Product design question
How would you improve Twitch to make creators run more ads on the platform?
- Twitch
- Product design
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Balancing platform monetization incentives with creator and viewer experience.
How to approach it
- Clarify the goal: increase ad revenue from creators' streams without hurting watch time or creator satisfaction, since both drive Twitch's long-term health.
- Identify why creators currently under-run ads: fear of viewer drop-off, low ad revenue share, or lack of easy scheduling tools.
- Propose better tooling: smarter ad-break placement (during natural lulls, detected via chat activity) instead of fixed intervals.
- Propose incentive changes: improved revenue share or bonus payouts for creators who opt into more frequent, well-placed ads.
- Test with a segment of mid-tier creators first, since they have the most to gain from added revenue and enough scale to matter.
- Define success metrics: ad revenue per stream hour, and creator opt-in rate, guardrailed by viewer session length.
What a strong answer includes
- Identifies the real blocker as creator fear of chasing away viewers, not lack of ad inventory.
- Proposes a concrete tool, like AI-detected natural break points (game loading screens, chat lulls) for less disruptive ad placement.
- Gives an illustrative trade-off, e.g. a 5% revenue share bump in exchange for one more ad break per hour, marked as an assumption.
- Sets viewer session length as a hard guardrail so ad growth doesn't quietly erode watch time.
Common mistakes
- Proposing to simply mandate more ads without addressing why creators resist running them.
- Ignoring viewer experience as a guardrail against over-monetization.
Likely follow-up questions
- How would you convince a reluctant top creator to run more ads?
- What would you do if viewer watch time dropped after the change?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop