I started with the customer angle and worked backwards, which felt right for Amazon.
Start by clarifying the goal and scope of the table-booking app, then segment target customers and identify their pain points. Propose a differentiated value proposition leveraging Amazon's strengths, outline a phased GTM plan, and sketch a long-term roadmap with clear success metrics.
Pro tip: Emphasize Amazon's flywheel and how the app can integrate with existing services (e.g., Amazon Pay, Prime) to create a seamless experience and drive adoption. Also, highlight the importance of solving the cold-start problem by focusing on a niche segment first.
Ask clarifying questions to understand the app's objective, target market, and constraints. Define what success looks like for Amazon.
Identify and prioritize target customer segments (e.g., diners, restaurants) based on needs, size, and strategic fit. Consider both B2C and B2B sides.
Articulate the unique value for each segment and how Amazon can differentiate from competitors (e.g., OpenTable, Resy) using its ecosystem.
Outline a phased launch plan: pilot in a select city, leverage Amazon's customer base and partnerships, and use incentives to solve the chicken-and-egg problem.
Define a roadmap with milestones (e.g., expansion, feature additions) and key success metrics (e.g., bookings, restaurant retention, customer satisfaction).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by framing the opportunity with Amazon's strengths (logistics, customer base, Prime) and the flower market's pain points (freshness, reliability). Then walk through a phased plan: MVP scoped to a narrow use case (e.g., everyday flowers in select cities), define success metrics tied to customer experience and unit economics, and position against incumbents by leveraging Amazon's speed and trust.
Pro tip: Emphasize the perishability challenge—flowers are not books—so highlight how you'd partner with local florists or build cold-chain capabilities incrementally, and tie success metrics to repeat purchase rate and delivery-on-time percentage, not just GMV.
Size the flower delivery market, identify key competitors (1-800-Flowers, local florists, startups like Bouqs), and map Amazon's unique advantages (logistics network, Prime membership, customer trust).
Propose a narrow MVP: e.g., same-day delivery of curated bouquets in 2-3 metro areas, integrated with Amazon Fresh or Prime Now, using local florist partnerships to ensure freshness and reduce upfront investment.
Define leading and lagging metrics: customer acquisition cost, delivery-on-time rate, freshness satisfaction (NPS), repeat purchase rate within 90 days, and unit economics (gross margin per order).
Discuss build vs. partner decisions (e.g., use existing Amazon logistics vs. third-party couriers), inventory management for perishables, and how to handle peak events (Valentine's Day, Mother's Day).
Outline launch tactics (Prime exclusive, cross-promotion with Amazon Fresh), pricing strategy, and criteria for scaling to new cities based on MVP learnings and unit economics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting of the three.
Start by framing the problem around Amazon's unique assets (shopper intent, retail data, and existing ad surfaces) and clarify the goal (e.g., a self-serve ad platform for third-party sellers). Then walk through a structured product and technical design: architecture, monetization, competitive differentiation, and a phased roadmap that ties back to Amazon's flywheel.
Pro tip: Anchor every decision in Amazon's leadership principles—especially 'Customer Obsession' and 'Invent and Simplify'—and explicitly connect ad revenue to improving the shopper experience (e.g., relevance, not clutter). This shows you understand Amazon's culture and long-term thinking.
Ask clarifying questions to define the platform's target users (e.g., third-party sellers vs. brands), primary ad formats (sponsored products, display), and success metrics (ROAS, ad revenue, shopper trust). State your assumptions explicitly.
Outline a high-level system architecture: ad serving, targeting (using retail signals like purchase history and search intent), bidding/auction mechanism, real-time measurement, and integration with existing Amazon systems (e.g., search, product pages). Emphasize scalability, latency, and data privacy.
Explain the revenue model (e.g., CPC, CPM, second-price auction) and how pricing balances advertiser ROI with shopper experience. Discuss budget controls, bidding strategies, and how to prevent ad fatigue or irrelevant ads.
Compare with Google Ads, Meta, and Walmart Connect—highlight Amazon's unique advantage in bottom-funnel intent data and closed-loop attribution. Identify gaps and how your platform would differentiate (e.g., retail-aware targeting, transparency).
Propose a long-term roadmap: MVP with core self-serve capabilities, then advanced features (automated bidding, cross-surface attribution, new formats like video). Tie each phase to measurable milestones and Amazon's strategic priorities (e.g., growing third-party seller success).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.