Teacher(s)

Ducarroz Caroline; Sinigaglia Nadia;

Language

French

Prerequisites

/

Main themes

This course does focus on advanced quantitative methods and models that can help face Marketing issues. Starting from these specific issues that a company/organization may face, a set of statistical/econometric methods and models are thoroughly presented. 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: 2. Knowledge and reasoning 3. Scientific and systematic approach 5. Work effectively in an international and multicultural environment 6. Teamwork and leadership 7. Project management Learning outcomes At the end of this class, students will be able: Identify the type of method/model that is adequate for a specific Marketing issue; Understand the mechanisms behind the methods; Model the issue; Master a wide range of advanced statistical/econometric methods and tools that can be used to collect and analyze primary and secondary data. Students will be able to apply each of the methods/models to a real case (by using a software specialized in quantitative methods, SAS Enterprise Guide), and then to interpret the results and formulate managerial recommendations to the company/organization. Work in an international setting, with US native speakers and practice online collaboration.

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.
More precisely, the main themes (which might slightly vary from one year to the next) are:

• Measurement tools in Marketing

* Principal Component Analysis
* Reliability analysis of a measurement scale

• Customer segmentation

* Clustering
* Discriminant analysis

• Probability of belonging to a group

* Logistic regression (modelling)

• Perceived similarities between brands (in the context of a brand image study)

* Multi-dimensional scaling techniques (MDS)

• Experimentation in Marketing (design and data)

           * ANOVA analysis (with moderation effects)

Teaching methods

Teaching methods and learning activities of this course are oriented toward: (i) learning the methodological rigor essential to use advanced statistical and econometric methods and models (rigorous knowledge and know-how); (ii) facing companies/organizations’ reality through real case studies; (iii) facing challenges related to online collaborating with students from another country (USA), in a common project (virtual exchange).
In concrete terms, sessions alternate lectures, discussions and case studies on real data linked to digital marketing. Each session is devoted to a specific model. From an issue 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 Enterprise Guide, analyzing results and making recommendations to the company). A last theoretical reminder is done
Our collaboration with the Appalachian State University (North Carolina, USA) will have the students, grouped in mixed Belgian-US groups, work on a common project (Virtual Exchange) around the methods and models covered in class, in the context of a market study. 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 Enterprise Guide software) ; during the exam session
• their report on the group project (Virtual Exchange – US and Belgian students mixed groups – in English);
• their individual report (Virtual Exchange – in English)

More information on the Virtual Exchange (group project report and individual report) as well as the distribution of the final grade across the evaluation components will be provided during the first class session.
In case a student fails the course, only the "written exam" part can be improved (the grade linked to the group project and individual report cannot be improved). 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
• Informations nécessaires au Virtual Exchange

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