Metrics question

How would YouTube go about detecting if a video is watched in a group or not?

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What this question tests

Metrics and product thinking for inferring an unobserved behavior (group viewing) from indirect signals, a common ambiguous data problem.

How to approach it

  1. Clarify the goal: why does YouTube care if a video is watched in a group, likely for better engagement measurement or targeted content and ad relevance.
  2. State the challenge: YouTube cannot directly observe how many people are in a room, so this must be inferred from indirect signals.
  3. Propose signals: device type (smart TV or living room device usage correlates with group viewing more than mobile), time of day (evening and weekend viewing on TV), and engagement patterns like pause and rewind frequency, which may differ for group co-viewing.
  4. Propose a secondary signal: multiple accounts logging engagement (likes, comments) shortly after a single viewing session on a shared device.
  5. Build a probabilistic model combining these signals into a group-viewing likelihood score rather than a binary yes or no answer.
  6. Define success as the model's ability to predict group viewing sessions validated against a small opt-in survey sample.

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