Diagnose a drop in retention
Last updated: July 10, 2025
Quick Overview
The engagement dropped by 15% this week. Walk through your investigation process.
Uber
July 10, 20256
6
2,001 solved
The engagement dropped by 15% this week. Walk through your investigation process.
This product question from Uber's Take-home Project goes beyond feature brainstorming. The interviewer expects a framework-driven approach that considers market dynamics, user segmentation, and data-informed decision making.
What the Interviewer Expects
- Demonstrate strategic product thinking beyond feature-level decisions
- Analyze second-order effects and long-term ecosystem impact
- Balance multiple stakeholder needs with clear trade-off rationale
- Design experiments to validate key assumptions before building
- Consider platform effects, network effects, and marketplace dynamics
- Propose a phased rollout strategy with go/no-go criteria
Key Topics to Cover
How to Approach This
- Start with the user. Who are they, what problem do they have, and what is their current workaround?
- Use a prioritization framework: impact vs effort, RICE scoring, or ICE scoring.
- Define success metrics using the AARRR framework: Acquisition, Activation, Retention, Revenue, Referral.
- Consider second-order effects. A feature might boost short-term engagement but hurt long-term retention.
Possible Follow-up Questions
- How would you sequence the rollout across different user segments?
- What would you do if usage data contradicts user feedback?
- How would you decide to kill this feature if it's not working?
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Problem Framing
To diagnose the 15% drop in retention for Uber, we need to first define what 'retention' means in this context. For Uber, retention can be measured by the frequency of rides taken by users within a sp...
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