CUSTOMER INSIGHT IN THE AI ERA
Instructional goals
The course aims to provide students with the conceptual and methodological tools to understand and govern the impact of artificial intelligence on consumer behavior analysis. The objective is to develop the ability to integrate data from heterogeneous sources through AI-based technologies, in order to generate strategic insights that support effective, ethical and evidence-based marketing decisions.
Intended learning outcomes
By the end of the course, students will be able to:
- Describe the evolution of AI applied to marketing.
- Design an AI-based consumer insight process, choosing appropriate data sources, analytical tools consistent with the objectives, and adequate evaluation metrics.
- Critically evaluate the quality of insights produced by AI systems, recognizing their biases, methodological limits and ethical implications.
- Effectively present generated insights to an audience of marketing decision-makers, translating analytical output into strategic recommendations.
- Autonomously update their competencies in a rapidly evolving domain, critically evaluating new technologies and new methodological approaches.
Course Contents
The course is structured in four thematic blocks. The first block introduces the conceptual foundations of AI in marketing and the data sources relevant for insight generation. The second block addresses the main applied areas of AI for consumer insight: social media intelligence, customer journey, personalization, and predictive analytics. The third block is dedicated to the use of generative AI in marketing research. The fourth block explores the strategic applications of AI (content and branding, conversational AI, virtual influencers, immersive experiences).
Reference Books
Suriano, Di Domenica, Bertino. "GENERATIVE AI E MARKETING". 2025. McGraw Hill
"Encyclopedia of Artificial Intelligence in Marketing" Edited by Riadh Ladhari. 2025. Springer
Teaching Methods
Frontal lectures
Analysis and discussion of case studies
Experiential workshops with AI tools
Group project work
Assessment Method
Compliant students
1/3 group project
2/3 final written exam
Non-Compliant students
100% written exam
Thesis assignment criteria
none in particular
Week 1
Introduction: AI, Generative AI and Consumer Behavior
Reference reading:
• Generative AI e Marketing, chapters 1-2.
• Encyclopedia of AI in Marketing: Reconceptualizing Marketing Competencies for the AI Era (García-Madurga, Grilló-Méndez); Consumer Acceptance of AI in Marketing (Monaco, Sacchi); The AI Paradox (Gani, Ariffin, Rafi).
Week 2
Data, Sources and Taxonomy of AI Uses for Consumer Insight
Reference reading:
• Generative AI e Marketing, chapter 5.
• Encyclopedia of AI in Marketing: Predictive Analytics for Consumer Behavior Insights in Marketing (Taherdoost et al)
Week 3
Social Media Intelligence and Consumer Language Analysis
Reference reading:
• Encyclopedia of AI in Marketing: AI Empowered Social Media Marketing (Farivar, Wang, Yuan)
Week 4
Customer Journey and Customer Experience in the AI Era
Reference reading:
• Encyclopedia of AI in Marketing: Enhancing Customer Journey Through AI (Chan, Colapinto, Soffiato)
Week 5
Personalization, Predictive Analytics and Marketing Decision-Making
Reference reading:
• Encyclopedia of AI in Marketing: Role of Artificial Intelligence in Marketing Decision-Making for Customer Personalization (Abrokwah-Larbi); Metrics for Evaluating Personalized Marketing Systems (Malthouse)
Week 6
AI and Marketing Research: the Silicon Sample
Sarstedt, M., Adler, S. J., Rau, L., & Schmitt, B. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology & Marketing, 41(6), 1254-1270.
Week 7
Generative AI for Content, Brand and Communication
Reference reading:
• Generative AI e Marketing, chapters 6 and 9.
• Encyclopedia of AI in Marketing: Generative AI for Content Marketing (Wahid); Branding in the Age of AI (Tiwari, Jain); Rethinking Brand Orientation in the Age of Artificial Intelligence (Vernuccio, Patrizi, Boccalini); AI and Creativity (Pagani, Wind)
Week 8
Conversational and Agentic AI: the Dialogue with the Consumer
• Encyclopedia of AI in Marketing: Agentic and Generative AI in Marketing and Sales (Puri, Pandey, Saha)
Week 9
Virtual Influencers and New Forms of Endorsement
Reference reading:
• Encyclopedia of AI in Marketing: Virtual Influencer Marketing (Ham, Eastin); AI-Generated Influencers (Yu); AI-Generated Spokesperson (Yim et al.)
Week 10
Immersive Experiences: AR, VR, and Intelligent Retail
• Generative AI e Marketing, chapter 10.
• Encyclopedia of AI in Marketing: How AI and Augmented Reality Are Transforming Digital Shopping in E-commerce? (Lemoine, Msakni)
Week 11
Ethics, Regulation and Responsibility in AI Marketing
• Generative AI e Marketing, chapters 3-4.
• Encyclopedia of AI in Marketing: Corporate Digital Responsibility (CDR) in AI-Driven Marketing (Kunz, Wirtz)
Week 12
Group Project Presentations