would you use them in your workflow?”
How many followups to ask? Two to three follow-ups is usually enough—just enough to clarify the main idea or surface a motivating example, but not so many that it feels like an interrogation. With Specific, you can set this automatically, or let the AI skip ahead once it senses you’ve captured the required information.
This makes it a conversational survey: By stringing together questions this way, your survey starts to feel like a guided conversation, not a soulless form. Users open up, leading to more thoughtful and complete answers.
Easy AI analysis, even for unstructured text: Even with long, open responses, analyzing your results is simple with tools like AI survey response analysis. Specific lets you chat with your data, pulling out well-organized summaries, themes, and recommendations at scale.
Automated follow-ups are a brand-new standard—generate a survey with Specific and experience the difference for yourself.
How to prompt ChatGPT to generate survey questions about feature requests
Let’s say you want AI to draft questions. The best prompt to start with is:
Suggest 10 open-ended questions for user survey about feature requests.
But if you give the AI more context—like what your app does, your top goals, or the type of users you have—the results become sharper and more relevant.
Try this:
My app helps remote teams manage projects. Our users are mostly project managers. Suggest 10 open-ended questions to uncover which feature requests would most improve their collaboration and reporting workflows.
Once you have a batch of questions, organize them for clarity:
Look at the questions and categorize them. Output categories with the questions under them.
With those categories identified, identify key topics and go deeper:
Generate 10 questions for categories like "workflow automation" and "reporting enhancements".
This keeps your survey tight and focused, tailored for real insights.
What is a conversational survey?
Conversational surveys are AI-driven interviews that feel like natural chats—asking, clarifying, and responding just like a talented researcher or product manager would in person. Unlike static forms, conversational surveys respond, adapt, and probe for clarity in real time, so even "boring" questions get richer answers.
Here’s a quick snapshot of the difference:
Manual surveys | AI-generated (conversational) surveys |
|---|---|
Create questions one by one, edit and test manually | Write a short prompt—AI builds and personalizes questions instantly |
Fixed pathways: no follow-up unless pre-coded | AI follow-ups probe deeper based on each response |
Analysis of free text is slow/manual | AI organizes responses, finds themes, and suggests actions |
Often feels impersonal and tedious for users | Feels like a brief, relevant chat—boosting engagement |
Why use AI for user surveys? AI survey examples outperform traditional surveys when you care about speed, engagement, and the quality of your insights. Instead of laborious form building, AI survey generators (like Specific) craft effective, bias-minimized question sets in a fraction of the time, making high-quality feedback accessible for everyone.
Specific offers the smoothest user experience for conversational surveys, both for you and your users—it’s chatty, smart, and context-aware. If you’re new to conversational surveys, check out our practical guide on how to create a user survey about feature requests.
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