I went straight to revenue and purchase frequency, which felt obvious the second I said it out loud.
Start by clarifying the business goal and defining what 'high-value' means (e.g., revenue, lifetime value, engagement). Then outline a data-driven process: data collection, feature engineering, modeling, validation, and deployment, while emphasizing iteration and business impact.
Pro tip: At Amazon, tie your approach to leadership principles like Customer Obsession and Dive Deep. Show you can balance technical rigor with business pragmatism, and always consider scalability and cost.
Ask questions to understand what 'high-value' means for the business (e.g., revenue, retention, engagement) and how the results will be used.
Determine relevant data sources such as transaction logs, user behavior, demographics, and customer support interactions.
Choose a target metric (e.g., CLV, RFM) and engineer features that capture customer value, such as purchase frequency, recency, and monetary value.
Select appropriate models (e.g., clustering, regression, classification) and validate using techniques like cross-validation and A/B testing.
Deploy the model into production, monitor performance, and iterate based on feedback and changing business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.