DomovSubjectsCustomer Data Analysis

Customer Data Analysis

Holder: Customer Data Analysis

Type of subject: Izbirni predmet

Number of credit points: 5

Subject code: 89

Subject description

Introduction to customer data analysis.
Customer life cycle and typologies of customer data.
Data sources for customer data analysis.
Databases and data warehouses of customer data.
Customer equity and customer lifetime value measurement.
Customer profiling:

RFM technique.
Factor analysis.
Cluster analysis.

7. Customer response modelling:

Regression.
Decision trees.
Neural networks.

8. Market basket analysis.

9. Special topics in customer data analysis:

Data mining and customer data analysis.
Web mining and customer data analysis.
Dealing with nominal data.
Dealing with large datasets.
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

Berry, Linoff: Data Mining Techniques for Marketing, Sales, and Customer Support. Wiley, 2011.
Hair, Tatham, Anderson, Black: Multivariate Data Analysis. Prentice Hall, 2005.
Kaplan: Structural equation modelling: foundations and extensions. Sage, 2008.
Middleton Hughes: Strategic Database Marketing. McGraw-Hill, 2005.
Ratner: Statistical Modelling and Analysis for Database Marketing. CRC Press, 2003.
Shmueli, Patel, Bruce: Data Mining for Business Intelligence. Wiley, 2008.