Metrics question

Rider experience for Uber Airport rides is down in the last 6 months. What would you do?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests root-cause diagnosis of a declining experience metric over a multi-month window, requiring segmentation before any fix is proposed.

Define 'rider experience' concretely: likely a blend of rating, wait time, and cancellation rate for airport pickups specifically.
Segment by airport, time of day, and flight status (delayed vs on-time), since airport pickups have unique supply and timing challenges.
Check for a supply-side cause: driver incentive changes, a new airport queuing or staging policy, or reduced driver density near airports.
Check for a demand-side cause: a rise in flight delays or airport traffic that outpaces the app's ETA accuracy.
Correlate the decline's start with any policy or product change over the 6-month window, such as a change to airport pickup zones.
Propose a fix targeted at the isolated cause, such as improving ETA accuracy using live flight data or adjusting driver staging incentives.

Breaks 'rider experience' into concrete sub-metrics (wait time, cancellation, rating) rather than treating it as one vague number.
Considers the airport-specific supply chain, such as driver staging lots and queuing rules, which behave very differently from street-hail pickups.
Looks for a specific change that coincides with the 6-month decline rather than assuming gradual, unexplained drift.
Proposes integrating live flight data to improve ETA accuracy, a concrete and airport-specific fix.

Treating airport rides like any other ride without accounting for their unique staging and queuing dynamics.
Proposing a broad platform-wide fix without first isolating what changed specifically for airport pickups.

Chapters of the AI PM course that teach what this question tests.