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Capital One·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

PM interview at Capital One with a product analytics case about Instagram engagement. Just the one question from what I can tell, but it had some depth to it.

Questions Asked (1)

Q1

You're a PM at Meta. DAU for Instagram is down while MAU is holding steady. How do you diagnose and address this?

Product Analytics & MetricsRoot Cause AnalysisProduct Strategy
Author's notes

This tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definitions and segmenting the DAU decline to isolate where and for whom it's happening. Then form hypotheses about root causes (e.g., product changes, seasonality, technical issues) and prioritize them using data. Finally, propose targeted solutions and a plan to measure impact.

Pro tip: Acknowledge that DAU/MAU ratio is a key engagement metric and that a decline signals reduced stickiness; showing you understand the business implication (e.g., ad revenue, network effects) will set you apart.

1. Clarify and Validate

Confirm the definitions of DAU and MAU, check data accuracy, and ensure the decline is real and not due to tracking issues.

2. Segment and Localize

Break down DAU by dimensions (platform, geography, user cohort, feature usage) to identify which segments are driving the decline.

3. Generate Hypotheses

Brainstorm potential causes: product changes, bugs, seasonality, competition, external events, or shifting user behavior.

4. Prioritize and Test

Use data to rank hypotheses by impact and likelihood, then design quick tests or analyses to validate the top ones.

5. Recommend and Measure

Propose solutions based on root cause, outline an implementation plan, and define success metrics to track improvement.

Key Points to Mention

  • DAU/MAU ratio as a measure of engagement and stickiness
  • Segmenting by new vs. existing users, platform (iOS/Android), and geography
  • Considering recent product changes or A/B tests that might have impacted engagement
  • Checking for technical issues like app crashes or slow load times
  • Evaluating external factors such as seasonality, holidays, or competitor launches
  • Proposing a cross-functional approach (e.g., with engineering, data science) to diagnose and fix

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