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Investigate why an advertiser’s spend decreased

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a Data Scientist's competency in analytics and experimentation—specifically root-cause analysis of ad spend declines, attribution and measurement diagnostics, and marketplace behavior in video advertising—within the Analytics & Experimentation domain and at a practical-application level.

  • easy
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Investigate why an advertiser’s spend decreased

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Technical Screen

A video ads product has two ad formats: - **Direct ads** (optimize for in-platform actions) - **Brand ads** (user clicks the video and lands on the advertiser’s website) You notice that for a specific advertiser, **ad spend decreased materially** over the last few weeks. ### Your tasks 1. **Validate the drop is real** (not a logging/attribution/billing artifact). 2. **Quantify** the change and localize it (which product surfaces, geos, devices, formats, campaigns, days/times, etc.). 3. Propose a structured approach to **identify root cause(s)** (e.g., demand-side budget changes vs supply/auction issues vs performance/measurement changes). 4. Explain how you would distinguish between: - advertiser intentionally lowering budget/bids - platform delivering fewer impressions (supply constraint) - auction competitiveness changes - pacing/budgeting bugs - tracking/attribution changes (especially for Brand ads with offsite landing) ### Constraints to consider (discuss explicitly) - Seasonality (day-of-week, holidays) - Changes in targeting, creatives, or landing page performance - Delayed/partial labels for offsite conversions - Selection bias (survivorship of campaigns; paused campaigns) - Metric tradeoffs: revenue vs spend vs impressions vs CTR vs CVR vs CPA/ROAS ### Deliverable Walk through the analysis plan, key metrics (primary + diagnostics + guardrails), and what data cuts or experiments you would run to confirm the leading hypothesis and recommend actions.

Quick Answer: This question evaluates a Data Scientist's competency in analytics and experimentation—specifically root-cause analysis of ad spend declines, attribution and measurement diagnostics, and marketplace behavior in video advertising—within the Analytics & Experimentation domain and at a practical-application level.

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Meta
Feb 16, 2026, 9:27 AM
Data Scientist
Technical Screen
Analytics & Experimentation
4
0
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A video ads product has two ad formats:

  • Direct ads (optimize for in-platform actions)
  • Brand ads (user clicks the video and lands on the advertiser’s website)

You notice that for a specific advertiser, ad spend decreased materially over the last few weeks.

Your tasks

  1. Validate the drop is real (not a logging/attribution/billing artifact).
  2. Quantify the change and localize it (which product surfaces, geos, devices, formats, campaigns, days/times, etc.).
  3. Propose a structured approach to identify root cause(s) (e.g., demand-side budget changes vs supply/auction issues vs performance/measurement changes).
  4. Explain how you would distinguish between:
    • advertiser intentionally lowering budget/bids
    • platform delivering fewer impressions (supply constraint)
    • auction competitiveness changes
    • pacing/budgeting bugs
    • tracking/attribution changes (especially for Brand ads with offsite landing)

Constraints to consider (discuss explicitly)

  • Seasonality (day-of-week, holidays)
  • Changes in targeting, creatives, or landing page performance
  • Delayed/partial labels for offsite conversions
  • Selection bias (survivorship of campaigns; paused campaigns)
  • Metric tradeoffs: revenue vs spend vs impressions vs CTR vs CVR vs CPA/ROAS

Deliverable

Walk through the analysis plan, key metrics (primary + diagnostics + guardrails), and what data cuts or experiments you would run to confirm the leading hypothesis and recommend actions.

Solution

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