I started with user context, like time of day and usage patterns, which felt right, but I rambled for a while before getting to any concrete product structure.
Start by clarifying the goal and scope of the suggestion system, then segment users and use cases to identify key needs. Propose a data-driven solution that balances personalization, context-awareness, and user control, and define success metrics to evaluate its impact.
Pro tip: Emphasize privacy and transparency as core design principles, and propose a feedback loop that lets users refine suggestions, showing you understand Google's user-first and data-responsible approach.
Ask clarifying questions to understand what 'app suggestion' means: is it for app discovery, re-engagement, or task completion? Define the target platform (Android) and success criteria.
Identify user segments (e.g., by demographics, usage patterns) and key contexts (time, location, activity) where suggestions are most valuable. Map user needs and pain points.
Propose a system that uses on-device signals (usage history, time, location) and optionally cloud data to generate personalized, context-aware suggestions. Include UI/UX considerations for how suggestions are presented.
Explain how user data is protected (e.g., on-device processing, anonymization) and give users control over what data is used and how suggestions appear.
Outline success metrics (e.g., suggestion acceptance rate, user engagement, retention) and a plan for A/B testing and continuous improvement based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.