Evaluate Metrics for Restaurant-Feature Impact and Engagement Trade-offs

Q: Evaluate Metrics for Restaurant-Feature Impact and Engagement Trade-offs

This question evaluates product analytics and experimentation competencies such as metric design, engagement trade-off analysis, cannibalization detection, A/B test interpretation, segmentation, and hypothesis-driven investigation in a data scientist context within the Analytics & Experimentation domain.

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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Product Analytics Case: Restaurant Recommendations vs. New-Friend Suggestions

Background

You are a data scientist at a large social app. The app launched a new restaurant-recommendation feed that may compete with existing new-friend suggestions on multiple page types (e.g., group pages, celebrity pages, family pages). The product team wants to understand engagement trade-offs and avoid cannibalization.

Tasks

Metrics: Which metrics would you track to evaluate the restaurant-recommendation feature from both the user side and the restaurant side?
Interpretation: Overall engagement is flat, but the new-friend add rate falls by 2%. How would you interpret this change?
Mitigation: What product or targeting changes would you propose to mitigate any cannibalization between restaurant recommendations and new-friend suggestions?
Page-type Differences: Activity on group pages is higher than on celebrity and family pages. Generate hypotheses to explain this difference and outline how you would validate them.

Evaluate Metrics for Restaurant-Feature Impact and Engagement Trade-offs

Product Analytics Case: Restaurant Recommendations vs. New-Friend Suggestions

Background

Tasks

Solution

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