Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Interviewed for a product analyst role at Meta, got a classic engagement analysis question about social content. Pretty straightforward framing but there's a lot of depth hiding underneath if you're not careful.

Questions Asked (1)

For a social media app, how would you compare whether content from friends versus content from creators or strangers drives more user engagement?

Product Analytics & MetricsA/B Testing & ExperimentationProduct Sense & Ideation

Author's notes

I started by trying to define engagement, which felt right, but I spent too long on that part and the actual analysis structure came out rushed.

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AI HintsAI Generated

Suggested Approach

Start by defining engagement metrics and segmenting content by source (friends vs. creators/strangers). Then propose an A/B test or observational analysis to compare engagement, while controlling for confounders like content type and user demographics.

Pro tip: Acknowledge that engagement is multi-dimensional and that the 'better' source may depend on user intent or context; suggest a follow-up to understand why differences exist.

1. Define Engagement Metrics

Identify quantitative metrics such as likes, comments, shares, time spent, and click-through rates that represent engagement.

2. Segment Content Sources

Categorize content into friends vs. creators/strangers, ensuring clear definitions and handling edge cases like mutual friends or followed strangers.

3. Design Comparison Method

Propose an A/B test where users see varying ratios of friend vs. creator content, or use observational data with propensity score matching to control confounders.

4. Analyze and Interpret Results

Compare engagement metrics across segments, check statistical significance, and consider effect sizes and practical significance.

5. Consider Context and Next Steps

Discuss potential reasons for differences (e.g., trust, relevance) and suggest further experiments to optimize content mix.

Key Points to Mention

Define engagement metrics clearly (e.g., likes, comments, shares, time spent).
Segment content by source: friends vs. creators/strangers.
Use A/B testing or observational studies with controls for confounders.
Consider user demographics and content type as moderators.
Ensure statistical significance and practical significance.
Acknowledge limitations and propose follow-up experiments.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.