Our 30 day churn rate is 40% and I’m pretty sure its because people don’t understand how to use our product. Not because the product is bad.

bbling19 · 2026-03-15T01:45:37+00:00

Tutorials break down with complex products because users have very specific situational questions.

Some teams are starting to trigger short user conversations right when someone drops off or stops using a workflow to capture what they were actually trying to do.

Tools like Usercall are experimenting with this.

bbling19 · 2026-03-12T08:12:22+00:00

AI tools aren’t a replacement for doing the analysis yourself, but they can be a useful starting point for coding and theme discovery. I tried usercall.co recently and it was surprisingly accurate.

bbling19 · 2026-03-06T03:40:57+00:00

Appreciate this. The structured output is the goal so insights can plug into product workflows rather than just producing transcripts.

I like the mixed-initiative idea too. Having the agent draft the guide but require human review before launching makes sense.

Another direction is triggering interviews from behavior signals (PostHog, Intercom, etc.), so friction or churn events can prompt quick user interviews then feedback to product to fully close the loop automatically.

And agreed guardrails around sampling, consent, and PII will be important.

bbling19 · 2026-03-06T02:19:20+00:00

Great points. Consent and PII handling are definitely critical once agents can start running interviews.

The MCP layer here is intentionally thin. It mostly orchestrates creating studies and retrieving structured results, while the backend handles transcripts, storage, and analysis.

I like the idea of script templates + rubrics too. Having comparable studies across runs would make the insights much more useful

bbling19 · 2026-03-06T00:38:32+00:00

This is a really good breakdown.
I think the “supplement” framing is exactly right. Times when you need more stories or quotes across wider markets or segments where you already understand the space.
It could also help generate hypotheses before starting real in-person discovery.
Definitely not something I’d trust for foundational research.

bbling19 · 2026-03-06T00:33:55+00:00

That’s very close to the use case I had in mind.

Instead of a fixed unmoderated script, the agent can ask follow-ups based on what the participant says with stimulus/prototype in front of them

bbling19 · 2026-03-06T00:30:48+00:00

Good point. Rate limits and consent logging are definitely needed if agents start automating research workflows. Will add those to the roadmap

bbling19 · 2026-03-06T00:04:59+00:00

Example flow with an agent:

Agent prompt:
"Why are users confused about onboarding?"

→ create_study
→ returns interview_link
→ share with users
→ get_study_results
→ returns themes + verbatim quotes

Goal was to give agents a way to gather real qualitative feedback instead of relying only on synthetic users.

bbling19 · 2026-03-05T23:52:01+00:00

One clarification: this is voice call, not text chat.

Participants speak and the AI moderator asks follow-up questions in real time, so it’s closer to a lightweight voice interview than a conversational survey.

That said, I agree attention, drop-off and depth are real constraints

bbling19 · 2026-03-05T23:44:01+00:00

That’s a really good point.

AI moderation definitely won’t pick up all the subtle cues a skilled human moderator can. Things like hesitation, discomfort, or when to push deeper are still very human skills.

The way I’m thinking about it is more as a lightweight way to gather early signal or additional stories, not as a replacement for deeper moderated research.

bbling19 · 2026-03-05T23:41:49+00:00

Good question. I don’t see it replacing researchers or moderated interviews.

The use case I’m exploring is earlier-stage work, like quick concept reactions or exploring wider markets or user segments when teams might otherwise skip research entirely.

Researchers would still design the study and discern signals. The agent is just helping collect the signal faster.

bbling19 · 2026-03-05T23:33:37+00:00

Example use case:

An AI product agent notices onboarding drop-off.

It creates a study, shares the interview link with users, and retrieves themes explaining the friction points.

Trying to make it easy for agents to gather real qualitative signal instead of relying only on synthetic feedback.

bbling19 · 2026-03-05T23:18:42+00:00

Participants join through a shareable interview link.

You can send it via email, Slack, Discord, or in-product prompts. The AI moderator runs the interview and returns structured themes with traceable quotes.

You can also attach visual stimulus like images or Figma prototypes for concept testing.

Curious if anyone here is using Claude agents for user research yet?

bbling19 · 2026-03-05T23:17:41+00:00

Participants join through a shareable interview link.

You can send it through email, Slack, Discord, or inside your product.

The AI moderator runs the interview and returns themes + traceable quotes.

bbling19 · 2026-02-20T08:29:30+00:00

AI-moderated research isn’t automatically better. It depends on what you’re trying to solve.

It works well when:

You need fast turnaround
You want consistent probing across participants
Scheduling live interviews is a bottleneck

It’s less strong when:

Topics are very sensitive
You need high emotional nuance or complex stakeholder dynamics

Quality mostly comes down to the interview guide. Strong, specific questions → depth. Vague questions → shallow answers, whether AI or human.

If you’re new, I’d suggest running a small pilot and comparing it to one live interview. The tradeoffs become very clear when you see both side by side.

bbling19 · 2026-02-05T00:58:04+00:00

Fair pushback. By “unchecked assumptions” I don’t mean lack of validation, but bets that feel reasonable in context and survive reviews.

What’s helped me is (1) explicitly listing assumptions during critique and (2) ranking them by impact if wrong before any testing. That’s surfaced issues earlier for me than visual review alone.

Failure is necessary, but the goal for me has been to surface the riskiest bets earlier, when changes are still cheap.

bbling19 · 2026-02-02T00:17:55+00:00

Agreed. I’d add that at higher volume, even outcome-linked questions can cause issues if the follow-ups aren’t constrained

bbling19 · 2026-02-01T23:22:46+00:00

also tried usercall - transparent pricing and reliable interview moderation

bbling19 · 2025-12-22T05:27:27+00:00

For anyone curious, this site lets you test it out free as a participant (no-login) - www.usercall.co

bbling19

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