Analyze Causes of Increased Lyft Ride Wait Times

Q: Analyze Causes of Increased Lyft Ride Wait Times

This question evaluates a data scientist's abilities in root-cause analysis, causal inference, pricing strategy, and experimentation design applied to ride-hailing wait-time metrics.

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Question

Scenario

A ride-hailing marketplace observes a 20% month-over-month increase in rider wait time (time from request to driver arrival).

Tasks

Root-cause analysis: How would you structure an analysis to identify the drivers of the 20% wait-time increase?
Pricing assessment: Assuming pricing may be a contributing factor, how would you evaluate the costs and benefits of moving from static to dynamic pricing?
Experimentation: How would you design an experiment to validate whether dynamic pricing reduces ETA while controlling for demand–supply interference?

Clarifications and minimal assumptions

Wait time/ETA = time from rider request to driver pickup.
The marketplace is two-sided with riders (demand) and drivers (supply).
Static pricing = fixed per-minute/per-mile with occasional promos; dynamic pricing = price multipliers that respond to local demand–supply conditions.
Interference means that treating a subset (e.g., riders) can affect outcomes for others (e.g., by moving drivers), violating SUTVA if not addressed.

Analyze Causes of Increased Lyft Ride Wait Times

Scenario

Tasks

Clarifications and minimal assumptions

Solution

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