Code
MPYE MKT 6444
Level
M2
Field
Marketing, commercial
Language
Français/French
ECTS Credits
2
Class hours
28
Total student load
40
Program Manager(s)
Department
- Management, Marketing et Stratégie
Introduction to the module
This seminar trains students to activate customer data to design hyper-personalized experiences at scale. In a context where data lies at the heart of growth strategies, this course teaches how to leverage first-party and third-party data, activate audiences, and implement AI-driven personalization strategies. The seminar emphasizes practitioners’ insights into emerging practices.
Learning goals
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Learning objectives
- 1.2 - Use digital intelligence tools efficiently to support the societal, digital, energy and environmental transformations of organisations, ensuring their sustainable and responsible impact.
Course Learning objectives
• Comprendre l'architecture data marketing (CDP, DMP, CRM) et les flux de données
• Appliquer les principes de la réglementation sur les données personnelles dans la conception de stratégies d'activation et de personnalisation
• Maîtriser les stratégies de segmentation et d'activation des audiences
• Mettre en œuvre et évaluer la performance des campagnes de personnalisation client via l'IA
• Understand the marketing data architecture (CDP, DMP, CRM) and data flows.
• Apply the principles of personal data regulation when designing activation and personalization strategies.
• Master audience segmentation and activation strategies.
• Implement and evaluate the performance of AI-powered customer personalization campaigns.
Content : structure and schedule
• Marketing data architecture: CDP (Customer Data Platform), DMP, CRM, and data flow integration.
• Advanced segmentation: behavioral scoring, propensity modeling, RFM, and LTV.
• AI personalization: recommendations, dynamic content, and hyper-personalization.
• Performance measurement: attribution, ROAS, LTV/CAC, and dashboards.
Sustainable Development Goals
• ODD 9 – Industrie, innovation et infrastructure / Industry, Innovation and Infrastructure : développement des compétences en architecture data marketing (CDP, DMP, CRM) et en IA pour concevoir des infrastructures numériques innovantes et responsables.
• ODD 12 – Consommation et production responsables / Responsible Consumption and Production : mise en œuvre de stratégies de personnalisation et d’activation de la donnée client qui encouragent un marketing plus transparent, pertinent et durable, limitant le gaspillage publicitaire.
• SDG 9 – Industry, Innovation and Infrastructure: developing skills in marketing data architecture (CDP, DMP, CRM) and AI to design innovative and responsible digital infrastructures.
• SDG 12 – Responsible Consumption and Production: implementing customer data activation and personalization strategies that foster more transparent, relevant and sustainable marketing, reducing advertising waste.
Number of SDG's addressed among the 17
2
Learning delivery
synchrone
Pedagogical methods
1- Cours magistral
2- Exercices pratiques
3- Études de cas
4- Pitch
Evaluation and grading system and catch up exams
The assessment is based on several criteria: participation, engagement, course-related questions, applications, and a final pitch. The breakdown is as follows: 60% of the grade corresponds to individual work (In-class written exam with course-related questions, part of which may be in quiz format), and 40% to group work (practical application from the course, culminating in a final pitch).
The current academic regulations serve as the reference document.
If the final grade for a module is below 10 out of 20, a resit will be organized and will count for 100% of the final grade. The resit exam will consist of an individual research, reflection, and application paper, or an oral exam, on a topic covered in the course.
Any assignment submitted after the deadline will receive a grade of 0.
Grades may be adjusted individually based on participation, including unexcused lateness or absences, classroom behavior, etc., in the form of bonus or penalty points.
Punctuality and attendance are integral to the shared learning environment. They reflect respect for the instructors’ work, the group dynamic, the school’s standards, and the professional attitude expected of students.
Any unexcused absence for a half-day of class will result in a one-point deduction from the final grade of the relevant module. This penalty may also apply to any unexcused lateness. In cases of repeated lateness or absences, the number of points deducted may be increased to ensure the smooth running of the course.
Module Policies
The current academic regulations serve as the reference document.
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
1. Kihn, M., & O’Hara, C. B. (2020). Customer data platforms: Use people data to transform the future of marketing engagement. Hoboken, NJ: Wiley.
2. Maidenberg, E. (2024). Maîtriser l’IA générative dans la communication et le marketing: ChatGPT, Midjourney… Paris, France: Ellipses.
Keywords
Activation de la donnée client; Activation des audiences; Personnalisation pilotée par l’IA; CDP et architecture data marketing; Hyper‑personnalisation à grande échelle; Personnalisation conforme au RGPD; IA responsable & AI Act / Customer data activation; Audience activation; AI‑driven personalization; CDP‑based marketing data architecture; Hyper‑personalization at scale; GDPR‑compliant personalization; Responsible AI & EU AI Act.