Subject description
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Introduction to customer data analysis.
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Customer life cycle and typologies of customer data.
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Data sources for customer data analysis.
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Databases and data warehouses of customer data.
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Customer equity and customer lifetime value measurement.
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Customer profiling:
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RFM technique.
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Factor analysis.
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Cluster analysis.
7. Customer response modelling:
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Regression.
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Decision trees.
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Neural networks.
8. Market basket analysis.
9. Special topics in customer data analysis:
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Data mining and customer data analysis.
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Web mining and customer data analysis.
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Dealing with nominal data.
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Dealing with large datasets.
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Dealing with unbalanced datasets.
The subject is taught in programs
Objectives and competences
Main course objective:
Introduction to customer data analysis at the advanced level with the emphasis on issues, relevant for customer data analysis in practice.
Teaching and learning methods
This course is a combination of lectures, presentations, in-class and computer lab assignments as well as seminar discussions based on student individual seminar papers.
Expected study results
Learning outcomes:
- Thorough understanding of theoretical and practical aspects of customer data analysis.
- Thorough understanding of importance of customer data analysis in the framework of business analysis and planning.
- Student ability to carry out research work in the area of customer data analysis.
Basic sources and literature
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Berry, Linoff: Data Mining Techniques for Marketing, Sales, and Customer Support. Wiley, 2011.
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Hair, Tatham, Anderson, Black: Multivariate Data Analysis. Prentice Hall, 2005.
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Kaplan: Structural equation modelling: foundations and extensions. Sage, 2008.
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Middleton Hughes: Strategic Database Marketing. McGraw-Hill, 2005.
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Ratner: Statistical Modelling and Analysis for Database Marketing. CRC Press, 2003.
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Shmueli, Patel, Bruce: Data Mining for Business Intelligence. Wiley, 2008.