Metrics question
Average hours of videos watched per monthly active users have fallen for Youtube. What could be the possible reasons?
- Metrics
- Medium
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What this question tests
Tests root-cause hypothesis generation for a declining core engagement metric, organized by internal product causes versus external market causes.
How to approach it
- Clarify the metric: average watch hours per MAU falling could mean fewer sessions, shorter sessions, or both, so segment before hypothesizing.
- Consider internal causes: a recommendation algorithm change, more ads reducing watch tolerance, or a UI change increasing friction to start watching.
- Consider content-supply causes: a dip in high-performing creator content, or a shift toward Shorts cannibalizing long-form watch time.
- Consider external causes: rising competition from TikTok or streaming services, or seasonal effects.
- Segment the decline by content format (long-form vs Shorts), device, and region to see where it concentrates.
- Propose next steps: if Shorts is cannibalizing long-form time, decide whether that is an acceptable trade off given Shorts' own engagement value, rather than treating it as pure decline.
What a strong answer includes
- Explicitly separates internal causes (algorithm, ads, UI) from external causes (competition, content supply shifts) instead of guessing at one cause.
- Raises the specific, current, and plausible hypothesis that Shorts growth is cannibalizing long-form watch hours, a well-known dynamic at YouTube.
- Proposes segmenting by format before concluding the metric decline is bad news, since a shift to Shorts might just mean the wrong metric is being tracked.
- Connects the diagnosis to a decision: whether cannibalization is actually a problem if total engagement (including Shorts) is still healthy.
Common mistakes
- Treating average watch hours per MAU as inherently bad news without checking whether Shorts is absorbing that time instead.
- Listing hypotheses with no plan to segment or validate which one is actually driving the decline.
Likely follow-up questions
- How would you decide if a Shorts-driven decline in long-form hours is actually a problem worth fixing?
- What data would confirm whether this is a content-supply issue versus a recommendation-algorithm issue?
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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