Foundations of Customer Data Science

This unit introduces the nature, sources, and types of customer data and examines why that data is difficult to manage. Through technical overviews (databases, data ingestion), case studies (Caesars, Sephora), and an introduction to linguistic computing and generative AI, students will explore strategies for creating digital customer profiles, governing data, and preparing organizations for AI-enabled customer interactions.

Learning Objectives

• Analyze the characteristics, sources, and types of customer data (demographic, engagement, subjective/attitudinal), classifying each by structure, typical quality issues, and suitability for personalization
• Evaluate common causes of customer-data fragmentation and failure (identity resolution, siloed systems, privacy constraints) and recommend technical and human infrastructure or governance practices to mitigate those issues
• Apply data ingestion and structuring techniques to map raw customer inputs into database-ready formats and build digital customer profiles that support one-to-one engagement
• Demonstrate an understanding of linguistic computing and generative AI (e.g., GPTs), assessing their practical uses, limitations, ethical concerns, and implications for workforce and organizational readiness