Uber·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior

Jun 2026

Summary

PM interview at Uber focused on a product analytics and diagnosis problem around trip cancellations. Pretty standard product sense round but the question had enough depth to trip you up if you jumped straight to solutions.

Questions Asked (1)

Uber is seeing a spike in trip cancellations from riders. How would you investigate and address this?

Product Analytics & MetricsRoot Cause AnalysisProduct Sense & Ideation

Author's notes

My first instinct was to go straight into fixes, which was the wrong move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definition and segmenting the spike by rider, trip, and time dimensions to localize the problem. Then form hypotheses about root causes across the rider journey, prioritize them using data, and propose targeted solutions with success metrics.

Pro tip: Anchor your analysis in Uber's marketplace dynamics—cancellations affect driver utilization and rider wait times, so quantify the downstream impact to prioritize fixes. Also, consider both rider-initiated and driver-initiated cancellations, as they may have different root causes.

1. Clarify and Define the Metric

Ensure you understand what 'trip cancellations' means: is it rider-initiated cancellations after matching, or all cancellations? Define the numerator and denominator (e.g., cancellations per completed trips or per request).

2. Segment and Localize the Spike

Break down the spike by dimensions such as geography, time, rider cohort (new vs. existing), trip type (UberX, Pool), and device. Identify where the increase is concentrated to narrow down potential causes.

3. Generate and Prioritize Hypotheses

Brainstorm potential root causes across the rider journey: app issues, pricing changes, longer wait times, driver behavior, or external factors. Use data to validate or eliminate hypotheses, focusing on the most impactful ones.

4. Investigate Root Causes with Data

For top hypotheses, dig deeper with quantitative analysis (e.g., correlation with wait times, price changes) and qualitative methods (rider surveys, support tickets). Determine the primary drivers.

5. Propose Solutions and Measure Impact

Develop targeted solutions (e.g., improve ETAs, adjust pricing, enhance driver incentives) and define success metrics (e.g., cancellation rate reduction, rider retention). Prioritize based on impact and effort, and outline an A/B test plan.

Key Points to Mention

Segment the spike by rider type, geography, time, and trip characteristics to identify patterns.
Consider both rider and driver sides of the marketplace, as cancellations can be influenced by driver supply and behavior.
Analyze the rider journey: request, matching, pickup, and trip completion to pinpoint where cancellations occur.
Use quantitative data (e.g., wait times, pricing, app performance) and qualitative feedback (surveys, support tickets) to validate hypotheses.
Prioritize solutions based on impact on cancellation rate and overall marketplace health (e.g., driver utilization, rider wait times).
Define clear success metrics and propose an experiment (e.g., A/B test) to measure the effectiveness of the solution.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.