Metrics question
How would you grow the user base of LinkedIn by 2X over next 6 months?
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
Whether you can decompose an aggressive growth target into a workable metric tree and prioritized bets.
How to approach it
- Clarify baseline: current MAU, and whether 2x means total members or active users, and geography scope.
- Build a metric tree: new user acquisition, activation rate, and reactivation of dormant members.
- Size each lever, for example if acquisition needs to double, what channels (referrals, SEO, international expansion) get you there.
- Set guardrails: connection quality and spam should not degrade as growth accelerates.
- Pick 2 to 3 concrete bets, for example creator content for non-connected professionals, or a lighter signup flow in emerging markets.
- State the target split, like 60 percent from new user growth and 40 percent from reactivating lapsed users.
What a strong answer includes
- Uses a real number, for example if LinkedIn has roughly 900 million members (assumption), 2x in 6 months is aggressive and should be challenged with the interviewer.
- Flags that paid users and engaged users matter more than raw signups.
- Proposes a specific lever like localized onboarding for a market with low penetration.
Common mistakes
- Accepting the 2x target at face value without questioning feasibility in 6 months.
- No guardrail metric, risking a spam-driven growth hack.
- Treating LinkedIn and Hotstar as interchangeable without picking one context.
Likely follow-up questions
- Which single lever would you bet the most resources on and why?
- What would make you tell leadership 2x in 6 months is not realistic?
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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