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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.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Evaluate Impact of Targeting Ads to High-Intent Users | Meta Interview Question

Evaluate Impact of Targeting Ads to High-Intent Users

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in analytics and experimentation, focusing on ad allocation, user segmentation, revenue trade-offs, user experience impact, model learning rate, and overall marketplace health.

Ad Allocation to High-Intent Users Only

Scenario

A product manager proposes allocating all ad impressions to the "high-intent" user segment (users predicted to have a high probability of clicking/converting), and serving no ads to other users.

Assumptions (for clarity):

You operate a two-sided ads marketplace with finite ad inventory per day.
Users can be segmented by predicted intent (e.g., high vs. non-high).
Platform objectives include short-term revenue, user experience, and long-term growth.

Question

If we serve ads only to the high-intent segment, what are the main pros and cons in terms of:

Revenue (short-term yield and total revenue)
User experience (fatigue vs. relevance)
Long-term growth (market size, learning rate, and ecosystem health)

Hints: Consider short-term yield, user fatigue, market size, and model learning rate.

Solution

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