mlsmm2135  2018-2019  Mons

5 credits
30.0 h
Q2
Teacher(s)
Ducarroz Caroline;
Language
French
Prerequisites
/
Main themes
This course does focus on quantitative and qualitative methods and models that can help face Marketing issues. A set of methods and tools used to collect and analyze primary and secondary data are thoroughly presented: verbal and non verbal methods; observation methods; experimental design; measurement scales; advanced statistical and econometrical methods.
This course will train students to identify the type of method/model that can help for a specific issue, to deeply understand mechanisms behind the methods, and to be able to apply each of the methods/models to a real case (by using a software specialized in quantitative methods), and then by interpreting the results and by formulating managerial recommendations to the company. The case studies are linked to digital marketing.
Aims

At the end of this learning unit, the student is able to :

1

Competencies

Given the « competencies referential » linked to the LSM Master in Sciences de Gestion et in Ingéniorat de Gestion, this course mainly develops the following competencies:

  • Master knowledge
  • Apply a scientific approach
  • Act in an international and multicultural context: understand how the company works 

Learning outcomes

At the end of this class, students will be able:

  • To analyze a (digital) marketing issue, and identify the relevant method/model to implement;
  • To model this issue;
  • To master a set of advanced methods and tools (mainly statistical and econometrical) for data collection and analyze of primary/secondary data.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
This course makes students thoroughly think about how to model issues linked to marketing, and more specifically to digital marketing.
The teaching methods makes students face the company reality, while transmitting rigorous knowledge and know-how, from a methodological point of view. Each session is devoted to a specific model. From an issue really faced by an existing company, a theoretical lecture is done and followed by an application on real data (case study and learning by problems), where students follow the process from A through Z (analyzing the issue, filtering useful information, choosing the method/model, analyzing data with SAS, analyzing results and making recommendations to the company). A last theoretical reminder is done.
More precisely, the main themes (which might slightly vary from one year to the next) are:
  • Experimentation in terms of design and data analysis through variance analysis;
  • Measurement scales dimensionality and reliability analysis, thanks to principal component analysis;
  • Customer segmentation, through classification (clustering) and discriminant analysis;
  • Probability of belonging to a group, through logistical regression;
  • Analysis through multi-dimensional scaling techniques;
  • Performance analysis and monitoring thanks to quantitative marketing models.
Teaching methods
The teaching methods are oriented around learning the methodological rigor essential to use advanced statistical and econometrical methods and models, and around facing the company reality.
Concretely, sessions alternate lectures, learning by problems, and case studies on real data linked to digital marketing, with the use of SAS software.
Furthermore, a collaboration with the Appalachian University (North Carolina, USA) will lead the students, grouped in mixed Belgian-US groups, to work on a common project. New technologies will help them communicate. Students are expected to be available to communicate with US students, also outside classical class schedules (given US time difference) and during Easter weeks.
Evaluation methods
Students are evaluated by :
- a written exam (during the exam session) mixing theoretical methodological questions and deep thinking on a real issue linked to digital marketing (case on computer - SAS software).
- their report on the group project (to hand in by the end of the quadrimester, thus before the exam session)
In case the student fails the course, only the "written exam" part can be improved (the grade linked to the group project cannot be improved).
More information on evaluation methods will be provided during the first class session.
The professor may transform the written exam in an oral exam if less than 4 students are registered to the exam.
Online resources
Moodle (Student Corner)
Bibliography
Support de cours
Le matériel pédagogique, à disposition des étudiants sur Moodle (Student Corner), est composé de :
  • Slides (écrans Power Point)
  • Etudes de cas
Références bibliographiques recommandées, lectures conseillées :
[1] CHURCHILL G., IACOBUCCI D. (2009), Marketing Research: Methodological
Foundations, 10th ed., South-Western.
[2] D'Astous A. (2015), Le Projet de Recherche en Marketing, 5ème Edition, Chenelière Education.
[3] Evrad Y., Pras B. et Roux E. (2009), Market: Fondements et Méthodes des Recherches en Marketing, 4ème Edition, Dunod, Paris.
[4] Jolibert A. et Jourdan P. (2011), Marketing Research: Méthodes de Recherche et d'Etudes en Marketing, Dunod, Paris.
[5] Malhotra N., Décaudin J-M., Bouguerra A. et Bories D. (2011), Etudes Marketing, 6ème Edition, Pearson Education France.
[6] Malhotra N.K., Birks D. F., and Wills P. (2012), Marketing Research: An Applied Approach, 4th Edition, Pearson Education Limited.
[7] Vernette E., Filser M., et Giannelloni J-L. (2008), Etudes Marketing Appliquées, Dunod, Paris.
Faculty or entity
CLSM


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Business Engineering

Master [120] in Management

Master [120] in Management

Master [120] in Business Engineering