Uber Airport Imbalance: Why Pickups Trail Drop-offs and How the Product Should Respond

Quick Overview

An Uber analytical case: rides ending at an airport outnumber rides starting there. Candidates explain the gap by validating the data and ranking rider, driver and external causes, then propose product changes to raise airport pickups, backed by a success metric, guardrails and a test plan.

Uber Airport Imbalance: Why Pickups Trail Drop-offs and How the Product Should Respond

Company: Uber

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

A data point at Uber shows more rides ending at an airport than starting from it: drop-offs at the airport outnumber pickups there. Explain why this might be happening, and say what you would change in the Uber product to shift that balance. ### Clarifying Questions - Is this one airport or a pattern across many airports, and over what period was it measured? - Is the gap new, or has it always existed? - Is the aim to raise airport pickups directly, or first to establish whether the gap is a problem at all? ### Part 1 — Explain the gap List the most plausible reasons drop-offs exceed pickups, and explain how you would narrow them down. ```hint Check the number first Before explaining behavior, ask whether drop-offs and pickups are counted the same way, and what a balanced airport would even look like for Uber. ``` #### What This Part Should Cover - Checks that the data point is real and comparable before explaining it - Hypotheses on the rider side, the driver side and outside the product, such as other ways to leave the airport and airport rules - A way to rank the hypotheses, with the data that would confirm or rule out each ### Part 2 — Change the product Propose what you would build or change in Uber's product to increase pickups at the airport, and explain how you would judge whether it worked. ```hint Follow the arriving traveler Walk a traveler from the gate to a car and mark each point where Uber can lose them. ``` #### What This Part Should Cover - A target cause or traveler segment chosen from the diagnosis, with the reason - Product changes compared and prioritized with explicit criteria - A success metric with guardrails and a plan to test it ### What a Strong Answer Covers - A diagnosis that moves from validating the data to ranked causes before any fix is proposed - Fixes that follow from the diagnosed cause rather than generic incentives - Attention to both sides of the marketplace and to the airport's own rules and fees - Trade-offs such as margin, driver supply elsewhere in the city and rider experience ### Follow-up Questions - If paying drivers to wait at the airport closes the gap but lengthens waits in the city at the same hours, how do you decide? - How would your answer change if the gap shows up mostly for late-night arrivals or international flights? - What would you do if the airport caps how many ride-hailing cars may wait on its grounds?

Overview: An Uber analytical case: rides ending at an airport outnumber rides starting there. Candidates explain the gap by validating the data and ranking rider, driver and external causes, then propose product changes to raise airport pickups, backed by a success metric, guardrails and a test plan.

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Uber
Jan 16, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
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A data point at Uber shows more rides ending at an airport than starting from it: drop-offs at the airport outnumber pickups there. Explain why this might be happening, and say what you would change in the Uber product to shift that balance.

Clarifying Questions Guidance

  • Is this one airport or a pattern across many airports, and over what period was it measured?
  • Is the gap new, or has it always existed?
  • Is the aim to raise airport pickups directly, or first to establish whether the gap is a problem at all?

Part 1 — Explain the gap

List the most plausible reasons drop-offs exceed pickups, and explain how you would narrow them down.

What This Part Should Cover Guidance

  • Checks that the data point is real and comparable before explaining it
  • Hypotheses on the rider side, the driver side and outside the product, such as other ways to leave the airport and airport rules
  • A way to rank the hypotheses, with the data that would confirm or rule out each

Part 2 — Change the product

Propose what you would build or change in Uber's product to increase pickups at the airport, and explain how you would judge whether it worked.

What This Part Should Cover Guidance

  • A target cause or traveler segment chosen from the diagnosis, with the reason
  • Product changes compared and prioritized with explicit criteria
  • A success metric with guardrails and a plan to test it

What a Strong Answer Covers Guidance

  • A diagnosis that moves from validating the data to ranked causes before any fix is proposed
  • Fixes that follow from the diagnosed cause rather than generic incentives
  • Attention to both sides of the marketplace and to the airport's own rules and fees
  • Trade-offs such as margin, driver supply elsewhere in the city and rider experience

Follow-up Questions Guidance

  • If paying drivers to wait at the airport closes the gap but lengthens waits in the city at the same hours, how do you decide?
  • How would your answer change if the gap shows up mostly for late-night arrivals or international flights?
  • What would you do if the airport caps how many ride-hailing cars may wait on its grounds?