Identify Potential Users for Instagram Shopping Tab Adoption
Overview
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.
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Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
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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
- How would you identify potential users most likely to adopt the Shopping tab?
- Which success metrics would you track for the feature?
- What trade-offs and guardrails would you monitor (e.g., impact on core feed engagement)?
Solution
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