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?
- Slickdeals
- Metrics
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
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
- 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.
- Hypothesis 1: a recent update introduced a bug, permission change, or performance regression that broke the extension for many users.
- Hypothesis 2: an external trigger, like a security/privacy news story about extensions, or a browser update changing extension behavior broadly.
- Hypothesis 3: a pricing or feature change (e.g. new paywall) that a segment of free users reacted to by uninstalling.
- Segment the uninstalls: by browser version, extension version, geography, and user tenure to isolate which hypothesis fits the pattern.
- Recommend the next step: check release/deploy logs and error rates around the exact day of the spike before proposing a fix.
What a strong answer includes
- Treats the sharp, single-day spike as a strong signal of a specific trigger event, not gradual organic churn, and reasons accordingly.
- Names a concrete, plausible cause, a recent update or browser-level change, grounded in how browser extensions commonly break.
- Proposes segmenting by extension/browser version first, the fastest way to confirm a release-related root cause.
- Holds off recommending a fix until checking deploy timing and error logs against the spike date.
Common mistakes
- Treating a sharp one-day spike the same as gradual churn and proposing long-term retention fixes instead of investigating the trigger.
- Not checking release/deploy timing against the uninstall spike, missing the fastest path to root cause.
Likely follow-up questions
- What would you check first: deploy logs, or user-reported reviews/support tickets?
- How would you win back users who already uninstalled once you find the cause?
More metrics questions
- How would you measure the success of Facebook Likes?Meta · Metrics · Medium
- Walmart's order return rate is increasing. As a product manager, what things would you look into to isolate the problem?Amazon · Metrics · Medium
- What metrics would you track if you were PM of Facebook Birthdays?Metrics · Medium
- How do you define success for Yelp reviews?Google · Metrics · Medium
- Utilization went down by 45% on app XYZ in Italy for the month of August. Give a reason why and draft a plan to fix it.Spotify · Metrics · Medium
- Define the metrics for YouTube search.Google · Metrics · Medium
More questions from Slickdeals
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop