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

SeniorPrefer not to say

Jun 2026

Summary

Pinterest product analytics question, one of those metric discrepancy cases where you have two numbers moving in opposite directions and you have to explain why. Pretty classic but still stressful in the moment.

Questions Asked (1)

Pinterest's weekly active users are up 5% but email notification open rates dropped 2%. What's going on?

Product Analytics & MetricsRoot Cause Analysis

Author's notes

I jumped straight to 'more users means more emails sent, so the denominator grew faster than the opens' and the interviewer just kind of waited.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging that the two metrics measure different things and may not be directly comparable; then systematically explore possible explanations such as changes in user mix, notification targeting, or external factors. Finally, propose how you would validate each hypothesis with data and what actions you might take.

Pro tip: Remember that correlation does not imply causation—the drop in email open rates might be a result of the same underlying change that boosted WAU, such as a shift in user demographics or a product change that reduced email relevance. Always consider the broader context and avoid jumping to conclusions.

1. Clarify the metrics and their relationship

Define what 'weekly active users' and 'email notification open rates' mean at Pinterest, and note that they measure different user behaviors. Consider whether the 5% increase in WAU is absolute or relative, and whether the 2% drop in open rates is percentage points or relative change.

2. Segment the data to identify patterns

Break down both metrics by user segments (e.g., new vs. existing, active vs. inactive, device type, geography) to see if the changes are driven by a particular group. For example, a surge in new users who haven't opted into emails could lower overall open rates.

3. Generate hypotheses for the divergence

Brainstorm possible causes: changes in email frequency or content, algorithm changes affecting notifications, seasonality, competitive actions, or a product change that increased engagement but reduced email relevance. Also consider that the two metrics might be unrelated.

4. Prioritize hypotheses and plan validation

Assess which hypotheses are most likely and testable with available data. Outline experiments or analyses (e.g., cohort analysis, A/B tests, holdout groups) to confirm or rule out each cause.

5. Recommend next steps and potential actions

Based on the most probable cause, suggest actions such as adjusting email targeting, improving personalization, or investigating further. Emphasize the need to monitor both metrics and consider trade-offs.

Key Points to Mention

Distinguish between correlation and causation; the metrics may be independent or driven by a common cause.
Consider changes in user mix: new users may be less engaged with email, lowering open rates even as overall WAU grows.
Check for product or algorithm changes that could affect email deliverability or relevance, such as a new notification system.
Look at external factors: seasonality, holidays, or competitor launches could impact email engagement.
Use segmentation and cohort analysis to isolate the affected user groups and identify root causes.
Propose a data-driven approach: form hypotheses, validate with experiments, and iterate.

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