From Feedback to Triage: How We Turn Raw Input into Product Decisions

Collecting feedback is the easy part. The hard part — the part most companies get wrong — is turning raw input into actionable product decisions without losing the nuance that made the feedback valuable in the first place.

Here's how our triage system works, from the moment feedback arrives to the point where it influences the roadmap.

Step 1: Structured intake

Every piece of feedback enters Minti through a structured form. We don't accept unstructured "thoughts" or feature requests. Instead, we ask for specific fields: your role, the recurring problem, your current workaround, how often it happens, and what a good outcome looks like.

This structure isn't bureaucracy — it's signal engineering. By constraining the input format, we ensure that every submission contains the minimum information needed for meaningful analysis. A well-structured problem description is worth fifty "it would be nice if..." comments. If you want to compare that against real raw wording, the patterns in our first 13 feedback submissions and these customer feedback examples make the difference obvious.

Step 2: Taxonomy classification

Once feedback is submitted, it gets classified against our research taxonomy. This taxonomy maps feedback to categories like problem type, affected workflow, user segment, and evidence strength.

The taxonomy isn't static. It evolves as we see new patterns in the data. When multiple pieces of feedback don't fit cleanly into existing categories, that's a signal that our model of the problem space needs updating — which is itself a valuable insight.

Step 3: Scoring and prioritization

Each piece of feedback receives a triage score based on several factors:

Evidence strength — Is the problem described with specific details, workarounds, and frequency data? Or is it vague and hypothetical?

Frequency — How often does this problem occur? Daily problems compound faster than quarterly ones.

Cluster density — Is this an isolated report, or are multiple people describing variations of the same underlying problem?

Contributor weight — Has this person demonstrated sustained, thoughtful engagement? (This is where the credit system connects to triage, and where the scorecard helps us keep the weighting transparent.)

These factors combine into a priority score that determines where a piece of feedback lands relative to everything else we're tracking.

Step 4: Clustering

Individual feedback submissions are useful. Clusters of related submissions are powerful. Our system groups feedback that describes similar problems, even when the language and framing differ across submissions.

A product manager describing "I can't get engineering to prioritize the right features" and a founder saying "our roadmap doesn't reflect what customers actually need" might be describing two sides of the same problem. Clustering surfaces these connections.

When a cluster reaches critical mass — enough independent reports with strong evidence — it becomes a candidate for the roadmap. This is where individual voices aggregate into collective signal, and it is the same compounding dynamic we describe in Building a Customer Research Engine.

Step 5: Roadmap influence

Clusters with high aggregate scores move into active roadmap consideration. This doesn't mean every popular request gets built — it means every well-evidenced problem gets genuine consideration, weighted by the quality and depth of the signal behind it.

We publish our roadmap publicly, including the reasoning behind priority decisions. If your feedback contributed to a roadmap item, you can see the connection. If something you care about didn't make the cut, you can see why. That same evidence trail is what makes building in public credible instead of performative, and it is the discipline behind the Product Evidence Kit.

This is what "building in public" means at the operational level. Not just showing the roadmap, but showing the engine that produces it.