Netflix Business Model, Revenue & Cost Structure Questions
In-depth analysis of Netflix's business model, revenue streams, pricing strategy, content costs, operating expenses, and profitability drivers, along with competitive positioning and platform economics within the streaming industry.
MediumTechnical
0 practiced
Design metrics to quantify binge-watching and engagement for Netflix. Propose at least three defensible metrics (with precise definitions and calculation approach), explain why each matters for retention and content investment, and describe how you would compute them from streaming event data.
EasyTechnical
0 practiced
List the key data quality checks and validation steps you would run before generating an executive monthly revenue report for Netflix. Include SQL-level tests, reconciliation tests against source systems, anomaly checks, and owner sign-offs to prevent incorrect numbers reaching leadership.
MediumTechnical
0 practiced
Design a content metadata schema to support analytics: list required fields and types (for example title_id, primary_title, genres, cast, release_date, production_country, language, runtime, content_type, parental_rating, tags), relationships (many-to-many), and indexing strategies to support queries such as top titles by genre, lookups by actor, and similarity joins. Explain how to version metadata and handle updates.
HardTechnical
0 practiced
Hard forecasting: Explain an approach to forecast net subscriber additions month-over-month for the next 24 months, accounting for seasonality, price changes, major content releases, marketing spend, and macroeconomic indicators. Specify model choices, feature engineering, data sources, evaluation metrics, and how you would produce scenario-based forecasts for leadership.
HardSystem Design
0 practiced
Design an analytics platform to support A/B tests for pricing and packaging across multiple regions. Requirements: deterministic bucketing, multi-arm experiments, near real-time metrics for KPI monitoring, integration with billing and retention data, support for millions of users and concurrent experiments. Provide component-level architecture and how you ensure experiment integrity and low-latency reporting.
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