Finding what is the best user feedback tool starts with asking the right questions—and knowing when to ask them.
This guide covers the best questions for user feedback collection, offering 15 example prompts engineered for AI-powered surveys that learn from every response.
We’ll dive into when to use each type of question, and how smart targeting can make your feedback dramatically more valuable.
15 AI-powered user feedback prompts for every scenario
Great questions uncover not just what users think, but why. I’ve organized these 15 sample prompts—each ideal for conversational AI surveys—by common feedback goals. These work even better with automatic AI follow-up questions that adapt in real time.
Feature discovery & validation
What’s the most useful feature you’ve found in our product so far?
When to use: After a major product update or on feature launch.
AI follow-up intent: AI probes for specific scenarios and details about workflows or outcomes.
Are there features you expected but couldn’t find?
When to use: For uncovering unmet needs or usability gaps.
AI follow-up: AI asks what tasks went unaccomplished or compares to competitor expectations.
How easy was it to use [Feature X]? What would make it easier?
When to use: After users try a new or complex feature.
AI follow-up: AI explores step-by-step friction and suggestions.
If you could change one thing about [Feature Y], what would it be?
When to use: For optimizing established features.
AI follow-up: AI clarifies the root of frustrations or requests for similar workflows.
Churn & retention insights
How likely are you to continue using our product in the next 6 months?
When to use: For ongoing retention checks.
AI follow-up: AI asks what influences the decision or what could increase loyalty.
What nearly made you stop using the product?
When to use: When users’ activity drops or at cancellation.
AI follow-up: AI uncovers alternative solutions and what tipped the scale.
What’s the number one thing we could improve to keep you as a user?
When to use: For at-risk or churning users.
AI follow-up: AI digs into specific pain points and missed expectations.
Have you considered alternatives? Why or why not?
When to use: Useful for competitive analysis.
AI follow-up: AI gathers names of alternatives and compares perceived pros/cons.
Onboarding & first impressions
What was your first impression after signing up?
When to use: Immediately post-onboarding.
AI follow-up: AI identifies confusing steps or unmet expectations.
Was anything unclear or unexpected during your first use?
When to use: After onboarding or first session.
AI follow-up: AI digs into specific points of confusion and reasons for them.
Value & ROI understanding
How has our product helped you save time or achieve your goals?
When to use: After 1-4 weeks of regular use.
AI follow-up: AI quantifies benefits and collects concrete before/after examples.
What’s the most measurable impact you’ve experienced since using us?
When to use: Post-launch, quarterly business review, or renewal points.
AI follow-up: AI explores metrics and how users track impact.
General satisfaction & NPS
How satisfied are you overall with our product?
When to use: Regular check-ins or trigger-based feedback.
AI follow-up: AI asks “why” for low scores; explores highlights for high scores.
On a scale of 0-10, how likely are you to recommend us to a friend?
When to use: Standard NPS touchpoints.
AI follow-up: AI personalizes follow-up: celebrates or requests suggestions.
What’s one thing you wish we’d asked you about, but didn’t?
When to use: End-of-session or after key flows.
AI follow-up: AI seeks unaddressed topics or novel suggestions.
Using targeted prompts like these, especially with AI follow-up questions, captures more context than static checkboxes ever could. In-app surveys see response rates up to 30%—outperforming traditional email by a wide margin [1].
Open-ended vs multiple-choice: choosing the right format
The format you choose matters as much as the question itself. Here’s a quick comparison:
Open-ended | Multiple-choice |
|---|---|
Best for discovering unknowns, motivations, and pain points | Best for benchmarking, trends, and quantifiable answers |
Collects richer, nuanced stories—but may lower completion rate by 41% [3] | Structured for quick analysis—higher response rates |
AI-driven follow-ups reveal hidden insights | Easy for users, with options to probe via follow-up |
Hybrid questions—multiple-choice with AI follow-ups—combine the best of both worlds. You get organized data plus deep context when users pick “other” or give surprising answers. Switch between formats instantly with the AI survey editor—just describe the change, and it’s done.
Follow-up questions make surveys conversations, not interrogations—a real conversational survey experience.
Smart targeting: asking the right users at the right time
Behavioral triggers let you prompt feedback after key actions—like using a new feature, making a purchase, or finishing onboarding. It’s how you capture motivation or friction while it’s still fresh, not remembered later.
User segmentation means tailoring questions for power users, new users, or free-vs-paid tiers. Relevant questions boost response rate and data quality—what excites a veteran is not what confuses a newcomer.
Frequency controls control how often someone gets surveyed—setting recontact periods and maximum survey exposures. With response rates declining by 30% industry-wide due to survey fatigue [4], this helps you collect honest feedback without burning out your users.
For advanced targeting, in-product delivery like in-product conversational surveys ensures surveys reach the right audience, at the right moment. Better targeting leads to noticeably higher completion and more thoughtful answers [2].
From feedback to insights with AI analysis
Collecting answers is only the start—turning them into clarity is what actually matters. With AI survey response analysis, it’s like plugging in a research analyst whenever you need.
Want to spot patterns across dozens (or thousands) of user answers? Try:
What themes appear most frequently in users’ explanations for low satisfaction scores?
Need to break down feedback by audience segment?
Can you analyze onboarding pain points specifically for users who signed up in the last 30 days?
Looking for emerging product ideas?
List the most common feature requests mentioned by users in the last month.
Or maybe you’re after retention answers:
Summarize the top reasons for cancellation based on recent feedback.
With parallel AI analysis threads, product, support, and growth teams can each explore the same dataset from their own angle—without waiting for data scientists. AI instantly summarizes and distills thousands of lines of feedback into clear, actionable themes.
Making user feedback a competitive advantage
If you’re not running these conversational surveys, you’re missing out—not only on richer insights, but also on catching churn, surfacing hidden pain points, and building user loyalty where it matters.
Set up one well-timed, targeted question after activation or core feature use.
Replace part of your user interviews with AI-powered follow-ups for continuous discovery.
Automate monthly NPS check-ins, segmented by account or user type for more actionable insights.
Create dedicated AI analysis threads for product, support, and growth teams to cut through the noise quickly.
With Specific, you deliver best-in-class conversational surveys that make giving—and analyzing—feedback easy and engaging.
Move fast: create your own survey with the AI survey generator and start learning from real user conversations in minutes.