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

SeniorPrefer not to say
Apr 2026

Summary

Microsoft product sense interview, one question about diagnosing a metric drop on a platform they don't even own. Short and a bit disorienting.

Questions Asked (1)

Q1

Usage on the Apple App Store dropped 30% recently. How would you diagnose what happened?

Product Analytics & MetricsRoot Cause AnalysisProduct Sense & Ideation
Author's notes

The weird part is this is a Microsoft interview and they're asking about Apple's platform.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric and scope: is the 30% drop in downloads, active users, or revenue? Then segment the data by dimensions like time, geography, device, and user cohort to isolate the cause. Finally, correlate with internal changes (releases, pricing, marketing) and external factors (competitor launches, OS updates, seasonality) to identify the root cause.

Pro tip: Always validate the data first—check for tracking errors or definition changes before assuming a real drop. Also, consider that a 30% drop might be intentional (e.g., removing a feature) or due to a shift in strategy, so align with business goals.

1. Clarify the metric and goal

Confirm what 'usage' means (e.g., daily active users, sessions, downloads) and the time period. Ask if the drop is compared to last week, month, or year, and if it's a sudden or gradual change.

2. Segment the data

Break down the metric by dimensions such as platform (iOS version, device), geography, user demographics, acquisition channel, and app version to see if the drop is concentrated in a specific segment.

3. Check internal factors

Review recent app updates, pricing changes, marketing campaigns, or feature removals that could impact usage. Also check for technical issues like crashes or login failures.

4. Analyze external factors

Investigate competitor launches, Apple policy changes, seasonal trends, or macroeconomic events that might affect user behavior.

5. Form and test hypotheses

Prioritize the most likely causes based on data, then propose further analysis or experiments (e.g., A/B tests, user surveys) to confirm and address the root cause.

Key Points to Mention

  • Define the metric precisely: downloads vs. active users vs. sessions, and the time frame.
  • Segment by dimensions: iOS version, device type, geography, user cohort, acquisition source.
  • Check for internal changes: app updates, pricing, marketing, feature deprecation, bugs.
  • Consider external factors: competitor actions, Apple policy changes, seasonality, economic trends.
  • Validate data quality: tracking issues, definition changes, or reporting errors.
  • Prioritize hypotheses and suggest next steps: further analysis, experiments, or user research.

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