Here are some of the best questions for a user survey about feature usefulness, plus tips on crafting an effective survey. If you want to build your own, Specific lets you generate a survey about feature usefulness in seconds with AI—no need to start from scratch.
Best open-ended questions for user survey about feature usefulness
Open-ended questions dig into real user experiences and motivations. They’re helpful for discovering nuanced needs, frustrations, or creative use cases that close-ended questions often miss. However, keep in mind that open-ends can come with higher nonresponse rates—one Pew Research Center study found some open questions saw nonresponse rates over 50% while the average was 18% [1]. For best results, limit how many you ask and use them where you need richer insights.
What do you find most valuable about this feature?
Can you describe how you most often use this feature?
Is there anything you find confusing or difficult about using this feature?
Can you share a recent situation where this feature helped you?
If you could change one thing about this feature, what would it be and why?
What do you wish this feature did that it currently does not?
Have you ever stopped using this feature? If so, what caused that decision?
In your own words, how does this feature affect your overall experience with our product?
How would you explain the benefits of this feature to a new user?
Are there any alternatives you use instead of this feature? If so, why?
For tips on creating your own user survey about feature usefulness, check out our how-to guide.
Best single-select multiple-choice questions for user survey about feature usefulness
Single-select multiple-choice questions are ideal when you need structured, quantifiable data or want to quickly identify patterns among users. They’re a great starting point—respondents can answer easily, which keeps completion rates high (closed-ended questions have response rates between 98-99% compared to just 82% for open questions [1][2]). Then you can use follow-up questions to dig deeper into interesting responses. Here are a few effective examples:
Question: How often do you use this feature?
Every day
A few times a week
Once a week or less
Rarely/Never
Question: Overall, how useful do you find this feature?
Extremely useful
Somewhat useful
Neutral
Not very useful
Not at all useful
Question: What’s the main reason you use this feature?
It saves me time
It simplifies a process
It helps me achieve a goal
Other
When to follow up with "why?" Asking “why?” as a follow-up helps you move beyond surface choices. Whenever a user selects an option that could mean different things for different people—like “Rarely” or “Other”—following up with “Why did you choose that?” yields actionable insights. For instance, if someone says a feature is “not very useful,” probe into what’s missing or frustrating for them.
When and why to add the "Other" choice? Always add “Other” if not every scenario fits your options. The follow-up question (“Please explain what you meant by ‘Other’”) often surfaces pain points, missed use cases, or competitor mentions that are otherwise invisible.
Should you include an NPS-style question in a user survey about feature usefulness?
The Net Promoter Score (NPS) is usually used to measure overall loyalty, but a focused version works well for assessing specific feature usefulness. It asks users how likely they are to recommend this particular feature to a friend or colleague. This simple scale—from 0 to 10—gives you a quick quantifiable readout, and combining it with targeted follow-ups reveals underlying motivations. If you want to try this, Specific makes it easy to build an NPS survey for feature usefulness—complete with tailored probe questions for detractors and promoters.
The power of follow-up questions
Follow-up questions unlock the “why” and “how” behind quick answers. That’s why we built the automatic AI follow-up questions feature into Specific—it analyzes each user’s answer in real time and asks probing questions, just like an expert interviewer.
Automated follow-ups are a game-changer: no need for slow, back-and-forth email exchanges; no wrangling ambiguous responses afterward. The conversation stays on track, is context-aware, and drives rich, actionable insight. Here’s what happens if you don’t use follow-ups:
User: I rarely use this feature.
AI follow-up: Can you tell me what’s holding you back from using it more often?
How many followups to ask? In most cases, 2-3 follow-up questions per user are plenty to get the context you need. Specific lets you tune this—set a maximum, or move to the next question as soon as you’ve got enough detail.
This makes it a conversational survey: Every interaction feels like a two-way chat, not a static form. Respondents naturally open up, leading to more honest and complete answers.
AI analysis, summarization, and themes: Even with lots of open-ended replies, analyzing everything is easy: simply use AI survey response analysis—summarize, search themes, or chat with your data; no manual spreadsheet work required.
Automated probing is a new approach—try generating a feature usefulness survey and see how natural the conversation becomes.
How to prompt ChatGPT (or any GPT) to generate great questions for user feature usefulness surveys
If you want to create a user survey from scratch, a powerful approach is to prompt an AI like ChatGPT for help. Start broad, then give context. For initial brainstorming, try:
Suggest 10 open-ended questions for User survey about Feature Usefulness.
But remember, context matters. Share details about your product, your user base, and goals. For example:
We have a B2B SaaS tool used by small business owners for financial reporting. We want to understand how useful the new "Dashboard Export" feature is. Can you suggest a mix of open and closed questions to gauge usefulness, ease-of-use, and unmet needs? Keep language friendly and professional.
After generating a list, help structure them:
Look at the questions and categorize them. Output categories with the questions under them.
Then, dive deep into the most relevant topics:
Generate 10 questions for categories A, B, and C.
Tailoring your prompts at each step leads to expert-quality surveys fast—or just let Specific do it with its AI survey builder.
What is a conversational survey? AI-generated vs. manual surveys
A conversational survey is an interactive form that feels like a one-on-one chat. Instead of dumping a list of questions on a page, the survey adapts to answers, probes automatically, and maintains a natural “back and forth.” It’s the simplest, richest way to get actionable, honest feedback from your users—especially when exploring feature usefulness.
Manual Surveys | AI-Generated Conversational Surveys |
|---|---|
Static forms, no real-time adaptation | Adaptive, responsive, probes for detail automatically |
Limited to pre-written options | Digs deeper with custom follow-ups for each user |
Response quality often shallow | Extracts deeper context naturally |
Manual analysis and follow-ups needed | AI summarizes and analyzes instantly |
Time-consuming to create and edit | Survey is built by chatting—it’s fast and painless |
Why use AI for user surveys? Because it handles the busywork of survey creation, adapts like a pro interviewer, and analyzes all replies—freeing you to focus on what matters: making the right product decisions. The difference in user engagement and data depth is night and day. For an AI survey example or to use the best AI survey builder, see how Specific can change your approach—and your results.
Specific offers the best-in-class conversational survey user experience, making both your data collection and your respondents’ journey effortless. Check out our step-by-step guide for creating a user survey about feature usefulness if you want to learn more about setup and best practices.
See this feature usefulness survey example now
This is your chance to unlock user insights that help you build better features, faster—by asking the right questions and capturing feedback in a way that’s seamless and smart.