Start by defining clear metrics for engagement and exposure, then use observational data to compare engagement rates across relationship types while controlling for confounders. Design an A/B test to measure the causal impact of increasing unconnected content, and finally quantify long-term value through metrics like user retention and network growth.
Pro tip: Acknowledge that near-term engagement may not capture long-term value; propose measuring downstream metrics like user retention and content discovery to show maturity. Also, emphasize the importance of guardrail metrics to ensure user experience isn't harmed.
Clearly define engagement metrics (e.g., reaction rate per view, average duration) and exposure metrics (e.g., proportion of unconnected content). State the hypothesis that friend content drives higher engagement.
Analyze historical data to compare engagement rates for friend vs. unconnected content, using stratification or regression to control for confounders like post type, time of day, and user activity.
Design an A/B test where treatment group sees increased unconnected content. Define randomization unit (e.g., user), sample size, duration, and primary/secondary metrics including guardrails.
Measure long-term effects through metrics like user retention, network growth, and content diversity. Use holdout groups or long-term tracking to assess value beyond immediate engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.