When it comes to product feature validation, the best questions for feature validation go far beyond “Would you use this?” To really understand what works, we need to strategically test desirability, usability, and value—and that's where AI-powered follow-ups surface the insights we’d otherwise miss. AI-generated probing digs into users' true motivations, not just surface reactions.
The three pillars of feature validation
Every successful feature checks three boxes: desirability (do users want it?), usability (can they use it?), and value (will it make a real impact?).
Desirability: Is this something users genuinely care about? Miss this, and you’re left with features that gather dust.
Usability: Even if a feature is wanted, it’ll flop if users can’t figure it out or if it doesn’t fit their routine.
Value: What’s it worth—will it save time, money, or deliver real ROI? If not, usage will be a short-lived spike at best.
Making sure your questions tap all three avoids the classic product pitfall: obsessively testing usability in a vacuum, while ignoring if anyone would even want the feature in the first place. The data backs this up—69% of companies fail to validate core assumptions, leading to a 15–20% dip in product success. [1] Strong validation practices directly increase launch success, cut wasted spend by 67%, and deliver market fit more than four times faster.[2][3]
Desirability questions that reveal true user interest
The desirability test cuts through politeness and surface-level “sure, sounds nice” answers. We want to know: will users change their workflow, are they already finding workarounds, and what problem would this really solve?
“How would this feature change your current workflow?” — Reveals existing pain points and how disruptive or helpful the feature could actually be.
“What problem would this solve for you?” — Surfaces if this is a genuine need, or simply a ‘nice-to-have’.
“Have you ever tried to do this another way?” — Highlights any existing workarounds (which means the need is strong, but current solutions are lacking).
“What would you use if this wasn’t available?” — Gets at substitutes or competitors already filling this gap.
To dig deeper, AI-powered follow-ups should always probe for origin stories, emotional reactions, and specifics. For example, after a user answers, the AI might automatically ask “Why is that important to you?” or “Tell me more about why that matters in your role.” You can configure this custom probing in the AI survey editor, giving the AI the right nudge to keep digging.
For each open-ended response, ask at least one 'why' follow-up. If a user mentions a pain point, prompt: “Can you walk me through a recent time when you experienced this?” Prioritize depth over breadth.
With Specific’s survey builder, it’s practically effortless to fine-tune your AI follow-ups: just describe what context you want—like real-world examples or emotional drivers—and the AI will handle it naturally.
Testing usability before you build
Usability is where most teams default to: can someone actually use this? But superficial tests only tell half the story. The best usability questions gently challenge mental models and uncover where users trip up, so you can fix UX snags pre-launch.
“What would you expect to happen when you click [this]?” — Reveals if the feature’s behavior matches intuition.
“Where would you look for this feature?” — Shows if placement and iconography are discoverable.
“How would you try to accomplish [the task] today?” — Uncovers current habits and process gaps.
“If you ran into a problem here, what would you do next?” — Identifies natural fallback steps or whether your flow breaks down.
Good practice | Bad practice |
|---|---|
Ask open-ended “What did you expect?” | Ask “Did this work as you expected?” (yes/no) |
Probe for reasons behind confusion | Accept vague “It’s fine” replies |
Encourage real-time walk-throughs | Only discuss ideas in theory |
AI follow-ups here are invaluable. If a user is confused, the AI can immediately ask “Can you describe what you thought would happen?”—clarifying how people truly interpret your design, so you’re not just hoping they’ll figure it out.
My experience shows the most actionable usability insights come from in-product surveys, delivered when users are already in context. Users demonstrate what works in real time, and AI can instantly clarify as the moment unfolds. See how in-product surveys boost contextual feedback.
Measuring perceived value and willingness to pay
Value is make-or-break: will users pay for this, upgrade their plan, or dramatically change engagement? Even free features need to drive quantifiable impact, not just check a box.
“How much time would this save you in a typical week?” — Translates impact into hours (and dollars).
“Would you be willing to pay extra for this capability?” — Direct test of willingness to pay, critical for SaaS features.
“If you lost this feature tomorrow, how would it affect your work?” — Reveals stickiness and the real cost to the user.
“On a scale of 1–10, how valuable does this feel compared to your current tools?” — Puts your offer in perspective.
AI-powered follow-ups go deep by asking users to put real numbers or scenarios behind their claims. If someone claims a big time savings, the AI can automatically ask “How did you come up with that estimate?” or “What would you do with the time saved?”—which often leads to more honest, grounded answers.
Follow up on any estimated savings or value statements with: “Can you give a concrete example from last week where this would have helped you? If possible, put a number on the time or money saved.”
These real-world specifics are what guide effective prioritization and pricing—not generic “would you pay” guesses. For prospect feedback (people not yet using your product), landing page surveys are ideal for quantifying perceived value and market appetite. See how landing page surveys validate new concepts with fresh audiences.
Choosing between in-product and landing page surveys
Survey Type | Best For | Strengths | When to Use |
|---|---|---|---|
In-product surveys | Current users | Contextual feedback, high accuracy, usability testing | Testing features with existing customers in real use cases |
Landing page surveys | Prospects and broader market | Large audience reach, market sizing, competitor checks | Validating new feature ideas or pricing before full build |
In-product surveys dive into authentic user behavior in the wild—ideal for usability and desirability. Landing page surveys capture early signals from prospects, perfect for value and market fit analysis. Both support rich AI-driven analysis and custom probing for whatever you need to learn next. Discover how AI analysis works across all your surveys.
Turning validation responses into product decisions
Collecting data is just step one—the magic comes from analysis. With AI chat-based survey analysis, you can surface powerful patterns and untapped insights in minutes. Here’s how I use it for actionable answers:
For uncovering UI sticking points, I ask:
What are the top concerns users have mentioned about usability of the new feature? Summarize recurring pain points and suggest UI tweaks.
To identify the most promising audiences, I try:
Which user segments express the highest level of interest or willingness to pay for this feature? Provide any common characteristics.
And to tackle adoption blocks, I’ll prompt:
Summarize the key reasons users say they wouldn’t use this feature, and any situational barriers that come up repeatedly.
AI lets you instantly spin up multiple analysis threads—so product, marketing, and growth teams can each dig into their own questions, without tripping over each other. Summaries are clear, shareable, and make reporting back to stakeholders a breeze. Compared to spreadsheet wrangling, conversational analysis delivers the nuanced insights that move the needle for your roadmap.
Start validating with confidence
Effective feature validation blends savvy questions with AI-powered follow-ups to uncover what users truly want and need. Teams who close this loop build products people actually adopt. Ready to put your next feature to the test? Create your own survey—and let the insights lead your roadmap.