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Meta·Software Engineer·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

Meta PM interview question for Marketplace, classic metrics drop scenario. Not much context on how it went but the question itself is pretty meaty.

Questions Asked (1)

Q1

You're the PM for Facebook Marketplace and come back from a 10-day vacation to discover traffic has dropped 10%. Walk through how you'd handle it.

Root Cause AnalysisProduct Analytics & MetricsStakeholder Management
Author's notes

The vacation framing is doing a lot of work here.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and impact of the traffic drop, then systematically investigate potential causes using a data-driven approach. Prioritize quick wins and communicate findings to stakeholders while working towards a resolution.

Pro tip: Show empathy for the team and avoid jumping to conclusions; acknowledge that a 10% drop over 10 days could be due to multiple factors, and emphasize the importance of validating hypotheses with data before taking action.

1. Clarify and Validate the Metric

Confirm what 'traffic' means (e.g., DAU, sessions, page views) and ensure the drop is real and not a data anomaly. Check if the drop is consistent across platforms, regions, and user segments.

2. Gather Context and Timeline

Review recent changes, releases, or external events (e.g., holidays, competitor launches) that occurred during your vacation. Talk to team members to understand what happened while you were away.

3. Segment and Drill Down

Break down the traffic by dimensions like device, geography, user cohort, and entry points to isolate where the drop is concentrated. Use tools like funnel analysis to identify drop-off points.

4. Form and Test Hypotheses

Develop hypotheses for the root cause (e.g., bug, algorithm change, seasonality) and validate them with data. Prioritize hypotheses based on likelihood and impact.

5. Communicate and Act

Share findings with stakeholders, propose immediate mitigations if needed, and outline a plan for longer-term fixes. Ensure learnings are documented to prevent recurrence.

Key Points to Mention

  • Define the metric precisely and check for data pipeline issues.
  • Segment the data to identify if the drop is uniform or concentrated.
  • Consider external factors (e.g., seasonality, competitors) and internal changes (e.g., code deploys, experiments).
  • Prioritize hypotheses using impact vs. effort and validate with A/B tests or queries.
  • Communicate proactively with stakeholders, including engineering, data science, and leadership.
  • Document the incident and post-mortem to improve future response.

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