Aglet

Investigate notification preference overload with an event map

An investigation should show where the preference model becomes difficult to predict. Trace representative settings through event generation and channel behavior, compare grouped and separate controls, and test a person’s ability to explain the consequence without relying on internal terminology.

Build a useful investigation brief

  1. Build the preference matrix

    List event, category, channel, frequency, default, label, setting state, and resulting delivery for representative choices. Include optional and important updates. Record one event with overlapping controls so the map shows actual complexity.

  2. Trace one setting change

    Change one preference and compare in-app, email, browser, or mobile behavior where applicable. Check delayed, grouped, and muted outcomes. Record whether the setting changes the event, channel, timing, or only the presentation.

  3. Write the choice brief

    State the first ambiguity, affected categories, and competing explanations such as labels, defaults, or overlapping rules. Recommend one bounded taxonomy or copy experiment with a pass condition based on user prediction. Separate taxonomy complexity from a genuinely excessive event stream.

What to carry forward

The investigation is complete when representative settings can be traced to visible outcomes and the ambiguity boundary is explicit. Stop with one simplification experiment. Do not remove a choice merely because its current label is unclear; first identify the behavior it controls.

Technical background: Apple developer documentation.

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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