AI-Driven Customer Personalization

This unit examines how internal and third-party customer data are prepared, evaluated, and ethically used to drive personalization with contemporary AI methods. Students will learn data quality assessment, personalization model types (including generative approaches), contextualization, and operational tactics like automated message routing and agent support, building on foundational data concepts and preparing for deployment and evaluation in subsequent units.

Learning Objectives

• Analyze internal and third-party customer data sources and assess data quality using defined metrics to determine suitability for personalization
• Evaluate ethical, privacy, and regulatory implications of collecting and applying customer data and AI-driven personalization, and propose mitigation strategies
• Apply AI-based personalization approaches (e.g., collaborative, content-based, contextual, and generative models) to design targeted personalization strategies that account for context and customer type
• Demonstrate how personalization and automated routing improve customer experience by designing an end-to-end workflow and critiquing real-world examples and agent-support scenarios