u/Effective_Usual_895

[supprimé]

Stumbled across Frank AI researcher and one thing stood out immediately. It interviews your customers at scale and surfaces which features they genuinely value, not what you think they value, not what they politely mention on a call, but what they actually bring up unprompted when nobody's watching.

That gap is huge. I've seen teams spend months building features based on sales calls and support tickets, only to find out later that the thing customers loved most was something completely different. Something they never thought to ask about.

The reason this works is kind of fascinating, people are more honest with AI than with humans. No awkwardness, no social pressure, no trying to be helpful to the person interviewing them. They just say what they actually think. For product teams trying to figure out what to double down on vs what to quietly kill, this feels like it could save months of guesswork.

Has anyone used it specifically for feature prioritization? Would love to know what came out of it.

We're a team of three. No research budget, no agency, no dedicated researcher. For a long time that meant flying completely blind while bigger competitors seemed to move with actual intelligence behind their decisions.

Here is what changed:

We stopped treating research as a project and made it a background habit. One rotating post purchase survey question every few weeks. A quick Friday scan of support tickets not to fix things but just to notice what language kept repeating. Simple and free.

The analysis side is where we kept breaking down. We'd collect decent feedback and then either ignore it or confirm what we already believed. We stumbled across a few smaller tools that helped with that specific problem. One organizes qualitative responses, another tags patterns across feedback, and one we recently found called Frank AI researcher actually pushes back on your own interpretations which was the thing we needed most.

The other thing that changed everything costs nothing. Competitor reviews on marketplaces and forums. Specifically the angry ones. Frustrated customers describe exactly what they wish existed and almost nobody is paying attention to that signal.

What are other small teams doing for research? Especially curious about the analysis side because that's where most people seem to give up.

We're a team of three. No research budget, no agency, no dedicated researcher. For a long time that meant flying completely blind while bigger competitors seemed to move with actual intelligence behind their decisions.

Here is what changed:

We stopped treating research as a project and made it a background habit. One rotating post purchase survey question every few weeks. A quick Friday scan of support tickets not to fix things but just to notice what language kept repeating. Simple and free.

The analysis side is where we kept breaking down. We'd collect decent feedback and then either ignore it or confirm what we already believed. We stumbled across a few smaller tools that helped with that specific problem. One organizes qualitative responses, another tags patterns across feedback, and one we recently found called Frank AI researcher actually pushes back on your own interpretations which was the thing we needed most.

The other thing that changed everything costs nothing. Competitor reviews on marketplaces and forums. Specifically the angry ones. Frustrated customers describe exactly what they wish existed and almost nobody is paying attention to that signal.

What are other small teams doing for research? Especially curious about the analysis side because that's where most people seem to give up.

We're a team of three. No research budget, no agency, no dedicated researcher. For a long time that felt like we were just flying blind while bigger players had actual systems behind their decisions.

Here's what we built instead:

We stopped treating research as a one time project and made it a background habit. One rotating post purchase survey question every few weeks. A quick Friday scan of support tickets not to fix things but just to notice what words keep coming up. The consistency is what makes it useful not the sophistication. For the analysis side we tried a few smaller AI tools. Typewise for organizing qualitative responses, Notably for tagging patterns across feedback and Frank AI researcher for real customer behavior pattern. That part matters because most of us read our own data looking for confirmation not contradiction.

The other free thing that changed everything, going through competitor reviews on marketplaces and forums. Specifically the frustrated ones. People describe exactly what they wish existed and nobody is paying attention.

What are other small teams doing for research? Curious especially about how people handle the analysis side because that's where we kept giving up before we found a real system.