Evaluate Optimal Jogging Routes Feature with A/B Testing

Q: Evaluate Optimal Jogging Routes Feature with A/B Testing

This question evaluates proficiency in experimental design, product analytics, metric definition, randomization strategy, guardrail selection, power/MDE calculations, and event instrumentation for a mapping/navigation feature recommending jogging routes.

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Question

Evaluate and Experiment: Jogging Route Recommendations in Google Maps

Context

Google Maps is considering a feature that recommends optimal jogging routes (e.g., safe, scenic, appropriate distance/elevation) when a user indicates intent to go for a run.

Task

Design how you would:

Assess whether the idea is valuable for users and for the business.
Launch an experiment to validate impact, including:
- Primary and secondary success metrics
- Triggering/eligibility logic
- Unit of randomization
- Guardrail metrics
- Minimum Detectable Effect (MDE) and power assumptions
- How to log/track recommendation events

Hints

Define a primary KPI (e.g., completed jogs) and secondary metrics (adoption, satisfaction, safety proxies, engagement).
Specify how a “trigger” is defined and when a user is exposed.
Choose a randomization unit that minimizes contamination.
Provide MDE math with concrete numeric assumptions.
Outline the event schema for impressions, accepts, starts, and completes.

Evaluate Optimal Jogging Routes Feature with A/B Testing

Evaluate and Experiment: Jogging Route Recommendations in Google Maps

Context

Task

Hints

Solution

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Evaluate Optimal Jogging Routes Feature with A/B Testing

Overview

Evaluate and Experiment: Jogging Route Recommendations in Google Maps

Context

Task

Hints

Solution

Comments (0)