Product design question
List 5 enterprise software that are well done and has market potential. Explain why you chose them. Then pick one and define the KPI for its success. Define a North Star metric for this product. Given the North Star, define the roadmap. What features do you have to pay attention to for the specific case you picked?
- Product design
- Hard
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What this question tests
Broad enterprise strategy and metrics synthesis, testing whether the candidate can pick a defensible example and go deep with a coherent metric framework.
How to approach it
- Name five real, well known enterprise software examples briefly, such as Salesforce, Slack, Snowflake, Datadog, and Figma, explaining briefly why each is well regarded.
- Pick one, for example Datadog, and explain its market potential: growing demand for observability as companies run more complex, distributed infrastructure.
- Define the KPI for its success: net revenue retention and active hosts or services monitored per customer account.
- Define a North Star metric: something like weekly active engineers taking action (viewing dashboards, resolving alerts) per customer, since usage depth predicts renewal.
- Given the North Star, sketch a roadmap: deepen integrations with more cloud and infrastructure tools, expand from monitoring into incident response workflows, and add AI-assisted root cause analysis.
- Name features to focus on: alert accuracy and reducing false positives, since alert fatigue is the top driver of churn for observability tools.
What a strong answer includes
- Picks a defensible, specific example, Datadog, and explains its market potential with a real, current driver, infrastructure complexity growth.
- Chooses a North Star tied to genuine usage depth (active engineers taking action) rather than a vanity metric like total logins.
- Names alert fatigue and false positive rate as the specific feature risk to manage, a well known real pain point for this category of product.
Common mistakes
- Listing five products with no real reasoning for why each was chosen.
- Picking a North Star metric disconnected from what actually predicts retention for the chosen product.
Likely follow-up questions
- Why did you pick this North Star metric over total revenue or logins?
- What would make a customer churn despite hitting your North Star metric?
More product design questions
- Design a TV for a car.Google · Product design · Hard
- Design a refrigerator for the blind.Google · Product design · Hard
- How do you design a camera for the elderly?Google · Product design · Medium
- Redesign Twitter for eCommerce.Shopify · Product design · Hard
- How would you go about designing a new location sharing app for Google?Google · Product design · Medium
- How would you improve Google Home?Google · Product design · Easy
More questions from Google
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop