##### Scenario Social app launches a restaurant-recommendation feed that may compete with existing new-friend suggestions; product team wants to understand engagement trade-offs across features and page types. ##### Question Which metrics would you track to evaluate the restaurant-recommendation feature from both the user side and the restaurant side? Overall engagement stays flat but the new-friend add rate falls by 2 %. How would you interpret this change? What product or targeting changes would you propose to mitigate any cannibalization between restaurant recommendations and new-friend suggestions? 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. ##### Hints Tie metrics to user value, surface cannibalization hypotheses, segment by audience, propose experiments or ranking changes to balance resource allocation.

Evaluates product analytics for restaurant recommendations that may cannibalize new-friend suggestions. Strong answers track user and restaurant-side value, interpret flat engagement with a 2% friend-add decline, and propose targeting, placement, or ranking mitigations.

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

A large social app launches a restaurant-recommendation feed that may compete with existing new-friend suggestions across page types such as group pages, celebrity pages, and family pages. The product team wants to understand engagement trade-offs and avoid cannibalization.

Constraints & Assumptions

Evaluate both user-side and restaurant-side value.
Separate total engagement from shifts between features.
Include guardrails for social graph growth and content quality.
Treat flat overall engagement with a friend-add decline as a meaningful trade-off to interpret.

Clarifying Questions to Ask Guidance

Where is the restaurant feature shown relative to new-friend suggestions?
What action is considered success for restaurant recommendations?
Are restaurants businesses, pages, or external destinations?
Is the product goal engagement, local discovery, revenue, or retention?

Part 1 - Metrics

Which metrics would you track to evaluate the restaurant-recommendation feature from both the user side and the restaurant side?

What This Part Should Cover Guidance

User metrics such as reach, impressions, CTR, saves, shares, directions, reservations, dwell, repeat use, retention, hides, and satisfaction.
Restaurant-side metrics such as page visits, follows, calls, reservations, orders, reviews, and merchant distribution.
Guardrails for new-friend suggestions, core engagement, spam, quality, and cannibalization.

Part 2 - Interpretation

Overall engagement is flat, but the new-friend add rate falls by 2%. How would you interpret this change?

What This Part Should Cover Guidance

Cannibalization versus harmless substitution.
Segment and page-type analysis.
Long-term impact on social graph growth, retention, and user value.
Statistical and practical significance.

Part 3 - Mitigation

What product or targeting changes would you propose to mitigate cannibalization?

What This Part Should Cover Guidance

Placement, ranking, targeting, frequency caps, page-type-specific treatment, personalization, multi-objective ranking, and holdouts.
Experiments to validate mitigations.

What a Strong Answer Covers Guidance

A strong answer tracks incremental value and substitution, interprets friend-add decline in business context, and proposes targeted product changes that preserve restaurant discovery without damaging social growth.

Follow-up Questions Guidance

What if restaurant clicks are high but downstream actions are low?
How would you detect page-type-specific cannibalization?
What if the 2% friend-add drop affects only new users?