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Establishment
Language of instruction
English
Teaching content
MARKETING
This course occurs in the following program(s)
IESEG Degree - Programme Grande École
- Crédits ECTS: 2.00
Training officer(s)
K.COUSSEMENT
Stakeholder(s)
Kristof COUSSEMENT
Présentation
Prerequisite
Students must have basic competencies in marketing
Goal
At the end of the course, the student should be able to:
- the student should be able to spot opportunities to transform customer data into actionable results.
- the student should be able to use customer data him- or herself to improve the customer relationships.
- the student should be able to spot opportunities to transform customer data into actionable results.
- the student should be able to use customer data him- or herself to improve the customer relationships.
Presentation
Nowadays, there is a tremendous increase in customer information which is available for the marketer. Indeed, companies are collecting different types of information from their customers like social media information, purchasing behaviour, complaining behaviour, socio-demographic information,... Consequently, knowing how to use this new information to improve customer relationships could be of high benefit for every marketer because better decisions could based upon that. This course tries to fulfil the gap by reaching students new ways to interact with customers on a one-to-one basis.
1. Introduction to Customer Intelligence
2. Understanding basic concepts and recognizing possible business applications
3. Explaining the predictive modelling approach: Sample, Explore, Modify, Model and Assess
4. Acknowledgment of the importance of data pre-processing
5. Introduction to the most popular predictive modelling applications
6. Understanding of the most popular evaluation metrics
1. Introduction to Customer Intelligence
2. Understanding basic concepts and recognizing possible business applications
3. Explaining the predictive modelling approach: Sample, Explore, Modify, Model and Assess
4. Acknowledgment of the importance of data pre-processing
5. Introduction to the most popular predictive modelling applications
6. Understanding of the most popular evaluation metrics
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours magistral | 16,00 | ||
| Travail personnel | |||
| Group Project | 16,00 | ||
| Autoformation | |||
| Recherche | 18,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
Details will be given in the first lecture.
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Autres | |||
| Projet Individuel | 10,00 | 1 | 40,00 |
| Rapport écrit | 5,00 | 1 | 20,00 |
| Contrôle continu | |||
| Participation | 16,00 | 1 | 20,00 |
| exposé | |||
| exposé | 0,00 | 1 | 20,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
Coussement K., Customer Intelligence: SAS Enterprise Miner Reference Manual version 0.3 (2011). -
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin. Database Marketing: Analyzing and Managing Customers. Springer (2008). -
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin. Database Marketing: Analyzing and Managing Customers. Springer (2008). -
Internet resources