Diagnose Delivery Delays: Key Metrics and Experiment Design

Q: Diagnose Delivery Delays: Key Metrics and Experiment Design

This question evaluates a data scientist's diagnostic analytics and experiment-design competencies, including root-cause analysis, metric and segment specification, and causal inference using event-level timestamps, geolocation, ETA predictions, and operational supply-demand data.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Diagnosing Delivery Delays and Validating a Fix

Context

You are the PM for a consumer delivery marketplace that has seen an uptick in delivery delays. You have access to event-level timestamps (order placed, merchant accepts, courier assigned, courier arrives at merchant, food ready, pickup, drop-off), geolocation, ETA predictions, and operational data on courier supply and demand.

Task

Diagnose the root causes of delivery delays.
Specify the key metrics and segments you would analyze.
Design an experiment to validate one proposed solution to reduce delays.

Guidance

Consider a stage-level funnel from order to delivery.
Examine courier supply vs. demand imbalance, restaurant prep times, geography/time-of-day, and batching/dispatch logic.
Propose an A/B test using control vs. treatment regions.

Diagnose Delivery Delays: Key Metrics and Experiment Design

Diagnosing Delivery Delays and Validating a Fix

Context

Task

Guidance

Solution

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