Why does this matter?
For one, it makes the whole discovery process faster. You quickly learn what motivates your customers—what they like, what frustrates them—without wasting days on manual analysis. Teams avoid endless synthesis and instead base decisions on real evidence. And when everyone uses the same set of insights, alignment across the team gets a lot better.
Here’s how it works:Â
the AI groups feedback into clusters by topic, analyzes sentiment, looks for root causes, and then creates short, focused insights. Things like:
- “Users can’t find the filter panel easily.”
- “Teams want more export options to share findings.”
- “Mobile performance issues disrupt user tasks.”
For a few real-world examples—if people keep reporting onboarding issues, you might see: “Onboarding steps are confusing.” If search is a problem: “Users request advanced filtering.” When uploads fail: “Unstable file handling.”
Where can you use this?
Anywhere you want to keep learning from users. It’s great for ongoing discovery, identifying opportunities, setting priorities, improving user experience, and monitoring churn risk.
If you’re using Usersnap (with Airis), the platform brings all your feedback into clusters, analyzes it, and delivers insights you can actually act on—whether you’re planning your roadmap or just deciding what to fix next.
.svg)
