Foundations of AI in Marketing

This unit establishes foundational knowledge of artificial intelligence, machine learning, natural language processing, and generative models as they apply to modern marketing practices. Students will explore data definitions and collection, core AI techniques, practical marketing use cases (e.g., customer service automation, segmentation, predictive analytics, personalization), and ethical and operational considerations needed to integrate AI into marketing workflows.

Learning Objectives

• Analyze the historical and technical foundations of AI (including machine learning, neural networks, NLP, and generative models such as GPT) and explain their relevance to marketing strategy and campaign design.
• Apply data concepts and analytic methods (data definition, selection of collection methods, predictive analytics, churn modeling, and A/B testing) to design a basic marketing measurement plan and a sample predictive segmentation approach.
• Demonstrate how conversational and generative AI can be used in marketing by creating use-case outlines for customer service automation, copilot workflows, localization/translation, and personalized messaging channels (email/SMS).
• Evaluate ethical, operational, and measurement considerations for integrating AI into marketing—assessing risks (bias, privacy), proposing mitigation strategies, and outlining steps for responsible implementation and performance tracking.