Code
MPYF MKT 6439
Level
M2
Field
Marketing, commercial
Language
Anglais/English
ECTS Credits
2
Class hours
28
Total student load
40
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
This course introduces students to different methods that are commonly used to make marketing decisions based on the collection and analysis of data. It will cover methods to segment consumers, conjoint analysis to design and price products, marketing mix models and controlled experiments to optimize the marketing mix. Tools from statistics and machine learning will be introduced in a practical way; the focus will be on their applications in business settings to make better decisions.
Learning objectives/Intended learning outcomes
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 6.2 - Optimise the use of tools adapted to different areas of management, and define and interpret relevant KPIs in order to measure and guarantee sustainable value creation for all stakeholders.
Rubrics
- Appliquer des méthodes d’analyse de données pour segmenter des consommateurs et représenter leurs préférences
- Analyser la valeur et le comportement des clients afin d’orienter les décisions marketing
- Évaluer l’impact et la performance des actions marketing à partir d’indicateurs et de résultats chiffrés.
- Concevoir des offres et actions marketing en s’appuyant sur des informations issues des données
- Interpréter les résultats d’analyses statistiques pour formuler des recommandations marketing argumentées
Content : structure and schedule
Day 1: Segmentation, perceptual mapping, and diffusion models
Day 2: Product analytics (conjoint analysis)
Day 3: Analytics for pricing and promotion decisions (marketing mix models, controlled experiments,)
Day 4: Customer analytics (Logistic Regression, RFM analysis, Customer Lifetime Value)
Sustainable Development Goals
ODD4 – Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.
ODD8 - Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all.
Ce cours contribue principalement à l'ODD 4 (Éducation de qualité) en formant les étudiants aux méthodes analytiques avancées et aux compétences en science des données appliquées au marketing, essentielles pour leur employabilité future. Il soutient également l'ODD 8 (Travail décent et croissance économique) en préparant les futurs professionnels à des emplois à forte valeur ajoutée dans l'analyse marketing data-driven et la prise de décision basée sur les données.
Number of SDG's addressed among the 17
2
Learning delivery
synchrone
Pedagogical methods
Lectures and class discussion on practical examples
Case studies
Hands-on applications on datasets
Evaluation and grading system and catch up exams
- Participation: 10%
- Short quizzes: 10%
- Group project: 30%
- Final exam: 50%
The catch-up exam will take the form of a 30-minute oral exam.
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 or by videoconference 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.
● No delay is tolerated. Attendance will be declared on Moodle 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
Palmatier, Petersen, Germann: Marketing Analytics Based on First Principles (2022)
Keywords
Data marketing, Customer analytics, Product analytics, Marketing mix models, Segmentation