Analyze Data to Boost Group Post Comment Rates
Overview
This question evaluates a data scientist's skills in product analytics, experimentation design, causal inference, funnel and segmentation analysis, metric definition, and A/B testing for driving comment coverage on group posts.
Meta
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
53
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Scenario
A social shopping platform wants to increase the share of group posts that receive at least one comment.
Task
Design a rigorous analytics and experimentation plan to improve the "comment coverage" metric.
Definitions and Goal
- Primary metric: Comment coverage = number of posts that receive ≥1 comment within a defined time window (e.g., 48 hours) / number of posts created.
- Scope: Group posts only. Include organic comments; exclude spam/removed content.
Questions
- What data would you examine first? Specify the initial checks, core datasets, and key descriptive cuts.
- Provide a step-by-step diagnostic structure to identify why posts fail to receive comments.
- Map the user-journey funnel leading to a first comment. Include both the viewer (commenter) and the author (post) perspectives.
- Propose meaningful user and content segments to analyze.
- Brainstorm at least 10 tactics to increase comment rate.
- Rank those tactics by priority and justify your prioritization criteria.
- Choose one tactic and design an A/B test to validate its impact (units, metrics, power, guardrails, duration, and analysis plan).
- If an experiment shows increased impressions but no increase in comments, interpret the result and outline concrete next steps.
Solution
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