Compare Instagram and Facebook Stories Using Key Performance Metrics
Quick Overview
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Compare Instagram and Facebook Stories Using Key Performance Metrics
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
Comparing Instagram Stories with Facebook Stories performance.
##### Question
How would you quantitatively compare the success of Instagram Stories versus Facebook Stories? 2. What metrics and experimental or analytical methods would you employ?
##### Hints
Think DAU posting, views per story, completion rate, time spent; consider matched-pairs or A/B tests across markets.
Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Compare Instagram and Facebook Stories Using Key Performance Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Compare Instagram and Facebook Stories Using Key Performance Metrics
Scenario
You are a data scientist tasked with quantitatively comparing the success of Instagram Stories versus Facebook Stories.
Question
Define what “success” means for Stories across both apps and articulate the units of comparison (e.g., per eligible DAU, per poster, per story, per viewer session).
Specify the key metrics you would track for:
Creators (supply)
Viewers (demand/quality)
Business/monetization
Ecosystem health and cannibalization
Propose experimental designs to estimate causal differences (e.g., A/B tests, geo experiments, matched-pairs), noting interference/spillover concerns between apps.
If controlled experiments are limited, propose analytical methods (e.g., matched pairs using cross-posted stories, difference-in-differences, fixed-effects models) to compare performance.
List guardrails, assumptions, and pitfalls to ensure a fair, apples-to-apples comparison.
Hints: Consider DAU posting, views per story, completion rate, time spent; consider matched-pairs or A/B tests across markets.
Constraints & Assumptions
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?