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Google·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
Jun 2026

Summary

Google PM interview with a KPI design question. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

How would you choose a KPI for a system you're building, and how would you go about improving it over time?

Product Analytics & MetricsProduct StrategyA/B Testing & Experimentation
Author's notes

I went straight to user engagement metrics without pausing to think about what 'the system' actually needed to accomplish.

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AI HintsAI Generated

Suggested Approach

Start by defining the system's goal and the user problem it solves, then select a KPI that is a leading indicator of that goal and is actionable. Explain how you would validate the KPI, set targets, and iterate using experiments and data to improve it over time.

Pro tip: Choose a KPI that balances user value and business value, and be ready to discuss how you'd avoid gaming the metric by pairing it with counter-metrics.

1. Clarify the system's objective

Understand the system's purpose, the user problem it addresses, and how it aligns with company goals. This ensures the KPI measures what truly matters.

2. Select a candidate KPI

Choose a metric that is a leading indicator of success, actionable, and easy to understand. Consider frameworks like HEART or AARRR to identify options.

3. Validate and set targets

Check that the KPI correlates with long-term value, is not easily gamed, and has a clear baseline. Set realistic targets based on historical data or benchmarks.

4. Monitor and iterate

Track the KPI over time, run experiments to test improvements, and analyze results. Use A/B testing to isolate the impact of changes.

5. Refine and adapt

As the system evolves, revisit the KPI to ensure it remains relevant. Be willing to change it if it no longer reflects the goal or if unintended consequences arise.

Key Points to Mention

  • Alignment with business and user goals
  • Leading vs. lagging indicators
  • Actionability and measurability
  • Counter-metrics to prevent gaming
  • A/B testing and experimentation
  • Iterative improvement and learning

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