# Explain why IG Story usage exceeds Facebook ## 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. ### 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 - 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 - 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 - What sanity checks would you run before trusting the result? - How would you handle novelty effects, seasonality, or selection bias? - What decision would you make if metrics disagree?