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

Senior
Jun 2026

Summary

Meta PM interview, product sense round focused on Instagram Stories. Single question but it had a lot of surface area and I don't think I covered it as well as I should have.

Questions Asked (1)

Q1

You're the PM for Instagram Stories and are evaluating whether to extend the 24-hour expiration window. What metrics would you look at to make that decision?

Product Analytics & MetricsProduct StrategyA/B Testing & Experimentation
Author's notes

I jumped straight into engagement metrics and kind of forgot to frame why the 24-hour limit exists in the first place.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of the change—whether it's to increase engagement, retention, or monetization—and then structure your answer around a north-star metric with guardrail metrics. Use a hypothesis-driven approach: propose how extending the window might affect user behavior, then identify metrics that would validate or refute that hypothesis across the user journey.

Pro tip: Anchor your metrics to Instagram's business model: time spent, ad impressions, and creator/business value. Also mention that you'd run an A/B test with a long enough duration to capture novelty effects and measure long-term retention.

1. Clarify the objective

Ask what problem we're solving: Is it to reduce content pressure, increase evergreen engagement, or boost creator retention? The goal determines which metrics matter most.

2. Define success metrics

Identify a north-star metric (e.g., daily active users, time spent) and supporting metrics (e.g., story views, completion rate, creation rate) that directly reflect the desired outcome.

3. Map user behavior changes

Hypothesize how extending the window affects viewer and creator behavior: more views on older stories, increased creation, or potential clutter. List metrics for each behavior.

4. Identify guardrail metrics

Consider potential negative effects: decreased urgency leading to fewer daily active users, ad revenue impact, or privacy concerns. Define guardrails like DAU, ad impressions, and user reports.

5. Plan validation via A/B test

Propose an experiment with a control and treatment group, measuring both short-term and long-term effects. Include sample size, duration, and success criteria.

Key Points to Mention

  • North-star metric: Daily Active Users (DAU) or Time Spent, as these drive ad revenue.
  • Engagement metrics: Story views, completion rate, replies, and shares, especially for older stories.
  • Creator metrics: Story creation rate, frequency, and retention of creators, particularly businesses.
  • Guardrail metrics: DAU, ad impressions, user reports, and privacy perceptions.
  • A/B testing methodology: Randomize users, run for at least 2-4 weeks to account for novelty, and measure long-term retention.
  • Business impact: Ad revenue, creator monetization, and competitive differentiation.

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