Analyze Data to Boost Group Post Comment Rates

Quick Overview

Meta analytics prompt on increasing group post comment coverage, covering funnel diagnosis, segmentation, tactics, prioritization, A/B test design, comment quality guardrails, and interpretation of more impressions without more comments.

Analyze Data to Boost Group Post Comment Rates

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario Improving the percentage of group posts that receive at least one comment on a social shopping platform. ##### Question To raise the proportion of posts that get at least one comment, what data would you examine first? Lay out your step-by-step analytical structure to diagnose why posts do not receive comments. Map the user-journey funnel for commenting and suggest meaningful user segments to analyze. Brainstorm at least ten different tactics to increase comment rate. Rank those tactics by priority and justify your prioritization criteria. Pick one tactic and design an A/B test to validate its impact. If an experiment shows more impressions but no increase in comments, how would you interpret the result and what next steps would you take? ##### Hints Consider funnel conversion rates, segmentation, experiment design, and creative growth tactics.

Overview: Meta analytics prompt on increasing group post comment coverage, covering funnel diagnosis, segmentation, tactics, prioritization, A/B test design, comment quality guardrails, and interpretation of more impressions without more comments.

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Jul 12, 2025
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Analytics Plan to Increase Group Post Comment Coverage

A social shopping platform wants to increase the percentage of group posts that receive at least one comment.

Define comment coverage as:

posts with >=1 valid comment within T hours / eligible group posts created

Assume spam, removed comments, and deleted posts should be excluded.

Constraints & Assumptions

  • Specify the comment window T , such as 24, 48, or 72 hours.
  • Separate reach, relevance, and commenting friction.
  • Analyze both author/post-side and viewer/commenter-side funnels.
  • Choose one tactic and design an A/B test.

Clarifying Questions to Ask Guidance

  • What counts as a valid comment?
  • Are group posts currently limited by impressions or by low comment conversion after impressions?
  • Which surfaces distribute group posts: feed, notifications, search, groups, or recommendations?
  • What guardrails matter most: comment quality, spam, retention, notifications, or author satisfaction?

What a Strong Answer Covers Guidance

  • Initial data: posts, comments, impressions, opens, notifications, groups, users, integrity labels, ranking position, and moderation status.
  • Data quality checks: duplicate events, bots, deleted/hidden content, logging changes, time zones, and denominator consistency.
  • Diagnostic structure: zero impressions versus impressions without comments, time to first comment, impressions per post, comments per impression, composer starts, submits, and failed/removed comments.
  • User-journey funnel from post creation to distribution, impression, dwell, comment composer open, comment submit, moderation pass, and author response.
  • Segments: group size/activity, author tenure, post type, topic, media, surface, viewer relationship to author, locale, device, and daypart.
  • At least ten tactics, ranked by impact, confidence, effort, risk, and learning value.
  • A/B test with randomization unit, primary metric, secondary metrics, guardrails, power, duration, and interpretation.
  • If impressions rise but comments do not, diagnose relevance, intent, creative quality, friction, audience mismatch, or comment-quality threshold.

Follow-up Questions Guidance

  • Which decomposition would you run first?
  • How would you avoid increasing low-quality comments?
  • What if comment coverage rises but author retention falls?
  • How would network effects affect the experiment?
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