Metrics question
Users are having decision fatigue on the Uber Eats food delivery app. What kind of success metrics would you use to check the impact?
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What this question tests
Tests defining success metrics for a UX intervention targeting choice overload, requiring metrics that capture decision speed and satisfaction, not just order volume.
How to approach it
- Clarify the problem: too many restaurant or menu options cause users to abandon the app without ordering, or take excessively long to decide.
- Define a primary metric: time-to-order, from app open to checkout, for sessions where a fix like curated shortlists is applied versus a control.
- Define a completion metric: browse-to-order conversion rate, since decision fatigue often ends in app abandonment rather than a completed order.
- Define a satisfaction metric: a post-order survey on decision confidence, since reduced fatigue should improve subjective experience, not just speed.
- Define a guardrail: order value and restaurant diversity shouldn't collapse, since pushing everyone to the same recommended options could hurt smaller partners.
- Prioritize time-to-order and conversion as primary, with the diversity guardrail tracked to ensure the fix doesn't over-narrow the marketplace.
What a strong answer includes
- Names decision speed, time-to-order, as the most direct metric for decision fatigue specifically, not just a generic engagement number.
- Pairs a behavioral metric, conversion, with a subjective one, a decision confidence survey, capturing both outcome and experience.
- Adds a marketplace-health guardrail, restaurant diversity, recognizing a fix could over-optimize for speed at the cost of partner fairness.
- Explicitly connects each metric back to the stated problem, decision fatigue, rather than generic app metrics.
Common mistakes
- Measuring only total orders, which doesn't distinguish whether fatigue was actually reduced or just marginally patched.
- Ignoring the risk that reducing choice could unfairly concentrate orders on a few recommended restaurants.
Likely follow-up questions
- How would you A/B test a curated shortlist without hurting smaller restaurant partners' visibility?
- How would you measure decision fatigue directly, beyond just speed?
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- What is the most important metric for Uber Eats? Why?Uber · Metrics · Medium
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop