Identify Potential Users for Instagram Shopping Tab Adoption
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
# Instagram Shopping Tab: Targeting, Metrics, and Trade-offs
## Context
Instagram is launching a new in-app Shopping tab. As a data scientist, you need to:
- Identify users most likely to adopt the Shopping tab at launch.
- Define success metrics to evaluate the feature.
- Specify trade-offs and guardrails to monitor, especially impacts on the core feed experience.
Assumptions:
- You have historical logs for commerce-related actions (e.g., product-tag clicks, Shops visits, checkout usage).
- Feature placement (tab) is visible to eligible users; adoption is not forced.
## Questions
1. How would you identify potential users most likely to adopt the Shopping tab?
2. Which success metrics would you track for the feature?
3. What trade-offs and guardrails would you monitor (e.g., impact on core feed engagement)?
Quick Answer: This question evaluates a data scientist's competencies in user segmentation, product analytics, and experimentation design for an in-app shopping tab rollout, placing it squarely in the Analytics & Experimentation domain and emphasizing practical application of historical event data and metric definition.