Customer data analysis drives smart feature prioritization, but asking the best questions for feature prioritization is what separates actionable feedback from noise.
With AI-powered surveys, we break past the limits of basic forms—digging deeper through real-time follow-ups and natural conversations that traditional approaches just can’t match.
In this guide, I’m sharing my go-to opportunity scoring prompts, smart “why/how-use” questions, and the analysis filters needed to turn power user insights into real product impact.
Opportunity scoring questions that reveal feature value
Opportunity scoring pinpoints which features customers crave and where they see the most value. In feature prioritization, this method shines a light on the true “opportunities”—those unmet or weakly addressed needs your competition has overlooked or your product hasn’t solved yet.
Leveraging an AI survey generator makes it easy to spin up targeted, conversational surveys that frame questions to reveal value gaps and pain points.
Impact assessment: These questions go beyond general sentiment to pinpoint the specific impact and desirability of a potential feature.
How would your workflow change if this feature was available? What specific outcomes would you expect?
Frequency of need: To determine the potential reach, ask about how often users actually need the proposed functionality.
How often do you encounter situations where this feature would be useful?
Current workarounds: The best opportunity scoring questions highlight areas where users cobble together imperfect solutions—your signal for high-priority fixes.
What are you doing today instead, if this feature is missing?
The difference between static scoring and conversational opportunity scoring is huge. According to recent industry data, customer analytics tools can increase customer lifetime value by up to 95%—but only if you’re capturing real context, not just surface-level ratings. [1]
Traditional scoring | Conversational opportunity scoring |
|---|---|
Static 1-10 “importance” ranking | Context-rich answers, rooted in real-life use cases |
Ignores workarounds or hidden pain | Uncovers workarounds, unmet needs, and emotional triggers |
Few actionable insights | Direct clues about what to build (or retire) |
Why and how-use questions for deeper feature insights
Understanding the why behind feature requests is gold—you discover not just what users say they want, but what they’re really trying to achieve.
Specific sparkles here, giving you best-in-class conversational survey experiences that feel effortless for both creators and customers, with thoughtful follow-ups that turn scattered replies into a story.
Our automatic AI follow-up feature can turn even simple prompts into multi-layered feedback sessions.
Jobs-to-be-done questions: Get behind the feature ask by focusing on the underlying job or progress the customer seeks.
When you requested this feature, what were you ultimately hoping to accomplish?
Workflow integration questions: Map out where this feature fits in the user’s day-to-day routine.
Can you walk me through how you’d use this feature in your current workflow?
Expected outcome questions: Target the results customers would celebrate if this feature shipped.
If this feature were released, how would you know it was successful for you?
Conversational follow-ups transform the survey into a dynamic exchange, not just a box-ticking exercise—ensuring every survey is a true conversation.
Analysis filters to segment power user feedback
Power users are your bellwether—they stretch your app’s limits and dream up the most valuable improvements. Getting their feedback right is critical for confident, strategic feature prioritization.
If you’re not using AI-powered response analysis on power user segments, you’re missing out on clear signals that drive outsized growth opportunities. Remember, companies using customer analytics are 23 times more likely to acquire customers—and that’s no coincidence. [2]
Usage frequency filters: Segment responses by how often users interact with your key features. Frequent users often spot friction or unmet needs sooner.
Feature depth filters: Some users stick to basics, but power users adopt edge cases or advanced options. Filter for those consistently using the full breadth of your product.
Account value filters: Prioritize feedback from your highest-value customers—the ones most likely to drive future revenue, retention, or brand advocacy.
Good practice | Bad practice |
|---|---|
Analyze power users separately using advanced filters | Mash all feedback together regardless of account type or usage |
Use AI to chat with filtered data (“What are the top frustrations among our daily users?”) | Scan exported spreadsheet manually, hoping to spot patterns |
Try these analysis prompts with your power user responses in Specific:
What do our top 5% usage customers say we should improve or add?
Summarize the key feature requests from our highest MRR accounts.
What jobs do expert users try to accomplish that basic users ignore?
This approach not only surfaces actionable ideas but tightens your focus on features that move the needle for loyal, high-value users.
There’s good evidence for this discipline: companies using customer analytics are 50% more likely to retain and upsell existing customers, making smart segmentation a must. [3]
Building your complete feature prioritization survey
To get the deepest possible insights, combine these question types into one survey—sequenced for maximum clarity and actionable feedback. With the AI survey editor, changing your survey is as easy as chatting, and Specific’s AI helps structure it from a starter prompt.
Question sequencing: Always lead with a broad opportunity scoring question, then drill down with why/how-use prompts, and wrap with demographic or usage filter questions.
Response branching: Use branching logic so that your AI survey naturally asks deeper follow-ups for ambiguous or high-signal answers, instead of over-questioning everyone.
Keep the survey focused and concise. Three core question types, plus a handful of targeted follow-ups, usually results in higher completion rates and richer responses; don’t overload users with more than 7-10 main questions per session. Prioritize engagement by keeping the tone conversational and assuring users their expertise shapes your roadmap directly.
Here’s how a complete structure might look:
1. How often do you wish [Feature] was available in your workflow?
2. Can you describe a recent time you needed this feature?
3. What did you try instead when the feature wasn’t available?
4. What would success look like if we built this feature?
5. How do you use our product—daily, weekly, or occasionally?
6. Which other advanced features do you regularly use?
7. Your role and company size? (for deeper segmentation)
8. Final open invite: “Anything else you wish we’d consider for our next update?”
This is the sweet spot: every question earns its keep, and Specific keeps the flow lively, context-aware, and incredibly actionable.
Start collecting feature insights today
Transform your feature prioritization with AI-powered, conversational surveys—cut through guesswork and get the user insights that actually drive your product forward. Create your own survey and turn every customer response into a roadmap advantage.