Metrics question

You have found out that 20% of Slack extension users uninstalled in one day. What are your assumptions, and what will you look into to find the root cause?

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What this question tests

Root-cause diagnosis for churn: can you generate concrete, testable hypotheses for a sharp uninstall spike rather than one vague guess.

How to approach it

  1. Confirm the scope: 20% uninstall in one day is unusually sharp, so first check if it's tied to a specific release, event, or a single segment.
  2. Hypothesis 1: a recent update introduced a bug, permission change, or performance regression that broke the extension for many users.
  3. Hypothesis 2: an external trigger, like a security/privacy news story about extensions, or a browser update changing extension behavior broadly.
  4. Hypothesis 3: a pricing or feature change (e.g. new paywall) that a segment of free users reacted to by uninstalling.
  5. Segment the uninstalls: by browser version, extension version, geography, and user tenure to isolate which hypothesis fits the pattern.
  6. Recommend the next step: check release/deploy logs and error rates around the exact day of the spike before proposing a fix.

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