Determine Success Metrics for Circle Feature Optimization

Q: Determine Success Metrics for Circle Feature Optimization

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.

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Question

Product Analytics for Circle (Groups-like Feature)

Scenario

Circle is a new groups/community feature on a social platform, similar to Facebook Groups. The product team wants to understand how to measure success and where to focus: small private groups (≈5–6 members) or large public groups (tens–hundreds of members).

Tasks

Define the success metrics you would track for Circle. Omit guardrail metrics and cannibalization concerns.
Using only retrospective (observational) data, decide whether Circle should optimize for small private groups or large public groups. Describe the analysis plan in detail and specify a data-driven decision rule.
Suppose you have three plotted lines of “total comments ÷ total posts” across different products. Can these lines be directly compared? Explain when and how such comparisons are valid or invalid.

Determine Success Metrics for Circle Feature Optimization

Product Analytics for Circle (Groups-like Feature)

Scenario

Tasks

Solution

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