Netflix — Identity & Commerce — Product Case Questions

Role context: Data Scientist, Identity DSE (Commerce) · Est. study time: 55 min · 5 questions

How to approach product cases here

Every product case is one move in a single decision chain: understand the business → decompose into a data problem → choose the metric or method → name the bias and trade-off → land on a decision and an action. A strong answer walks that chain and survives the interviewer's follow-ups; a weak one stops at the first plausible story.

Four things make Netflix Commerce cases distinctive:

• Revenue is Memberships × ARM — decompose before theorizing, and never read a blended number as health.
• Split churn into voluntary vs involuntary immediately; they have opposite fixes.
• The biggest levers (price, paid sharing, bundles) can't be cleanly A/B tested, so you reason from quasi-experiments — and you must separate incremental from gross.
• Name the cross-side trade-off (pricing vs churn, ad load vs engagement, sharing crackdown vs false-positive churn).

This role (Identity & Commerce, experimentation/causal) gets probed across all four case shapes — diagnose, measure success, launch-or-not, and measuring impact without a clean experiment — so the five below span all four; none is skipped.

Each answer is staged in three parts: a structured overview, an in-depth investigation with the numbers and charts you'd actually pull, and a verbal pitch showing how you'd say it in the room. Practice the overview aloud before revealing.