Ride-Hailing Launch Scorecard: Supply, Demand and Liquidity Metrics for Uber in a New City
Company: Swiggy
Role: Product Manager
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Other
Uber has just launched in a new city, and you are the admin responsible for running Uber there. Which metrics would you treat as the key ones for judging whether the launch is succeeding?
```hint Start from both sides of the market
A new city opens with few drivers and few riders. Ask what has to be true for each side, and for matching them, before revenue becomes the right yardstick.
```
### Clarifying Questions
- Over what horizon are we judging the launch: the first few weeks or the first year?
- Which products went live: standard rides only, or also premium tiers or delivery?
- Is Uber the first ride-hailing service in this city, or is it entering against an established competitor?
- Is the near-term goal rapid growth or reaching profitability in the city?
### What a Strong Answer Covers
- A definition of success for a new city that shifts with the stage of the launch
- Driver-side, rider-side and matching metrics, and how they depend on each other
- A north-star metric with guardrails for reliability, safety and driver earnings
- Unit economics and how much growth depends on launch incentives
- Cutting metrics by neighborhood and time of day, and reading combinations of metrics to decide what to do
### Follow-up Questions
- Trips are growing quickly, but most new riders arrived through launch discounts. How would you tell whether the growth will last?
- Pickup times are good downtown but poor in the suburbs. Which metrics expose this, and what would you do about it?
- If you could put only three metrics in a weekly update to leadership, which would you choose and why?
Overview: A product metrics question asking which measures a city-level admin should use to judge whether Uber's launch in a new city is succeeding. It tests reasoning about a two-sided marketplace across driver supply, rider demand, matching reliability, launch-stage priorities, incentive dependence and unit economics.