Evaluate Auto-Play Impact with Key Metrics and Experiment Design

Q: Evaluate Auto-Play Impact with Key Metrics and Experiment Design

This question evaluates a data scientist's competency in experiment design, metric selection, causal inference, segmentation, and trade-off analysis for product changes such as auto-play, including defining north-star and guardrail metrics.

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Question

Streaming Auto-Play Experiment: Metrics and Design

Context

A streaming platform is considering auto-playing the next episode to increase user engagement. The company wants to evaluate if auto-play meaningfully improves engagement without harming user satisfaction or retention.

Task

Define the primary (north-star) metric(s) to evaluate auto-play and the key guardrail metrics to protect user experience and business health.
Design an experiment to measure impact:
- State the hypothesis and success criteria.
- Specify the unit of randomization and any stratification.
- Outline duration, sample size approach, and analysis plan.
If results show churn increases among power users while total watch time rises overall, explain how you would decide whether to launch.

Hints

Discuss trade-offs, segmentation, and how the north-star metric guides decisions.
Consider user-level vs. account-level randomization, exposure/triggered analysis, and guardrails for retention and satisfaction.

Evaluate Auto-Play Impact with Key Metrics and Experiment Design

Streaming Auto-Play Experiment: Metrics and Design

Context

Task

Hints

Solution

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