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Explain why IG Story usage exceeds Facebook

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's proficiency in product analytics, experimental design, metric validation, and causal reasoning for feature adoption across two apps, and is categorized under Analytics & Experimentation.

  • easy
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Explain why IG Story usage exceeds Facebook

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: Onsite

## Product analytics case: Instagram vs Facebook Stories You work on **Stories** across two apps: **Instagram (IG)** and **Facebook (FB)**. A dashboard shows that **Stories are used by a much larger fraction of users on IG than on FB**, and this pattern has been stable over time. ### Your task Explain how you would **diagnose and quantify** why IG has higher Stories usage than FB, and what you would do next. ### Clarifications / assumptions (state and validate in interview) - Assume the metric shown is: **Story Usage Rate** = \( \frac{\text{# distinct active users who had ≥1 story view OR story creation event in a day}}{\text{# distinct daily active users (DAU)}} \). - Events are reliably logged, but instrumentation differences across apps are possible. - You can segment by user/app/version/country, and you can run experiments. ### What to cover 1. **Metric and data validation**: what could make the metric incomparable across apps? 2. **Decomposition**: break the gap into components (numerator vs denominator; view vs create; eligibility/supply/demand). 3. **Root-cause hypotheses** (product, audience, surface area, network effects, content supply). 4. **Analyses to run** to confirm/refute hypotheses (including segmentation and cohort analyses). 5. **Action plan**: what experiments or product changes you would test on FB Stories, and what success metrics + guardrails you’d use.

Quick Answer: This question evaluates a data scientist's proficiency in product analytics, experimental design, metric validation, and causal reasoning for feature adoption across two apps, and is categorized under Analytics & Experimentation.

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Meta
Aug 5, 2025, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
4
0

Product analytics case: Instagram vs Facebook Stories

You work on Stories across two apps: Instagram (IG) and Facebook (FB). A dashboard shows that Stories are used by a much larger fraction of users on IG than on FB, and this pattern has been stable over time.

Your task

Explain how you would diagnose and quantify why IG has higher Stories usage than FB, and what you would do next.

Clarifications / assumptions (state and validate in interview)

  • Assume the metric shown is: Story Usage Rate = \frac{\text{# distinct active users who had ≥1 story view OR story creation event in a day}}{\text{# distinct daily active users (DAU)}} .
  • Events are reliably logged, but instrumentation differences across apps are possible.
  • You can segment by user/app/version/country, and you can run experiments.

What to cover

  1. Metric and data validation : what could make the metric incomparable across apps?
  2. Decomposition : break the gap into components (numerator vs denominator; view vs create; eligibility/supply/demand).
  3. Root-cause hypotheses (product, audience, surface area, network effects, content supply).
  4. Analyses to run to confirm/refute hypotheses (including segmentation and cohort analyses).
  5. Action plan : what experiments or product changes you would test on FB Stories, and what success metrics + guardrails you’d use.

Solution

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