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

Senior
Jul 2026

Summary

Airbnb PM interview with a product metrics question focused on a business travel feature. Pretty standard format but the question had some depth to it once you started pulling on the threads.

Questions Asked (1)

Q1

Airbnb launched a feature aimed at growing its business travel segment. How would you measure whether it's working?

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

I went straight to bookings volume and kind of had to walk myself back from that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the feature's goal and the business travel segment's unique needs, then define a metric hierarchy from top-line business outcomes to granular engagement metrics. Propose a measurement plan that includes both leading and lagging indicators, and suggest an A/B test or quasi-experimental design to isolate the feature's impact.

Pro tip: Emphasize that business travel success isn't just about bookings—it's about repeat usage and policy compliance, so include metrics like repeat booking rate and expense integration. Also, mention the importance of segmenting by traveler type (e.g., solo vs. team) to avoid misleading averages.

1. Clarify the feature and its goal

Ask clarifying questions to understand what the feature is (e.g., business travel portal, expense integration) and its intended outcome (e.g., increase business travel bookings, attract corporate accounts).

2. Define success metrics

Identify a north-star metric (e.g., business travel bookings) and supporting metrics across the funnel: awareness, adoption, engagement, retention, and satisfaction. Include both quantitative and qualitative measures.

3. Design measurement approach

Propose an A/B test or holdout group to measure causal impact. If randomization isn't possible, suggest quasi-experimental methods like difference-in-differences or propensity score matching.

4. Analyze and segment

Break down results by traveler type, company size, region, and other relevant dimensions to uncover heterogeneous effects and actionable insights.

5. Iterate and decide

Based on results, recommend whether to double down, iterate, or kill the feature. Define thresholds for success and next steps for optimization.

Key Points to Mention

  • North-star metric: business travel bookings or revenue from business travelers
  • Funnel metrics: feature adoption rate, booking conversion, repeat booking rate
  • Retention and loyalty: repeat usage, corporate account retention, Net Promoter Score (NPS)
  • A/B testing or holdout groups to establish causality
  • Segmentation by traveler role, company size, and trip purpose
  • Guardrail metrics: impact on consumer bookings, customer support tickets, or host satisfaction

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