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5.00 credits
30.0 h
Q2
Teacher(s)
Language
French
> English-friendly
> English-friendly
Prerequisites
/
Main themes
Marketing Analytics refers to the systematic use of data to understand, evaluate and improve marketing activities. This course therefore introduces students to analytical approaches that can be used to identify trends, predict behaviour and support strategic decision-making. The aim is to transform raw data (on customers and the market) into actionable insights, in order to design more personalised, effective and impactful marketing strategies, whilst enhancing customer value.
The course covers a range of quantitative methodologies used in descriptive, predictive and experimental marketing analysis. These include data mining techniques (clustering), statistical and econometric methods (discriminant analysis, principal component analysis, multidimensional scaling, ANOVA/GLM), as well as predictive modelling approaches (logistic regression). Students will learn to apply these methodologies to real-world datasets, interpret the results and translate them into concrete recommendations. The course emphasises both statistical rigour and managerial relevance within a marketing context.
The course covers a range of quantitative methodologies used in descriptive, predictive and experimental marketing analysis. These include data mining techniques (clustering), statistical and econometric methods (discriminant analysis, principal component analysis, multidimensional scaling, ANOVA/GLM), as well as predictive modelling approaches (logistic regression). Students will learn to apply these methodologies to real-world datasets, interpret the results and translate them into concrete recommendations. The course emphasises both statistical rigour and managerial relevance within a marketing context.
Learning outcomes
At the end of this learning unit, the student is able to : | |
| 1 | Competencies Given the « competencies referential » linked to the LSM Master 120 in Management and in Business Engineering, this course mainly develops the following competencies: Learning outcomes At the end of this class, students will be able: |
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:
* Reliability analysis of a measurement scale
* Discriminant analysis
More precisely, the main themes (which might slightly vary from one year to the next) are:
- Measurement tools in Marketing
* Reliability analysis of a measurement scale
- Customer segmentation
* Discriminant analysis
- Probability of belonging to a group
- Perceived similarities between brands (in the context of a brand image study)
- Experimentation in Marketing (design and data)
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 a data processing software oriented towards quantitative methods, 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.
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 a data processing software oriented towards quantitative methods, 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:
More information on the Virtual Exchange (group project report and individual report) will be provided during the first class session.
In case the student fails the course, only the "written exam" part can be improved (the grades linked to the group project and the individual report remain unchanged for all exam sessions of the current academic year). Also, the professors may transform the written exam in an oral exam if less than 4 students are registered to the exam.
In case the Virtual Exchange cannot be implemented, it will be replaced by a group project requesting a report linked to the group project and an individual report. In this case, the same integration method as the one described earlier will apply.
Note: In the group/team and individual work assigned in this course, information sources must systematically be cited, according to the academic references standards. In case the student has used a generative AI, s/he must systematically mention the parts of his/her work in which s/he used this tool, by adding a bottom page note indicating if the generative AI tool has been used and the purpose of this use (information search, text writing and/or text revising). The student remains responsible for the content of its production, independently of the references used. Thus, by submitting a team/group or individual work report for evaluation, the student asserts that: (i) it accurately reflects the phenomenon studied, and to do so, s/he must have verified the facts; (ii) s/he has respected all specific requirements of the work assigned to her/him, in particular requirements for transparency and documentation of the scientific approach implemented. If any of these assertions are not true, whether by intent or by negligence, the student has violated his/her commitment to truth with respect to the piece of knowledge produced in the context of his/her work, and possibly to other aspects of academic integrity, which constitutes academic misconduct and will be considered as such.
- a written exam (50% of the final grade - during the exam session - open questions) mixing theoretical methodological questions and deep thinking on a real issue linked to digital marketing (case on computer – with use of a statistical software) ; during the exam session
- their report on the group project (Virtual Exchange – US and Belgian students mixed groups - 40% of the final grade - in English - to be handed in before the exam session);
- their individual report (Virtual Exchange – 10% of the final grade - in English - to be handed in before the exam session)
More information on the Virtual Exchange (group project report and individual report) will be provided during the first class session.
In case the student fails the course, only the "written exam" part can be improved (the grades linked to the group project and the individual report remain unchanged for all exam sessions of the current academic year). Also, the professors may transform the written exam in an oral exam if less than 4 students are registered to the exam.
