BEHAVIOURAL INSIGHTS FROM BIG DATA MULTIMODAL ANALYSIS

This course aims to equip students with the ability to extract, integrate, and interpret behavioural insights from diverse data modalities, such as text, images, audio, and structured digital traces, to inform marketing decisions. Students will learn how to apply machine learning and data visualization techniques to real-world behavioural data, critically assess ethical implications, and communicate findings effectively through dashboards and storytelling. The course emphasizes hands-on analysis, critical thinking, and the integration of behavioural theory with advanced data techniques.

Prior knowledge on basic data manipulation, and data visualization. Basic proficiency in data-driven reasoning is essential.

By the end of the course, students will be able to identify and integrate multimodal data sources relevant to behavioural analysis in marketing contexts. They will be able to apply appropriate machine learning techniques to extract behavioural insights, evaluate the ethical implications of their analyses, and communicate results effectively using advanced data visualization and storytelling methods. Students will also develop the ability to critically assess dashboards and predictive models, design basic behavioural experiments, and formulate data-driven marketing strategies grounded in consumer behaviour evidence.

The course covers the collection, integration, and analysis of behavioural data from multiple modalities, including text, image, audio sources. Topics include multimodal data fusion, sentiment and emotion analysis, visual content analytics, behavioural clustering, and recommender systems. Emphasis is placed on ethical and legal considerations, such as GDPR and responsible AI use. The course also addresses advanced data visualization, storytelling techniques, and the design of behavioural experiments. Case studies and hands-on project work will guide students in applying these methods to real-world marketing problems.

All the material needed for the course will be available on the website of the course.

Most lectures will integrate theoretical foundations with practical exercises. Working in teams, students will develop proof-of-concept solutions in response to a group project challenge assigned by the instructors. These solutions will be presented in class, with the goal of strengthening both their understanding of AI algorithms and their communication skills.

Students will be evaluated based on two mandatory components: a mid term individual written exam (1/3) and a group project (2/3). Please note that the group project must be presented in person, and its completion is a strict requisite to obtain an evaluation. Students who are unable to attend lectures and non-attending students are required to complete the group project and sustain a written exam. Additionally, non-attending students are responsible for reviewing all of the material provided through the course webiste and studying the designated supplementary texts if any.

A thesis will be assigned (upon specific request to the instructor) to students who have average grade >27/30 and who demonstrate a serious and motivated interest in the course topics.