Estimation question
How would you estimate Google AdSense revenue?
- Estimation
- Medium
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What this question tests
Tests structured estimation of an ad-network revenue figure using publisher traffic, ad impressions, and CPM assumptions.
How to approach it
- State the goal: estimate Google AdSense's annual revenue, the network serving ads on third-party publisher sites.
- Estimate the ad-supported web audience: assume roughly 2 billion unique monthly visitors across AdSense-participating publisher sites globally.
- Estimate ad impressions per visitor: assume an average of 20 ad impressions per visitor per month across pageviews.
- Estimate average CPM: assume a blended global CPM of $2, accounting for wide variance by region and ad format.
- Compute: 2 billion visitors times 20 impressions divided by 1000 times $2, about $80 billion in gross ad spend flowing through the network annually.
- Apply AdSense's revenue share: Google typically keeps roughly a third as its own revenue, about $25 billion, then sanity-check against disclosed figures.
What a strong answer includes
- Builds from a clear formula, visitors times impressions times CPM, then applies the actual revenue-share split, rather than guessing directly.
- States each assumption explicitly, audience size, impressions per visitor, CPM, so the logic is auditable and challengeable.
- Correctly distinguishes gross ad spend from Google's actual take, the revenue share, a detail many candidates miss.
- Sanity-checks against Google's own disclosed network revenue line to validate the order of magnitude.
Common mistakes
- Computing only gross ad spend and presenting it as Google's revenue, ignoring the publisher revenue share.
- Using one flat CPM assumption with no acknowledgment of the wide real-world variance by region and format.
Likely follow-up questions
- How would this estimate differ between search ads and display ads?
- How sensitive is your final number to the CPM assumption?
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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