Foundations of Customer Data Science

This unit introduces the nature, sources, and limitations of customer data and explains how data moves from paper and siloed records into databases and digital systems. Students study real-world customer-science infrastructures and emerging linguistic and generative AI tools to prepare for deeper analytics, activation, and ethical decision-making in subsequent units.

Learning Objectives

• Analyze and categorize customer data sources (demographic, engagement, subjective/attitudinal) and identify common quality, privacy, and integration issues for each source
• Evaluate the technical and organizational challenges of moving customer data into databases and digital platforms and propose prioritized solutions to improve data readiness
• Demonstrate the ability to map a basic customer data pipeline and the human/technical infrastructure components by synthesizing lessons from case studies (e.g., Caesars, Sephora)
• Apply principles of linguistic computing and generative AI to design a simple GPT-enabled customer interaction project concept and assess its potential benefits, risks, and ethical implications