I jumped straight to the restaurant owner's dashboard and order management flow, but the interviewer kept nudging me toward edge cases like menu updates mid-service or handling surge periods.
Start by clarifying the goal and scope: which market, what seller types, and what success metrics matter. Then segment sellers by needs and prioritize the most impactful pain points to design a UX that drives adoption and retention. Finally, outline the core flows and key features, and define how you'd measure success.
Pro tip: Anchor your design in a clear north-star metric like seller retention or order fulfillment rate, and show how each UX decision ladders up to it. This demonstrates strategic thinking beyond just features.
Ask questions to understand the market, seller types (e.g., restaurants, home chefs), and business objectives. Define what success looks like for the seller-side app.
Group sellers by size, cuisine, or technical savviness. Identify their key jobs-to-be-done and pain points in managing orders, menu, and analytics.
Use a framework like RICE or MoSCoW to prioritize features that address the most critical pain points. Define a minimum lovable product for initial launch.
Outline the main user flows (e.g., order management, menu editing, performance tracking) and establish UX principles like simplicity, speed, and clarity.
Set metrics such as order acceptance rate, time to onboard, and seller retention. Plan for feedback loops and iterative improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the goal and constraints of the MVP, then define the core user problem and the smallest set of features that deliver value. Prioritize ruthlessly using a framework like RICE or MoSCoW, and explicitly state what you would cut and why, tying cuts back to learning goals and speed to market.
Pro tip: Frame cuts as deliberate experiments: 'We're cutting X to validate Y faster; if Z metric moves, we'll invest.' This shows you understand MVP as a learning vehicle, not a half-built product.
Ask about the target market, business model, timeline, and team size to ground your MVP in reality. State your assumptions explicitly.
Identify the single most important job-to-be-done for the initial user segment (e.g., ordering food quickly from nearby restaurants).
List only the features required to solve that core problem end-to-end: restaurant discovery, menu browsing, order placement, payment, and delivery tracking.
Explicitly remove features that don't directly enable the core loop, such as ratings, reviews, loyalty programs, multiple payment options, and advanced search filters.
Specify what you'll measure (e.g., order completion rate, time to first order) and how you'll use learnings to decide next investments.
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Blanked for a second on how to structure this without just listing metrics randomly.
Start by clarifying the app's specific goals and target market, then define success using a balanced set of metrics across the user journey (acquisition, engagement, retention, monetization, and satisfaction). Emphasize how you would measure these metrics, set targets, and use experimentation to validate improvements.
Pro tip: Tie metrics to the company's north star and show how you'd prioritize them using frameworks like HEART or AARRR, while acknowledging trade-offs between growth and profitability.
Ask clarifying questions to understand the app's business model, target users, and strategic priorities (e.g., growth vs. profitability).
Identify key metrics across the user lifecycle: acquisition (CAC, installs), engagement (DAU/MAU, order frequency), retention (churn, repeat rate), monetization (AOV, take rate), and satisfaction (NPS, CSAT).
Select a north star metric and supporting metrics, then set realistic targets based on benchmarks and business goals.
Outline how you'll track metrics (analytics tools, dashboards), run A/B tests, and attribute causality.
Describe how you'll use data to inform product decisions, iterate, and communicate progress to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope—which app, product, and user segment—then walk through the flow from discovery to post-purchase, highlighting key user goals, friction points, and metrics at each stage. Emphasize how each step drives conversion and retention, and tie back to Meta's focus on seamless experiences.
Pro tip: Don't just list steps; narrate the emotional journey and call out where users drop off, then suggest one high-impact improvement. This shows you think like a PM who owns outcomes, not just features.
Ask clarifying questions to define the app, product type, user persona, and platform (mobile/web). State your assumptions explicitly to set a shared context.
Outline the end-to-end flow in major stages: discovery, evaluation, cart, checkout, payment, confirmation, and post-purchase. Keep it linear and logical.
For each stage, describe what the user does, what they see, and how the system responds. Highlight key decision points and potential friction.
Call out success metrics (e.g., conversion rate, time to checkout) and common drop-off points. Suggest one or two improvements to optimize the flow.
Recap the flow, emphasize how it aligns with business goals (e.g., increasing GMV, reducing churn), and invite feedback or next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one got more technical than I expected for a PM round.
Start by clarifying the user problem and product goals, then outline the key components of a geo-based search system, and finally discuss trade-offs and success metrics. Focus on how the feature delivers value to users and the business, while addressing technical feasibility at a high level.
Pro tip: Emphasize the importance of location data accuracy and privacy, and propose a phased approach starting with a simple MVP to validate user needs before scaling. This shows product sense and technical pragmatism.
Ask clarifying questions to understand the user, use case, and business objectives. Define what success looks like (e.g., increased engagement, restaurant partnerships).
Break down the feature into core components: location acquisition, data storage and indexing, search algorithm, ranking, and user interface. Consider both client and server sides.
Outline how to store and query geospatial data (e.g., using geohashing, quadtrees, or PostGIS). Discuss how to handle real-time updates, scalability, and latency.
Discuss trade-offs between accuracy, speed, and cost. Consider edge cases like sparse areas, privacy concerns, and offline usage.
Propose success metrics (e.g., click-through rate, time to find a restaurant) and a plan for iterating based on user feedback and data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.