Product design question
How would you improve Google Home?
- Product design
- Easy
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What this question tests
Tests product improvement thinking for a smart home hub, focused on picking one real friction point in the multi-device, voice-first experience rather than a scattershot feature list.
How to approach it
- Clarify scope: improving Google Home the smart speaker/hub product and its app, covering voice assistant interactions and connected-device management.
- Identify the user and pain point: households with multiple smart devices (lights, thermostats, cameras) often struggle with unreliable voice command recognition for less common phrasing, and confusing multi-device/multi-room setup in the app.
- Prioritize the highest-friction moment: initial setup and troubleshooting when a device 'goes offline' or the assistant misunderstands a command, since this frustration is what drives smart home abandonment.
- Propose a solution: proactive device health monitoring that alerts users before a device fully drops offline (e.g., detecting weak WiFi signal to a smart plug) and a more forgiving voice command model that handles varied phrasing for the same intent (e.g., 'lights off' vs 'turn off the lights' vs 'kill the lights').
- Address a second pain point: routine creation (multi-step automations) is often too technical for average users; propose a simplified, natural-language routine builder where users describe what they want in plain language and the app configures it.
- Define success: reduction in support-related queries about offline devices, and increase in successful voice command rate (commands understood correctly on the first try).
What a strong answer includes
- Picks one high-friction moment (device connectivity issues and misunderstood commands) instead of listing many shallow improvement ideas.
- Proposes proactive health monitoring, addressing the problem before it becomes a frustrating failure rather than only reacting to it.
- Names a specific voice-recognition improvement (flexible phrasing for the same intent), a concrete and common real-world frustration with voice assistants.
- Defines success with a measurable command-success-rate metric, directly tied to the core weakness identified.
Common mistakes
- Suggesting generic hardware ideas (better speakers, more colors) instead of addressing software/experience friction.
- Not identifying a specific user pain point, defaulting to 'add more features.'
- No success metric, or one unrelated to the actual improvement proposed.
Likely follow-up questions
- How would you handle multiple household members with conflicting routine preferences?
- How would you measure voice recognition improvement without manual review of every command?
- How would this work for guests unfamiliar with the household's specific routines?
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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