Product design question
How would you design an EV bike renting application for college students (residing in the campus)?
- Product design
- Medium
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What this question tests
Tests product design for a focused, local mobility use case: defining users, pain points, and a lean MVP for a niche campus rental service.
How to approach it
- Identify users: college students living on campus who need short trips to class, town, or errands without owning a vehicle.
- Identify pain points: cost of ownership, parking and charging access on campus, and the hassle of traditional rental processes (deposits, paperwork).
- Prioritize the core need: low-friction, short-duration rentals with simple in-app unlock and return, similar to campus bike-share models.
- Propose the solution: an app-based EV bike rental with geofenced campus zones, hourly or per-ride pricing, and designated charging or parking docks.
- Define an MVP: launch with a small fleet at a single campus, docked (not free-floating) to control charging and parking logistics before scaling.
- Define success: rides per bike per day (utilization) and repeat usage rate among students, balanced against fleet maintenance cost.
What a strong answer includes
- Anchors the design in the specific constraints of a campus environment (geofencing, limited area, charging docks) rather than a generic city-wide EV rental design.
- Chooses a docked model over free-floating for the MVP, a deliberate trade off that simplifies charging and reduces lost or misplaced bike risk early on.
- Prioritizes low-friction unlock and simple pricing, addressing the actual pain point of traditional rental paperwork and deposits.
- Defines utilization (rides per bike per day) as the core success metric, which directly reflects fleet efficiency and demand.
Common mistakes
- Designing a free-floating, city-wide system for the MVP instead of starting with a controllable, docked campus pilot.
- Ignoring charging logistics, which is the single biggest operational constraint for an EV fleet.
Likely follow-up questions
- How would you decide the right fleet size for a single campus pilot?
- How would pricing need to differ between peak class-change hours and off-peak times?
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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