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

Senior
Apr 2026

Summary

PM interview at Meta, one question focused on ad quality measurement. The question was deceptively specific and I don't think I fully nailed the false positive/negative framing under pressure.

Questions Asked (1)

Q1

As a PM at Meta, how would you define and measure the success of the 'Report Ad' feature, accounting for both false positives (legitimate ads incorrectly flagged) and false negatives (bad ads that slip through)?

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

I spent too long on the obvious stuff like report volume and resolution rate before getting to the actual crux of the question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of the 'Report Ad' feature: to improve ad quality and user trust by removing bad ads while minimizing harm to legitimate advertisers. Then define success metrics across user experience, advertiser impact, and operational efficiency, explicitly addressing false positives and false negatives. Finally, propose a measurement framework that balances these metrics and suggests experimentation to optimize thresholds.

Pro tip: Emphasize that false positives and false negatives have asymmetric costs—false negatives harm user trust and platform integrity, while false positives hurt advertiser ROI and can chill legitimate advertising. Propose a weighted metric or a guardrail metric to ensure balance.

1. Clarify the feature's purpose and stakeholders

Define the primary goal of 'Report Ad': to empower users to flag bad ads and improve ad quality. Identify key stakeholders: users, advertisers, and Meta's ad review team.

2. Define success metrics for user experience

Measure user trust and satisfaction: report rate, resolution rate, and reduction in bad ad impressions. Track false negatives via user surveys or downstream signals like ad hides.

3. Define success metrics for advertiser impact

Measure false positives: incorrectly removed ads, advertiser appeals, and impact on advertiser spend and retention. Use control groups to estimate counterfactual.

4. Define operational and system metrics

Track precision and recall of the ad review system, time to resolution, and cost per review. Use these to optimize the trade-off between false positives and false negatives.

5. Propose a balanced scorecard and experimentation plan

Combine metrics into a balanced scorecard with guardrails. Suggest A/B tests to tune thresholds and measure long-term effects on user trust and advertiser health.

Key Points to Mention

  • False positives harm advertisers (lost revenue, trust) and false negatives harm users (bad experience, trust erosion).
  • Use precision and recall as technical metrics, but translate them into business metrics like advertiser churn and user retention.
  • Consider leading and lagging indicators: report rate (leading) vs. user trust score (lagging).
  • Propose a guardrail metric to ensure one type of error doesn't dominate (e.g., cap false positive rate at X%).
  • Leverage A/B testing to measure causal impact of changes to the reporting system.
  • Account for feedback loops: user reports train the model, so measure long-term model improvement.

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