Product design question
How would you design a product for improving teenage health?
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What this question tests
Product design for a sensitive demographic, requiring careful segmentation and privacy-aware design.
How to approach it
- Clarify health dimension: physical, nutrition and activity, or mental health, or both, since teenage health is broad.
- Segment users: teens as primary users, with parents and schools as secondary stakeholders who influence adoption.
- Identify the pain point: teens disengage from generic health apps that feel parental or clinical, and privacy concerns limit data sharing.
- Prioritize mental health and habit-building over clinical tracking, where teen engagement and need are both high.
- Design the solution: a peer-supported habit tracker for sleep, movement, and mood check-ins, with strict privacy defaults and no parent visibility by default.
- Define success: weekly check-in consistency and self-reported wellbeing trend, not just downloads.
What a strong answer includes
- Explicitly separates teen privacy needs from parent and school stakeholder interests, a key tension in any teen-facing product.
- Picks mental health and habit formation as the higher-engagement wedge over clinical nutrition tracking.
- Proposes peer support as an engagement mechanism teens actually respond to, versus adult-style tracking.
- Uses consistency and self-reported wellbeing as success metrics, appropriate given health outcomes are slow to show.
Common mistakes
- Designing a parent-monitoring app that teens will reject or circumvent.
- Ignoring privacy and data sensitivity for a minor user base.
- Treating teenage health as one homogenous need instead of segmenting.
Likely follow-up questions
- How would you handle data privacy and consent for users under 18?
- How would you get schools or parents to support adoption without undermining teen trust?
- How would you measure actual health improvement versus app engagement?
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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