Estimate Instagram Shopping Feature's Revenue and Test Impact
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.
Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
0
0
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.
Solution
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