In case the Virtual Exchange cannot be implemented, it will be replaced by a group project requesting a report linked to the group project and an individual report. In this case, the same integration method as the one described earlier will apply.
Note: In the group/team and individual work assigned in this course, information sources must systematically be cited, according to the academic references standards. In case the student has used a generative AI, s/he must systematically mention the parts of his/her work in which s/he used this tool, by adding a bottom page note indicating if the generative AI tool has been used and the purpose of this use (information search, text writing and/or text revising). The student remains responsible for the content of its production, independently of the references used. Thus, by submitting a team/group or individual work report for evaluation, the student asserts that: (i) it accurately reflects the phenomenon studied, and to do so, s/he must have verified the facts; (ii) s/he has respected all specific requirements of the work assigned to her/him, in particular requirements for transparency and documentation of the scientific approach implemented. If any of these assertions are not true, whether by intent or by negligence, the student has violated his/her commitment to truth with respect to the piece of knowledge produced in the context of his/her work, and possibly to other aspects of academic integrity, which constitutes academic misconduct and will be considered as such.
Online resources
Moodle
Bibliography
Support de cours
Le matériel pédagogique, à disposition des étudiants sur Moodle, est composé de :
[1] D'ASTOUS A. (2019), Le Projet de Recherche en Marketing, 6ème Edition, Chenelière Education.
[2] DUCARROZ C., PONCIN I., JOLIBERT A. (2021). Chapitre 9 – Les analyses de variance univariée et multivariée. In Delacroix, E., Jolibert, A., Monnot, E., & Jourdan, P. (Eds.), Marketing Research (2nd Ed., pp 269-298). Dunod
[3] EVRARD Y., PRAS B. et ROUX E. (2009), Market: Fondements et Méthodes des Recherches en Marketing, 4ème Edition, Dunod, Paris.
[4] IACOBUCCI D., CHURCHILL G. (2018), Marketing Research: Methodological Foundations, 12th ed., South-Western.
[5] NUNAN D., BIRKS D. F., and MALHOTRA N.K. (2020), Marketing Research: Applied Insight, 6th Edition, Pearson Higher Education .
[6] PONCIN I., SINIGAGLIA N., JOLIBERT A., JOURDAN P. (2021). Chapitre 11 – L’analyse typologique et l’analyse discriminante. In Delacroix, E., Jolibert, A., Monnot, E., & Jourdan, P. (Eds.), Marketing Research (2nd Ed., pp 321-356). Dunod.
[7] VERNETTE E., FILSER M., et GIANNELLONI J-L. (2008), Etudes Marketing Appliquées, Dunod, Paris.
Le matériel pédagogique, à disposition des étudiants sur Moodle, est composé de :
- Slides (écrans Power Point)
- Etudes de cas
- Informations nécessaires au Virtual Exchange
[1] D'ASTOUS A. (2019), Le Projet de Recherche en Marketing, 6ème Edition, Chenelière Education.
[2] DUCARROZ C., PONCIN I., JOLIBERT A. (2021). Chapitre 9 – Les analyses de variance univariée et multivariée. In Delacroix, E., Jolibert, A., Monnot, E., & Jourdan, P. (Eds.), Marketing Research (2nd Ed., pp 269-298). Dunod
[3] EVRARD Y., PRAS B. et ROUX E. (2009), Market: Fondements et Méthodes des Recherches en Marketing, 4ème Edition, Dunod, Paris.
[4] IACOBUCCI D., CHURCHILL G. (2018), Marketing Research: Methodological Foundations, 12th ed., South-Western.
[5] NUNAN D., BIRKS D. F., and MALHOTRA N.K. (2020), Marketing Research: Applied Insight, 6th Edition, Pearson Higher Education .
[6] PONCIN I., SINIGAGLIA N., JOLIBERT A., JOURDAN P. (2021). Chapitre 11 – L’analyse typologique et l’analyse discriminante. In Delacroix, E., Jolibert, A., Monnot, E., & Jourdan, P. (Eds.), Marketing Research (2nd Ed., pp 321-356). Dunod.
[7] VERNETTE E., FILSER M., et GIANNELLONI J-L. (2008), Etudes Marketing Appliquées, Dunod, Paris.
Faculty or entity
