Determine Success Metrics for Circle Feature Optimization
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
##### Scenario
Product analytics for a new Circle feature (similar to Facebook Groups) on a social platform.
##### Question
Define the success metrics you would track for Circle; omit guardrail metrics and cannibalization concerns. Using only retrospective data, decide whether Circle should optimize for small private groups (≈5–6 members) or large public groups (tens–hundreds). Detail your analysis. Given three plotted lines of total comments per total posts across different products, can they be directly compared? Explain.
##### Hints
Focus on engagement/retention metrics, cohort normalization, and differences in user base sizes.
Quick Answer: This question evaluates proficiency in product analytics, metric design, and observational causal inference for product decisions, testing competencies in defining success metrics, prioritizing user segments, and judging the comparability of aggregated engagement ratios.