Start by clarifying the product vision and target user problem, then define the smallest set of features that deliver a testable solution to that problem. Prioritize features based on impact, effort, and risk, and validate with data or user feedback before building.
Pro tip: Frame your answer around learning goals—an MVP is an experiment to validate assumptions, not a mini version of the final product. Emphasize that you'd define success metrics upfront and be willing to pivot based on results.
Clearly articulate the user problem you're solving and the key assumption you want to test. This ensures the MVP is focused on learning, not just shipping features.
List all potential features, then ruthlessly cut to only those that directly enable the core user journey and test your hypothesis. If a feature doesn't contribute to learning, it's out.
Apply a prioritization framework like RICE (Reach, Impact, Confidence, Effort) or MoSCoW (Must-have, Should-have, Could-have, Won't-have) to rank features by value versus cost.
Socialize the proposed MVP with cross-functional teams and target users to gather feedback and ensure alignment. Adjust based on insights to avoid building the wrong thing.
Set clear, measurable goals for the MVP (e.g., activation rate, retention) and plan to iterate based on data. This turns the MVP into a continuous learning loop.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.