Aglet

Triage high-volume customer feedback that seems low impact

High volume is useful evidence, but it does not define severity by itself. Triage should verify what customers experience, how often the friction occurs per workflow, and whether repeated reports represent one harmless preference or a barrier that has been underestimated.

Establish what is happening

  1. Normalize the volume

    Compare reports, distinct accounts, workflow attempts, and channels over the same time window. Look for duplicates, retries, automated submissions, and one account generating many messages. Preserve the raw counts while stating which count represents affected customers.

  2. Measure the actual friction

    Describe the action customers cannot complete, the extra work required, the workaround, and the consequence of abandoning it. A frequent extra click may be low impact, while the same pattern in a time-sensitive workflow may be materially harmful.

  3. Check for hidden severity

    Ask whether the reports cluster around accessibility, permissions, billing state, release regressions, or irreversible actions. Mark those possibilities as hypotheses until evidence supports them, and route any credible severe branch for focused investigation.

What to carry forward

The output is a triage record that distinguishes volume, reach, effort, consequence, and hidden-severity hypotheses. Stop when the team can describe the customer cost and evidence ceiling; keep the report open for investigation if volume masks a plausible serious branch.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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