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Meta·Product Manager·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
May 2026

Summary

Meta product sense interview, one question about optimizing photo count in the Facebook Newsfeed. Pretty open-ended and I wasn't sure how deep to go on the metrics side.

Questions Asked (1)

Q1

How would you determine the right number of photos to display in the Facebook Newsfeed?

Product Sense & IdeationA/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

I started by trying to define what 'right' even means here, which took longer than it should've.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: the right number of photos should maximize meaningful engagement without harming user experience. Then propose a data-driven approach using A/B testing to find the optimal number, balancing metrics like engagement, time spent, and user satisfaction.

Pro tip: Frame the answer around trade-offs: more photos can increase engagement but may cause information overload or reduce content diversity. Show you understand that the 'right' number may vary by user segment and context.

1. Define success metrics

Identify primary and guardrail metrics such as engagement (likes, comments, shares), time spent, and user satisfaction (surveys, sentiment). Also consider negative signals like hide/block/report rates.

2. Form hypotheses

Hypothesize how the number of photos affects user behavior. For example, more photos might increase engagement but could also lead to scroll fatigue or reduced content diversity.

3. Design A/B test

Randomly assign users to different photo count variants (e.g., 1, 3, 5, 10) and measure the impact on success metrics over a sufficient duration to capture novelty effects.

4. Analyze results and segment

Analyze overall results and segment by user demographics, behavior, and content type. Look for statistically significant differences and consider qualitative feedback.

5. Iterate and optimize

Based on findings, determine the optimal number or a dynamic approach that adjusts based on user preferences and context. Continuously monitor and refine.

Key Points to Mention

  • A/B testing methodology and statistical significance
  • Guardrail metrics to ensure no harm to user experience
  • User segmentation and personalization
  • Trade-offs between engagement and content diversity
  • Long-term vs short-term effects (novelty effect)
  • Qualitative user research to complement quantitative data

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