How would you compare Facebook vs Instagram Stories?

Quick Overview

This question evaluates a data scientist's competency in product analytics, experimental design, and causal inference for short-form ephemeral content, including metric definition, overlap and cannibalization assessment, and trade-off analysis.

How would you compare Facebook vs Instagram Stories?

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Onsite

You work on short-form ephemeral content. Both **Facebook Stories** and **Instagram Stories** exist, and leadership asks: **Which product should we invest in, and how do we measure success without being misled by cannibalization?** Design an analysis/experiment plan: 1. Define **primary**, **diagnostic**, and **guardrail** metrics for Stories. 2. Explain how you would compare Facebook vs Instagram Stories given: - Overlapping users across apps - Potential cannibalization of Feed/Reels and messaging - Different baselines and user intents per app 3. Propose at least one **causal identification strategy** (e.g., A/B test, geo experiment, diff-in-diff) and discuss key biases/confounders. 4. Describe what decision you would recommend given possible outcomes (e.g., engagement up but revenue down; creator supply up but user retention flat).

Overview: This question evaluates a data scientist's competency in product analytics, experimental design, and causal inference for short-form ephemeral content, including metric definition, overlap and cannibalization assessment, and trade-off analysis.

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Nov 1, 2025
easyData ScientistOnsiteAnalytics & Experimentation
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You work on short-form ephemeral content. Both Facebook Stories and Instagram Stories exist, and leadership asks: Which product should we invest in, and how do we measure success without being misled by cannibalization?

Design an analysis/experiment plan:

  1. Define primary , diagnostic , and guardrail metrics for Stories.
  2. Explain how you would compare Facebook vs Instagram Stories given:
    • Overlapping users across apps
    • Potential cannibalization of Feed/Reels and messaging
    • Different baselines and user intents per app
  3. Propose at least one causal identification strategy (e.g., A/B test, geo experiment, diff-in-diff) and discuss key biases/confounders.
  4. Describe what decision you would recommend given possible outcomes (e.g., engagement up but revenue down; creator supply up but user retention flat).
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