Base Knowledge
Not applicable
Teaching Methodologies
The online sessions will be based on interactive exhibition, case studies, tool demonstration and practical exercises. The evaluation is continuous, based on:
- Participation and contribution to synchronous sessions (20%)
- Applied individual work (report or project of practical application of AI in a marketing/sales context) (80%)
The final work will evaluate the ability to integrate concepts, tools and ethics into the business value proposition. Approval criteria: final grade ≥ 10 points.
Learning Results
This course aims to develop skills applied to the use of Artificial Intelligence (AI) as a strategic tool for value creation in Marketing and Sales. Students are expected to understand key AI concepts and technologies (machine learning, NLP, automation, and predictive analytics), recognize their impact on personalizing the customer experience, and know how to use AI solutions to optimize campaigns, customer segmentation, behavior prediction, and business processes. By the end, students should be able to integrate AI into their marketing and sales strategy in an ethical, sustainable, and results-oriented way.
Program
- Introduction to Artificial Intelligence and its Application to Marketing and Sales.
- Data and algorithms: Machine learning fundamentals applied to segmentation and prediction.
- Chatbots, automation, and personalization with generative AI.
- Predictive analytics and customer scoring.
- AI Tools in the Digital Marketing Cycle: CRM, Automation, and Recommendation.
- Ethics, privacy, and responsible use of AI.
- Case studies and strategic integration of AI in Marketing and Sales.
Curricular Unit Teachers
Professor a definir - ISCACGrading Methods
- - Individual and/or Group Work - 80.0%
- - Attendance and Participation - 20.0%
Internship(s)
NAO
Bibliography
- Kotler, P., Kartajaya, H. & Setiawan, I. (2021). Marketing 5.0: Technology for Humanity. Wiley.
- Davenport, T. & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review.
- Rust, R. T. (2020). The Future of Marketing. International Journal of Research in Marketing.
- Haenlein, M., Kaplan, A. et al. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review.
- Ferrão, M. (2023). Inteligência Artificial Aplicada ao Marketing. Edições Sílabo.
- Recursos digitais: Google AI, OpenAI, HubSpot AI Academy, Salesforce Trailhead AI Learning Path.