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Establishment
Language of instruction
English
Teaching content
MARKETING
This course occurs in the following program(s)
MSc in Digital Marketing & CRM
- Crédits ECTS: 2.00
Training officer(s)
K.COUSSEMENT
Stakeholder(s)
Dr. Kristof Coussement
Présentation
Prerequisite
The students should have followed 'Introduction to analytical Customer Relationship Management' course
Goal
At the end of the course, the student should be able to:
° spot complex problems to propose innovative solutions by transforming customer data using actionable predictive analysis.
° develop an expertise to use customer data him- or herself to improve the customer relationships through predictive modeling.
° manage successfully customer relationship.
° Breakdown complex organizational problems using the appropriate methodology (LO3.A)
° Propose creative solutions within an organization (LO3.B)
° Demonstrate an expertise on key concepts, techniques and trends in their professional field (LO7.A)
° Be a reference point for expertise-related questions and ambiguities (LO7.D)
° spot complex problems to propose innovative solutions by transforming customer data using actionable predictive analysis.
° develop an expertise to use customer data him- or herself to improve the customer relationships through predictive modeling.
° manage successfully customer relationship.
° Breakdown complex organizational problems using the appropriate methodology (LO3.A)
° Propose creative solutions within an organization (LO3.B)
° Demonstrate an expertise on key concepts, techniques and trends in their professional field (LO7.A)
° Be a reference point for expertise-related questions and ambiguities (LO7.D)
Presentation
This course introduces students to the basic principles of predictive analytics. This hands-on course introduces students how to use past information to predict future customer information.
A detailed overview of the course content is given below.
• Introduction to Predictive Analytics
• Understanding basic concepts and recognizing possible business applications
• Explaining the predictive modeling approach: Sample, Explore, Modify, Model and Assess
• Acknowledgment of the importance of data pre-processing
• Introduction to the most popular predictive modeling applications
• Understanding of the most popular evaluation metrics
A detailed overview of the course content is given below.
• Introduction to Predictive Analytics
• Understanding basic concepts and recognizing possible business applications
• Explaining the predictive modeling approach: Sample, Explore, Modify, Model and Assess
• Acknowledgment of the importance of data pre-processing
• Introduction to the most popular predictive modeling applications
• Understanding of the most popular evaluation metrics
Modalités
Organization
| Type | Amount of time | Comment | |
|---|---|---|---|
| Présentiel | |||
| Cours magistral | 16,00 | ||
| Travail personnel | |||
| Group Project | 15,00 | ||
| Charge de travail personnel indicative | 15,00 | ||
| Autoformation | |||
| Recherche | 4,00 | ||
| Overall student workload | 50,00 | ||
Evaluation
Details will be given in first lecture
| Control type | Duration | Amount | Weighting |
|---|---|---|---|
| Autres | |||
| Projet Collectif | 10,00 | 0 | 50,00 |
| Etude de cas | 10,00 | 0 | 50,00 |
| TOTAL | 100,00 | ||
Ressources
Bibliography
- Kristof Coussement, Koen W. De Bock, Scott A. Neslin. Advanced Database Marketing: Innovative Methodologies & Applications of Managing Customer Relationships. Gower (Ashgate) 2013. -
- 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). -