Estimation question
YouTube Red is a premium service without ads. Assume that 1.5% of initial YouTube user base signs up for the service. What is the lifetime revenue Google generates from those users?
- Estimation
- Hard
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What this question tests
Tests structured estimation with explicit, reasonable assumptions and a clear formula, more than precision of the final number.
How to approach it
- State the goal: estimate lifetime revenue from the cohort of YouTube users who signed up for YouTube Red at launch.
- Estimate the initial YouTube user base: assume 1 billion monthly active users globally at the time (stating this as an assumption).
- Apply the 1.5% conversion: 1B x 1.5% = 15 million subscribers.
- Assume a price point and geography split: assume $10/month average (blending the $9.99 US price with lower international pricing).
- Assume an average subscriber lifetime before churn: assume 24 months average retention based on typical subscription-service churn.
- Compute: 15M subscribers x $10/month x 24 months = $3.6B lifetime revenue, then sanity-check against Google's overall revenue scale and note this ignores churn curve shape and CAC.
What a strong answer includes
- States every assumption explicitly (user base size, price, average lifetime) before multiplying, rather than presenting a single unexplained number.
- Uses round, defensible numbers (1B MAU, $10/month, 24-month lifetime) and flags them clearly as assumptions.
- Sanity-checks the final number against known company scale (does $3.6B look plausible relative to Google's total revenue at the time?).
- Notes the simplification of treating churn as a flat average lifetime rather than a decay curve, showing awareness of the model's limits.
Common mistakes
- Skipping the sanity check, producing a number wildly out of scale with reality.
- Not stating assumptions, so the interviewer can't follow or challenge the logic.
- Ignoring churn entirely and assuming subscribers stay forever.
Likely follow-up questions
- How would you adjust if churn were front-loaded in the first 3 months?
- How sensitive is your answer to the price assumption?
- How would you validate the 1.5% conversion assumption with real data?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop