I went with something around watch time retention rather than raw views, since views felt too gameable.
Start by clarifying the goal of Facebook Watch (e.g., driving engagement, ad revenue, or user retention) and then propose a primary metric that aligns with that goal, supported by secondary metrics. Emphasize that the choice depends on the product stage and business objectives, and explain how you would validate the metric.
Pro tip: Avoid fixating on a single metric; instead, discuss a metric tree that balances user value and business value, and mention how you'd guard against vanity metrics or unintended consequences.
Ask clarifying questions to understand what success means for Facebook Watch at this stage (e.g., growth, engagement, monetization).
Select a metric that directly measures progress toward the goal, such as daily watch time per user or weekly active viewers.
Identify complementary metrics (e.g., retention, ad revenue, content diversity) to ensure a balanced view and avoid optimizing one dimension at the expense of others.
Describe how you would track the metric (e.g., A/B tests, dashboards) and validate that it correlates with long-term success.
Discuss potential pitfalls (e.g., gaming the metric) and how you would mitigate them, showing strategic thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the context by asking what the first metric is and what product or feature it measures. Then, propose a complementary metric that captures a different dimension of success (e.g., quality, engagement, or long-term value) and explain how it balances or enhances the first metric. Finally, tie your choice back to Meta's goals and the user experience.
Pro tip: Choose a metric that is not just a variation of the first but provides a counterbalance—like pairing a growth metric with a quality metric—to show you understand trade-offs and avoid gaming. Also, mention how you'd measure it and what guardrail metrics you'd monitor.
Ask clarifying questions to understand what the first metric is, what product area it relates to, and what the overall goal is (e.g., growth, engagement, monetization).
Determine what aspect of success the first metric does not capture, such as user satisfaction, retention, or long-term value.
Select a metric that fills the gap and explain why it's complementary—e.g., if the first is a quantity metric, choose a quality metric.
Describe how the second metric balances the first, prevents unintended consequences, and aligns with Meta's mission and business objectives.
Outline how you would measure the metric, what data sources you'd use, and how you'd handle potential trade-offs between the two metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the goal: maximize long-term revenue without harming user engagement or experience. Then propose a data-driven approach: define metrics, run A/B tests comparing pre-roll, mid-roll, and no ads, and analyze trade-offs. Conclude with a recommendation based on results, considering segment-level differences.
Pro tip: Emphasize that the decision should be dynamic and personalized—different formats may work better for different content types, user segments, and session lengths. Also, mention the importance of measuring ad load tolerance and incremental revenue, not just total revenue.
Define success metrics (e.g., revenue, watch time, user retention) and constraints (e.g., ad load limits, user experience). Align with Meta's business goals.
Hypothesize how pre-roll vs. mid-roll might affect metrics. For example, pre-roll may drive higher completion but lower click-through, while mid-roll may have better engagement but disrupt viewing.
Set up an A/B test with control (no ads) and treatment groups (pre-roll, mid-roll). Ensure random assignment, sufficient sample size, and control for content type and user demographics.
Compare metrics across groups, check statistical significance, and segment by user cohorts (e.g., heavy vs. light viewers, content genre). Look for interaction effects.
Based on data, recommend the format that optimizes the objective. Consider hybrid approaches (e.g., pre-roll for short videos, mid-roll for longer). Plan for continuous testing and personalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.