How do I approach Analytics & Experimentation interview questions?

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 hard 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.

Determining the optimal ad load in News Feed

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's ability to model revenue-versus-experience trade-offs, estimate long-term customer lifetime value effects, and design user segmentation and experimentation for personalized ad frequency.

Scenario: Balancing Monetization and User Experience

You are asked to set a data-driven threshold for ad frequency ("ad load" = number of ads shown per unit of experience, e.g., per session or per feed length) that maximizes long-term value while protecting user experience.

Tasks

Determine the optimal ad load using marginal revenue vs. user tolerance and long-term LTV.
Segment users by ad tolerance and propose personalized caps.
Estimate long-term LTV impact of different ad loads.
Recommend a cautious rollout and monitoring strategy.

Question 1

How would you determine optimal ad load? (Hint: marginal revenue curve, user tolerance, long‑term LTV)

Solution

Show

Submit Your Answer to Earn 20XP

Loading comments...

Browse More Questions

More Analytics & Experimentation•More Meta•More Data Scientist•Meta Data Scientist•Meta Analytics & Experimentation•Data Scientist Analytics & Experimentation