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

Intermediate
May 2026

Summary

Interviewed for a bizops role at Google, got a strategy question about restaurant revenue that felt more like a consulting case than anything ops-related.

Questions Asked (1)

Q1

What advice would you give to a new restaurant trying to grow its revenue?

Product StrategyGo-to-Market (GTM)Pricing & Monetization
Author's notes

I went straight to the obvious stuff, foot traffic, upselling, delivery platforms, and I could tell midway through it wasn't landing as a structured answer.

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AI HintsAI Generated

Suggested Approach

Structure your answer by first clarifying the restaurant's context (type, location, target customers) and then applying a product-led growth framework. Focus on data-driven decisions, customer experience, and scalable strategies that mirror software engineering principles.

Pro tip: Emphasize experimentation and iteration: suggest running small, measurable tests (e.g., A/B testing menu prices or promotions) to validate ideas before full rollout, showing you think like an engineer.

1. Understand the Current State

Ask clarifying questions about the restaurant's concept, target market, current revenue streams, and pain points to tailor advice.

2. Identify Growth Levers

Analyze the customer journey and business model to pinpoint areas for improvement, such as menu optimization, pricing, marketing, or operations.

3. Prioritize High-Impact, Low-Cost Initiatives

Recommend quick wins like leveraging social media, implementing a loyalty program, or optimizing online ordering based on effort vs. impact.

4. Propose Scalable, Data-Driven Strategies

Suggest long-term growth tactics like dynamic pricing, delivery partnerships, or customer segmentation, emphasizing measurement and iteration.

5. Measure and Iterate

Outline how to track key metrics (e.g., average check size, customer retention) and use A/B testing to refine strategies continuously.

Key Points to Mention

  • Customer experience and retention (e.g., loyalty programs, personalized service)
  • Data-driven decision making (e.g., using POS data to identify best-sellers, peak hours)
  • Pricing strategies (e.g., psychological pricing, bundling, dynamic pricing)
  • Marketing and GTM (e.g., social media, local partnerships, targeted promotions)
  • Operational efficiency (e.g., streamlining menu, reducing waste, optimizing staffing)
  • Scalability and experimentation (e.g., A/B testing, pilot programs, tech adoption)

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