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 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.

Estimate Instagram Shopping Feature's Revenue and Test Impact

Quick Overview

This question evaluates revenue modeling, experiment design, and diagnostic troubleshooting skills for launching an in‑app social shopping feature, covering demand sizing, take‑rate monetization, randomized experiment setup, and analysis of mid‑test conversion anomalies.

Instagram Shopping: Sizing, Experiment Design, and Troubleshooting

Context

Instagram is launching an in‑app shopping feature. You are asked to:

Estimate the annual revenue opportunity from the feature.
Design an experiment to measure its impact on Gross Merchandise Value (GMV).
Troubleshoot a mid‑experiment conversion drop in the treatment group.

Assume: GMV is total dollar value of completed orders before returns; Instagram earns revenue via a take‑rate (fees/commission) on GMV. Focus on incremental impact versus status quo.

Tasks

Revenue sizing

Estimate annual revenue opportunity using a structured model (e.g., TAM × adoption × monetization/take‑rate). State assumptions clearly and provide a sensitivity range.

Experiment design to measure impact on GMV

Specify unit of randomization and exposure.
Define the primary metric, key secondary/funnel metrics, and guardrail metrics.
Describe a sample‑size/power approach suitable for heavy‑tailed GMV outcomes.

Mid‑experiment issue

Midway through the test, the treatment group’s conversion rate drops sharply. Outline a systematic, prioritized troubleshooting plan to diagnose and resolve the issue.

Quick Overview

Context

Instagram is launching an in‑app shopping feature. You are asked to:

Estimate the annual revenue opportunity from the feature.

Design an experiment to measure its impact on Gross Merchandise Value (GMV).

Troubleshoot a mid‑experiment conversion drop in the treatment group.

Assume: GMV is total dollar value of completed orders before returns; Instagram earns revenue via a take‑rate (fees/commission) on GMV. Focus on incremental impact versus status quo.

Tasks

Revenue sizing

Estimate annual revenue opportunity using a structured model (e.g., TAM × adoption × monetization/take‑rate). State assumptions clearly and provide a sensitivity range.

Experiment design to measure impact on GMV

Specify unit of randomization and exposure.

Define the primary metric, key secondary/funnel metrics, and guardrail metrics.

Describe a sample‑size/power approach suitable for heavy‑tailed GMV outcomes.

Mid‑experiment issue

Midway through the test, the treatment group’s conversion rate drops sharply. Outline a systematic, prioritized troubleshooting plan to diagnose and resolve the issue.

Estimate Instagram Shopping Feature's Revenue and Test Impact

Quick Overview

Instagram Shopping: Sizing, Experiment Design, and Troubleshooting

Context

Tasks

Solution

Submit Your Answer to Earn 20XP

Estimate Instagram Shopping Feature's Revenue and Test Impact

Quick Overview

Instagram Shopping: Sizing, Experiment Design, and Troubleshooting

Context

Tasks

Solution

Submit Your Answer to Earn 20XP