I jumped straight into user segmentation and probably spent too long there.
Start by clarifying the goal and scope, then segment users and identify their pain points. Propose a recommendation product that leverages Meta's data and social graph, and define success metrics and potential risks.
Pro tip: Anchor your solution in a clear user problem and tie it to Meta's strategic priorities like increasing engagement and time spent. Show how your product creates a flywheel effect by using social signals to improve recommendations.
Ask clarifying questions to understand the goal, target users, and constraints. Define what success looks like for Meta (e.g., engagement, retention).
Identify key user segments (e.g., casual listeners, enthusiasts) and their unmet needs in podcast discovery. Highlight pain points like overwhelming choices or irrelevant recommendations.
Propose a product concept that solves the pain points, leveraging Meta's strengths (social graph, AI). Outline core features such as personalized recommendations, social sharing, and discovery mechanisms.
Prioritize features based on impact and feasibility. Define a minimum viable product (MVP) and a roadmap for iteration.
Define success metrics (e.g., DAU, time spent, retention) and potential risks (e.g., privacy concerns, content moderation). Discuss mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.