Start by clarifying the business objective: reducing warehouse space by optimizing SKUs, and how a recommendation system on Prime can influence customer behavior to achieve that. Then, outline a systematic approach: define success metrics, design the recommendation logic, and consider implementation and iteration. Emphasize the trade-offs between space reduction and customer experience, and how you would measure and optimize.
Pro tip: Frame your answer around Amazon's leadership principles, especially Customer Obsession and Dive Deep, by showing how you balance customer needs with operational efficiency. Quantify the impact: estimate potential space savings and how it translates to cost reduction, and tie it back to Amazon's flywheel.
Confirm the objective: reduce warehouse space by optimizing SKUs, and the role of the Prime recommendation system. Ask clarifying questions about which SKUs are problematic (e.g., low turnover, bulky), and what constraints exist (e.g., customer satisfaction, Prime benefits).
Identify key metrics: warehouse space reduction (e.g., cubic feet saved), SKU rationalization (number of SKUs reduced), customer impact (e.g., conversion rate, customer satisfaction), and business impact (e.g., cost savings, Prime engagement).
Propose a system that surfaces similar products to customers, prioritizing alternatives to low-performing SKUs. Use collaborative filtering, content-based filtering, or hybrid approaches. Consider real-time personalization and A/B testing.
Discuss potential risks: cannibalization, customer dissatisfaction if preferred products are unavailable, and algorithmic bias. Mitigate by ensuring recommendations are relevant and by gradually phasing out SKUs.
Outline a rollout plan: pilot with a subset of SKUs, measure impact, and iterate. Use feedback loops to refine recommendations and adjust SKU optimization strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.