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.
HardTechnical
0 practiced
Describe how you would estimate price elasticity of demand for Netflix subscriptions using historical price changes, promotions, and any available regional price experiments. Specify an econometric approach (for example: difference-in-differences, panel fixed effects, instrumental variables), list the variables you would include, and explain identification assumptions and how to mitigate confounding factors like content releases or competitor promotions.
MediumTechnical
0 practiced
A popular title was removed from Netflix's catalog and shortly after there was a spike in cancellations. As a data analyst, outline a step-by-step investigation plan: what SQL queries or cohort tests you would run to isolate the impact of the removal, how you'd estimate revenue loss attributable to the removal, and what robustness checks you'd perform to rule out seasonality or unrelated events.
EasyBehavioral
0 practiced
Behavioral: Tell me about a time you, as a data analyst, presented a counter-intuitive insight (for example: churn increased despite higher marketing spend). Describe the situation, the analyses you ran to validate the finding, how you structured your recommendation for non-technical stakeholders, and the outcome.
MediumTechnical
0 practiced
Given tables:Write SQL or describe steps to compute monthly CAC by channel using a 30-day attribution window where spend in month M is attributed to users acquired within 30 days of exposure in that month. Explain how you would handle multi-touch exposures and offline channels.
marketing_spend(spend_date, channel, spend_usd)
acquisitions(user_id, acquired_at, channel)HardTechnical
0 practiced
Outline how you would attribute new Netflix subscribers to marketing channels across digital and offline channels. Compare rule-based multi-touch attribution, algorithmic attribution (e.g., Markov chains), and media mix modeling (MMM): list the data each requires, pros/cons, how they handle seasonality and cannibalization, and how you would validate the attribution outputs. Which approach would you recommend for strategic budget allocation?
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