← Uber Eats Interview Insights

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

Senior
May 2026

Summary

PM interview at Uber Eats with a product metrics question about a new feature rollout. Pretty focused session, one meaty question that required thinking through what success actually means for a UI change like this.

Questions Asked (1)

Q1

Uber Eats recently launched a new promotional tab in the app. How would you go about measuring whether it was successful?

Product Analytics & MetricsA/B Testing & ExperimentationProduct Sense & Ideation
Author's notes

I jumped straight to engagement metrics and kind of forgot to anchor on what the tab was even supposed to do.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of the promotional tab (e.g., increase orders, retention, or new user acquisition) and the key metrics that align with that goal. Then outline a structured measurement plan that includes defining success metrics, running A/B tests, and analyzing both short-term and long-term impacts. Conclude with how you would iterate based on the findings.

Pro tip: Emphasize the importance of guardrail metrics (e.g., app performance, customer satisfaction) to ensure the promotion doesn't harm other parts of the business. Also, consider segmenting users to see if the tab works differently for new vs. existing users.

1. Clarify Objectives

Ask clarifying questions to understand the primary goal of the promotional tab (e.g., drive incremental orders, increase retention, promote new restaurants). This ensures your measurement aligns with business objectives.

2. Define Success Metrics

Identify key performance indicators (KPIs) such as click-through rate, conversion rate, average order value, and retention. Also, select guardrail metrics like app load time and customer satisfaction to monitor unintended consequences.

3. Design Experiment

Propose an A/B test where a control group sees the old interface and a treatment group sees the new promotional tab. Ensure proper randomization, sample size, and duration to detect meaningful effects.

4. Analyze Results

Compare metrics between control and treatment groups, checking for statistical significance. Segment results by user cohorts (new vs. existing, geography) to uncover heterogeneous effects.

5. Iterate and Decide

Based on the analysis, recommend whether to roll out, iterate, or kill the feature. Consider long-term impact through holdout groups or longitudinal studies.

Key Points to Mention

  • Define clear success metrics aligned with business goals (e.g., incremental orders, retention).
  • Use A/B testing to establish causality and measure incremental impact.
  • Include guardrail metrics to monitor potential negative side effects.
  • Segment analysis by user cohorts (new vs. existing, high-value vs. low-value).
  • Consider long-term effects and novelty bias by running extended tests or holdouts.
  • Tie results back to business impact (e.g., revenue, profit) and recommend next steps.

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