I spent too long trying to segment users and never really landed on a crisp problem to solve.
Start by clarifying the goal and scope of the in-store experience (e.g., increase customer satisfaction, loyalty, or basket size) and identify key user segments. Then, map the end-to-end customer journey to uncover pain points, prioritize opportunities based on impact and feasibility, and propose a testable solution with success metrics.
Pro tip: Acknowledge that Nubank is a digital-first company, so show how you'd blend physical and digital experiences (e.g., using the app to enhance in-store shopping) to create a seamless omnichannel journey. This demonstrates strategic thinking and adaptability to Nubank's context.
Ask clarifying questions to understand what 'improve' means (e.g., increase NPS, reduce checkout time, boost repeat visits) and which customer segments to focus on. This ensures alignment and prevents solving the wrong problem.
Walk through the in-store experience from arrival to exit, identifying key touchpoints (e.g., parking, navigation, product discovery, checkout) and pain points for different personas.
Use a framework like RICE or impact/effort to prioritize pain points based on potential impact on the goal and feasibility of implementation. Consider quick wins vs. long-term bets.
Suggest a specific improvement (e.g., app-enabled self-checkout, smart shopping lists, in-store navigation) and define success metrics (e.g., reduced checkout time, increased basket size, higher NPS).
Outline a plan to pilot the solution in select stores, gather feedback, and iterate. Emphasize data-driven decision-making and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.