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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 Onsite rounds at Meta.

What role is this question designed for?

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

Diagnosing a drop in total ads revenue

Last updated: Apr 10, 2026

Quick Overview

This question evaluates a data scientist's competency in diagnostic analytics, causal reasoning, and metric-driven root-cause analysis for ad monetization, including interpreting bid CPM, inventory, fill-rate, and seasonality signals.

Diagnose a Sharp Drop in Global Ads Revenue

Context

You are a data scientist supporting a large ads marketplace. Last week, global ads revenue declined sharply. Potential drivers include auction dynamics (e.g., bid CPM shifts), supply/inventory changes, fill-rate issues, seasonality, and recent UX/ML/product changes. Your task is to triage data sources, isolate the drivers, and brief executives with actionable next steps.

Question

Outline a practical, step-by-step approach to diagnose root causes of the total ads revenue decline. Incorporate how you would use metrics such as bid CPM, inventory, fill-rate, and seasonality in your investigation.

Solution

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