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Google·Software Engineer·Onsite - Product Sense / Strategy·Intermediate

Intermediate
Apr 2026

Summary

Business analyst interview at Google with a product strategy question framed around Airbnb's restaurant expansion. One question, case-style, and it went about as well as you'd expect when you're not sure if they want a metrics framework or a full go-to-market plan.

Questions Asked (1)

Q1

How would you evaluate whether expanding Airbnb's restaurant product to a new region like Montreal is worth doing, and how would you test it before a full rollout?

A/B Testing & ExperimentationProduct StrategyGo-to-Market (GTM)
Author's notes

I spent too long on market sizing and not enough on what 'testing viability' actually means structurally.

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AI HintsAI Generated

Suggested Approach

Start by framing the evaluation around clear success metrics (e.g., incremental bookings, revenue, user retention) and the strategic fit for Montreal. Then propose a phased testing plan: begin with a small-scale pilot or A/B test in select neighborhoods, measure impact against a control, and use the results to decide on a full rollout. Emphasize data-driven decision-making and risk mitigation.

Pro tip: Show that you understand the difference between correlation and causation by suggesting a randomized controlled experiment (e.g., geo-based A/B test) and accounting for network effects, which are common in marketplace products like Airbnb.

1. Define success metrics and hypotheses

Identify key metrics such as incremental restaurant bookings, revenue, user engagement, and retention. Formulate a hypothesis about how the expansion will impact these metrics.

2. Assess market and strategic fit

Evaluate Montreal's market size, competition, regulatory environment, and alignment with Airbnb's overall strategy. Consider local partnerships and supply-side readiness.

3. Design a controlled experiment

Propose a geo-based A/B test or pilot in select areas of Montreal, with a control group in similar markets. Randomize at the user or neighborhood level to isolate the effect.

4. Measure and analyze results

Collect data over a sufficient period, analyze statistical significance, and check for cannibalization or network effects. Compare against pre-defined success thresholds.

5. Decide on full rollout

Based on results, recommend scaling, iterating, or abandoning. Outline a phased rollout plan if successful, including monitoring and iteration.

Key Points to Mention

  • Define clear, measurable success metrics (e.g., incremental bookings, revenue, retention).
  • Use a randomized controlled experiment (e.g., geo-based A/B test) to establish causality.
  • Consider local market factors: regulations, competition, cultural preferences, and supply.
  • Account for network effects and potential cannibalization of existing offerings.
  • Set a pre-determined decision rule (e.g., minimum detectable effect, statistical power).
  • Propose a phased rollout with monitoring and feedback loops.

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