u/Effective_Usual_895

Been using AI-conducted interviews for customer research and it raised an interesting problem I haven't seen discussed much. Each conversation Frank AI Researcher has with a user starts completely fresh. No memory of what that user said last time, no continuity across sessions. For a single interview that's fine. But if you want to do longitudinal research following up with the same users weeks later, tracking how their opinions change, building on previous answers — the stateless nature of current AI systems becomes a real limitation. A human researcher remembers. They build rapport across sessions. They catch contradictions. They know what to push on because they remember what the person said three weeks ago. AI interviews right now are good at breadth across many users in a single session. They fall apart for depth across time with the same user. Curious how people working on AI memory systems think about this use case. Is persistent user memory across sessions something being actively worked on, or is the focus more on within-session context management?

Been experimenting with AI-led qualitative research as a product person with no formal research background. Wanted to share what I learned because it changed how I think about AI as a tool beyond writing and coding. The problem I was trying to solve: I needed to talk to churned users but couldn't get them to respond to surveys or book calls. Classic response bias the people who engage are never the ones who left. I used Frank AI Researcher to run async interviews with 40 churned users. No scheduling, they respond when they want. 68% completed it. The completion rate gap made me think about why async AI works where synchronous methods fail. My theory: there's no social pressure, no founder in the room, no feeling of being judged. Disengaged users will apparently talk, just not to you directly.

The synthesis flagged a theme across 11 independent conversations I'd had data hints about for months but couldn't interpret clearly.

The methodological questions I'm still sitting with: How much does the AI interviewer shape responses through question framing?, How do you validate AI-synthesized themes without re-reading every transcript?, Where does this method break down compared to human interviews?

If anyone here has gone deeper on AI for qualitative research academically or practically, genuinely curious what you've found.

We spent most of last year shipping features based on what two customers told us once in an onboarding call. That's not a strategy, it's just what happens when you're small, overextended, and nobody on the team has any research background.

Eventually we had a feature in the roadmap that would take 6 weeks to build and we genuinely couldn't agree on whether customers actually wanted it or whether we just thought they did. That was the forcing function.

Here's what we tried:

Cold DMs to existing customers asking for 20 minutes, about 10% responded, we did 6 calls over two months. Actually useful conversations, but we kept reaching the same type of customer: the engaged ones. The people who left never responded.

Post-purchase surveys, 3-4% response rate, answers were either glowing or vague. Nothing that explained why people weren't converting or why they churned.

Calendly links in our offboarding flow to catch churned users right as they left, three bookings in three months. Two rescheduled twice and never showed.

ended up trying Frank AI Researcher, sent it to 40 churned users, 68% actually completed it which honestly surprised me. one thing that kept coming up, like 11 out of 40 conversations, people didnt leave because of the product. onboarding never connected what we built to what they were actually trying to do. specific enough that we could fix it

still not fully sold on ai interviews as a concept but the data was specific enough that we're continuing with it. curious if anyone else has tried something similar

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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.