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LinkedIn·Product Manager·Onsite - Product Sense / Strategy·Intermediate

Intermediate
Jun 2026

Summary

One question, LinkedIn PM loop, nothing fancy. They asked me to improve a product that has nothing to do with LinkedIn and I had to figure out on the fly whether they wanted a structured teardown or just vibes.

Questions Asked (1)

Q1

How would you improve Rover?

Product Sense & IdeationRoadmap Prioritization
Author's notes

Picked a weird product to throw at a LinkedIn PM candidate but fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying Rover's core value proposition and target users, then identify a specific problem area through user segmentation and pain point analysis. Propose a prioritized solution with clear success metrics, and tie it back to LinkedIn's ecosystem if relevant.

Pro tip: Show that you understand Rover's two-sided marketplace dynamics—improvements must benefit both pet owners and sitters to be sustainable. Quantify impact where possible to demonstrate product sense.

1. Clarify product and goals

Restate Rover's mission and key business objectives (e.g., bookings, retention, trust) to ground your analysis. Ask clarifying questions if needed.

2. Segment users and identify pain points

Break down users into pet owners and sitters, then further by needs (e.g., frequent travelers, last-minute bookers). Identify top pain points through data or assumptions.

3. Brainstorm and prioritize solutions

Generate potential improvements for the chosen pain point, then prioritize using a framework like RICE or impact/effort. Explain your rationale.

4. Define success metrics and risks

Outline how you'd measure success (e.g., booking conversion, repeat rate) and potential risks or trade-offs. Consider both sides of the marketplace.

5. Summarize and tie to LinkedIn

Recap your recommendation and, if relevant, suggest how LinkedIn's platform or data could enhance the solution (e.g., professional network for trusted sitters).

Key Points to Mention

  • Two-sided marketplace dynamics: balancing supply (sitters) and demand (owners)
  • Trust and safety features (e.g., background checks, reviews, insurance)
  • User retention and repeat booking incentives
  • Leveraging LinkedIn's professional network for trusted sitter recommendations
  • Data-driven prioritization (e.g., RICE framework)
  • Clear success metrics (e.g., booking conversion rate, NPS, sitter utilization)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.