What should a product feedback survey ask?

Fewer, more open questions: what were you trying to accomplish, where did the product slow you down, what would you fix first and why. With Diaform the AI handles the depth, each open question becomes a probed conversation, so 3-5 core questions usually outperform a 15-field static survey. See our product survey questions page for ready-to-use sets.

Does Diaform replace a feedback board like Canny?

They solve different halves of the problem. Boards let users file and vote on requests; Diaform finds out why those requests exist and what the quiet majority thinks. Teams often keep the board for logging and use Diaform conversations to validate before anything gets built.

Can I embed it in my product?

Yes. Run it as an embedded experience in your app or site, or as a shared link from release notes, lifecycle emails, or support follow-ups. Custom domains keep the experience on your brand either way.

How do I get users to actually respond?

Ask at the moment the experience happens, after the feature was used, after the rating was given, and keep it short. The conversational format itself helps: answering by voice for two minutes is less work than typing paragraphs, which is exactly why response depth goes up.

Product feedback that finds the root cause.

"Add dark mode" is a request. Why they want it is a roadmap decision. Diaform runs product feedback conversations that probe every request, complaint, and rating down to the underlying problem, so you build the right thing, not the loudest thing.

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Why product feedback is usually unusable

Requests without reasons

A feature-request list tells you what customers concluded, not what problem they hit. Building the literal request often ships the wrong fix for the real workflow.

Ratings without context

A 3-star in-app rating is a signal with no content. The static form that follows, one optional textbox, converts almost nobody, and the few answers are one line.

Vocal minority steers the roadmap

Widgets and boards over-represent power users who enjoy filing feedback. The quiet majority churns without ever telling you what was missing.

What Diaform does with product feedback

Probes to the underlying problem

For every request or complaint, the AI asks what a good PM would: the context, the frequency, the workaround, the cost of not fixing it. Conclusions become evidence.

Voice captures the frustration

Users explaining a broken workflow out loud give 2-3× more detail than they'd ever type, and the sentiment on the transcript tells you how much it hurts.

Knows your product

Upload docs, changelogs, and roadmap context so follow-ups are specific, and the AI can distinguish a missing feature from an undiscovered one.

Summaries instead of raw transcripts

Each response arrives summarized with sentiment and answer confidence. Aggregate views show recurring themes across the response set.

Alerts for the feedback that can't wait

A churn-risk complaint or a critical bug mention pings the right channel in Slack immediately, not at the next roadmap review.

Fits your existing motion

Trigger conversations from in-product moments, lifecycle emails, or support follow-ups. Export to CSV or push responses onward with webhooks.

How product feedback collection works

01
Define what you want to learn
Feature validation, friction discovery, post-release reaction, set the questions and goals, and give the AI your product context.
02
Put the link in the product moment
Embed it in-app, link it from a release note, or send it to a cohort after a feature ships. One link works everywhere.
03
The AI runs the conversation
It asks your questions, probes each answer to the root cause, and closes once it has what you need, typically 3-6 minutes per customer.
04
Prioritize with evidence
Review summaries and sentiment per response, see recurring themes in analytics, and export the set when it's time to argue the roadmap.

Product feedback moments worth a conversation

Feature validation before you build

Test whether the requested feature solves the actual problem, and what the smallest version that helps would be.

Post-release reaction

Ship, then ask the cohort that used it. Find out whether the feature landed, confused, or missed the workflow entirely.

Friction and bug discovery

"Something felt off" becomes a reproducible description when the AI asks what they were doing, what they expected, and what happened instead.

Low-rating follow-up

Turn a 3-star rating into a conversation about what specifically fell short, while the session is still fresh in memory.

Beta and early-access programs

Structured conversations with beta users at scale, without booking thirty calls or reading thirty raw transcripts.

Roadmap input from churned trials

Trial users who didn't convert know exactly what was missing. Ask them before they forget you exist.

Feedback widget or static survey vs. Diaform

Widgets, boards, and static surveys

Collects conclusions: requests, ratings, one-liners
No follow-up, the reasoning stays unknown
Over-represents the vocal minority
Open text needs manual reading and tagging
Urgent signals wait for the next review

Diaform

Collects reasons: context, workflow, severity
AI probes every answer to the root cause
Conversational format reaches the quiet majority
Summaries, sentiment, and themes auto-generated
Slack alerts fire the moment a red flag appears

Frequently asked questions.

The practical details behind setting up, running, and scaling this kind of research with Diaform.

Software for collecting and making sense of user feedback about a product: feature requests, friction reports, satisfaction signals, and reactions to releases. Tools differ mainly in depth, widgets and boards log conclusions, while conversational tools like Diaform probe for the reasoning behind them.

More conversations your team can run.

Explore the other ways Diaform can turn a static request for feedback into a useful conversation.

Your customers have more to say.

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