Aglet

Prioritize duplicate customer feedback without inflating demand

Prioritization becomes distorted when ten copies of one report look more important than a distinct severe failure. Build a queue decision from deduplicated demand while keeping reach, severity, recency, and confidence visible so a smaller but consequential problem is not hidden.

Decide where the work belongs

  1. Normalize the demand signal

    Use one grouped request as the unit of demand, then retain counts for reports, distinct accounts, and affected contexts. Explain whether counts are exact or sampled. Do not let a noisy channel outweigh corroborated evidence simply because it produces more submissions.

  2. Score impact separately

    Assess customer harm, frequency of occurrence, workaround cost, and business exposure as separate dimensions. A duplicate set with high reach but a reliable workaround may queue behind a rare data-loss report. Write the tradeoff in plain language for reviewers.

  3. Set a queue position

    Choose a queue position, owner, and review date using the normalized signal. Record what would move the item up or down, such as a new release regression or evidence from an affected account. Keep the duplicate count as context, not the decision itself.

What to carry forward

The output is a ranked request with deduplicated demand, explicit impact dimensions, and a stated reason for its position. Stop short of a confident rank when account reach or severity is unknown; mark the uncertainty and define the smallest evidence needed to resolve it.

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.

Create an account See the product workflow