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Design Experiment to Evaluate New Video-Ad Effectiveness

Last updated: Mar 29, 2026

Quick Overview

Evaluates experiment design for a new video-ad format in an auction-based consumer app. Strong answers choose randomization units that manage marketplace interference, define advertiser, revenue, and user guardrail metrics, handle power and sequential testing, and plan safe rollout.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Design Experiment to Evaluate New Video-Ad Effectiveness

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario The company plans to launch a new video-ad format and needs an experimental framework to evaluate its effectiveness. ##### Question Design an experiment to measure the impact of the new video ads. Include unit of randomization, primary/secondary metrics, sample-size/power considerations, and success criteria. If the primary metric shows no statistically significant improvement, what follow-up analyses or alternative actions would you take? ##### Hints Cover A/B vs. multivariate, geographic splits, sequential testing, metric deep-dive, heterogeneous treatment effects, experiment extensions.

Quick Answer: Evaluates experiment design for a new video-ad format in an auction-based consumer app. Strong answers choose randomization units that manage marketplace interference, define advertiser, revenue, and user guardrail metrics, handle power and sequential testing, and plan safe rollout.

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|Home/Analytics & Experimentation/Meta

Design Experiment to Evaluate New Video-Ad Effectiveness

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Jul 12, 2025, 6:59 PM
mediumData ScientistTechnical ScreenAnalytics & Experimentation
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Design an Experiment to Evaluate New Video-Ad Effectiveness

A large consumer app is considering a new video-ad format with changes to UI, creative rendering, or interaction. The goal is to estimate causal impact on advertiser effectiveness and revenue while protecting user experience.

Constraints & Assumptions

  • Ads are allocated through an auction, so experiment design may create marketplace interference.
  • Include randomization unit, metrics, power, success criteria, and rollout.
  • Protect user experience, latency, integrity, and revenue guardrails.
  • Discuss how the design changes if the first test is inconclusive.

Clarifying Questions to Ask

  • What changed in the video ad: rendering, length, placement, interaction, or targeting?
  • Is the goal advertiser lift, platform revenue, user experience, or all three?
  • Can the format be randomized post-auction, or does it affect auction ranking?
  • Are advertiser budgets and pacing affected by the treatment?

Part 1 - Randomization Design

What unit of randomization would you choose, and why?

What This Part Should Cover

  • Impression-level, user-level, advertiser-level, geo-level, or post-auction ghost/shadow options.
  • Auction interference and marketplace spillovers.
  • A phased approach if needed: isolated rendering test, then marketplace-level validation.

Part 2 - Metrics

What primary, secondary, and guardrail metrics would you define?

What This Part Should Cover

  • Advertiser outcomes such as view-through rate, clicks, conversions, cost per action, ROAS, and lift.
  • Platform outcomes such as revenue, eCPM, fill, auction health, and advertiser retention.
  • User guardrails such as retention, session time, hides, reports, load time, latency, and complaints.

Part 3 - Power and Analysis

How would you handle sample size, power, variance reduction, clustering, and sequential testing?

What This Part Should Cover

  • MDE, traffic allocation, duration, heavy-tailed outcomes, clustered or robust standard errors, CUPED, and pre-specified stopping rules.
  • Sample-ratio mismatch and instrumentation checks.

Part 4 - Success Criteria and Rollout

What launch criteria and rollout plan would you use?

What This Part Should Cover

  • Predefined thresholds for primary metric lift and guardrails.
  • Ramped rollout, monitoring, rollback plan, and follow-up tests.
  • What to do if results are inconclusive or mixed.

What a Strong Answer Covers

A strong answer respects ad-marketplace interference, chooses a defensible randomization design, measures advertiser, platform, and user outcomes, and uses staged rollout with clear guardrails.

Follow-up Questions

  • What if advertisers in treatment spend budget faster?
  • How would you test creative quality separately from ad placement?
  • What if revenue rises but user retention falls?
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