Metrics question
LinkedIn’s engagement is falling by 10% day over day. What would you do?
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Whether the candidate can run a structured root cause analysis on a sudden metric drop before jumping to a fix.
How to approach it
- Confirm the drop is real: check for tracking or logging bugs, and confirm whether 10 percent day over day is sustained or a one-day blip.
- Segment the drop by platform, region, and user cohort to see if it is broad or concentrated, for example mobile app only or one country only.
- Check for external causes: a recent release, an outage, a policy or feed algorithm change, or a seasonal effect like a holiday.
- Break down engagement into its components (feed views, posts, comments, notifications clicked) to find which specific action dropped.
- Once the cause is isolated, propose a fix and a guardrail metric to confirm recovery, such as daily sessions per user returning to baseline.
What a strong answer includes
- Checks for instrumentation bugs first, since a sudden 10 percent cliff often signals a logging break, not real user behavior change.
- Segments by platform and region before theorizing, since a broad LinkedIn-wide drop points to a systemic cause like an algorithm or outage.
- Names a specific recent change to investigate, such as a feed ranking update or a notification opt-out change, as the first hypothesis to test.
Common mistakes
- Proposing a fix before confirming the drop is real and not a data issue.
- Looking only at aggregate numbers instead of segmenting by platform or cohort.
Likely follow-up questions
- What would you check first if the drop is isolated to one country?
- How would you distinguish a tracking bug from a real behavior change?
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More questions from LinkedIn
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