Investigate Causes and Effects of Dynamic Pricing on ETAs
Overview
This question evaluates a data scientist's competency in diagnostic analytics, demand–supply and pricing analysis, causal inference, cost–benefit assessment, and experimental design within the Analytics & Experimentation domain.
Lyft
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
71
0
Lyft ETA Increase, Pricing Strategy, and Experiment Design
Context
- Lyft observes a 20% month-over-month increase in rider wait times (ETA). Assume ETA refers to the time from ride request to driver arrival, reported by median (p50) unless otherwise noted.
- Pricing has been static (fixed base + time/distance rates with no surge). The team suspects static pricing may be causing demand–supply imbalances.
- Goal: Diagnose the root causes, assess the costs/benefits of moving to dynamic pricing, and design an experiment to test whether dynamic pricing reduces ETAs without harming other key metrics.
Tasks
- Diagnose the 20% MoM ETA increase systematically.
- Assess the costs and benefits of switching from static to dynamic pricing.
- Design an experiment to test whether dynamic pricing reduces ETAs without negatively impacting other KPIs.
Hints
Consider rider/driver funnels, cohort analyses, geo/time splits, demand–supply interference, metric selection, and experiment-design choices.
Solution
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