Metrics question
You are a PM for Netflix homepage and notice that while there is a lot of hits on the homepage (users are scrolling and browsing the content a lot. They are even watching the trailers and snippets.), there is a very high drop-off rate. While the time spent on the homepage has gone up, the content consumption is declining. How would you go about understanding this more and fixing this?
- Intuit
- Metrics
- Hard
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What this question tests
Tests metrics diagnosis on a nuanced pattern: rising browsing activity but falling actual content consumption, which points to a discovery or decision-fatigue problem, not an engagement problem.
How to approach it
- Restate the paradox clearly: homepage time and browsing are up, but completed content consumption (full watches) is down, meaning browsing is not converting.
- Hypothesize choice overload: users are being shown more options (a recent redesign, more rows or trailers) that increase browsing but overwhelm the decision.
- Segment by user type and by row or placement to find where drop-off between browse and watch is worst.
- Check for a recent product change, such as autoplay trailers, more thumbnails, or a recommendation algorithm update, that coincides with the shift.
- Test a hypothesis: run an experiment reducing choice or improving personalized ranking for a subset of users and compare watch-through rate.
- Redefine the success metric for the homepage to include downstream content starts and completions, not just time spent or scroll depth.
What a strong answer includes
- Names the core insight precisely: more time browsing without more watching signals decision paralysis or poor recommendation relevance, not healthy engagement.
- Connects the symptom to a plausible product cause, such as recent changes to trailer autoplay or row layout, rather than treating it as unexplainable.
- Proposes an experiment (reduced choice or better ranking for a test cohort) to validate the cause before a full rollout.
- Redefines the homepage's success metric to include watches, not just browsing time, since the current metric is misleading.
Common mistakes
- Treating rising homepage time as automatically positive without checking whether it converts to watching.
- Proposing a fix without first testing the choice-overload or recommendation-relevance hypothesis.
Likely follow-up questions
- How would you redesign the homepage metric so 'more browsing' stops looking like a false win?
- What would you test first if the drop-off were concentrated on mobile only?
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More questions from Intuit
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