Foundations of Customer Science

This unit establishes foundational understanding of customer data: its types, sources, and the technical and human challenges of managing it. Using real-world cases and an introduction to linguistic computing and generative AI, students will prepare to ingest, evaluate, and ethically apply customer data in later, more technical units.

Learning Objectives

• Analyze the nature and sources of customer data (demographic, engagement, subjective/attitudinal, and linguistic), distinguishing between structured and unstructured formats and identifying common quality issues and management challenges
• Demonstrate procedures for converting customer information into digital, database-ready formats, including basic data ingestion, cleaning, and integration techniques
• Evaluate technical and organizational infrastructures using case studies (e.g., Caesars and Sephora) to identify success factors, barriers to one-to-one personalization, and human roles required for effective customer science
• Apply introductory concepts of linguistic computing and generative AI to generate and classify customer insights while assessing ethical considerations, risks (e.g., privacy, bias, misuse), and change management strategies