Metrics question
Daily active users on Instagram have dropped by 20%. What are the steps you will follow to identify the root cause?
- Meta
- Metrics
- Medium
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What this question tests
Tests structured root-cause analysis on a large, sudden engagement drop, prioritizing instrumentation and segmentation before jumping to conclusions.
How to approach it
- Confirm the drop is real: rule out a tracking bug or a recent SDK or app update miscounting DAU before assuming real behavior changed.
- Segment the drop: by platform, region, and user tenure, since a 20% drop concentrated in one segment points to a specific cause.
- Check for a recent product change: a release, redesign, or algorithm change shipped around the same time.
- Check external factors: a competitor launch, an App Store or Play Store outage, a regulatory action, or a major news event.
- Check technical health: server errors, crash rate spikes, or login failures mechanically blocking users from opening the app.
- Prioritize hypotheses by how much of the drop each explains and how fast each can be checked, starting with crash rate and segment breakdown.
What a strong answer includes
- Checks instrumentation and technical health first, since a drop this large is often partly measurement or an outage.
- Segments by platform, region, and tenure before hypothesizing a cause, avoiding a one-size explanation.
- Separates internal causes, a recent release, from external ones, a competitor or outage, for a structured investigation.
- Proposes fast checks, crash rate and segment breakdown, before slower ones like surveys.
Common mistakes
- Assuming a product or behavioral cause without first ruling out a tracking or technical failure.
- Not segmenting the drop, missing that it's concentrated on one platform or region.
Likely follow-up questions
- What would you check first if the drop were concentrated on iOS only?
- How would you distinguish a real engagement drop from an outage?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop