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Regular version of the site

Database Marketing and Analytical CRM

2019/2020
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
2 year, 1, 2 module

Instructors

Course Syllabus

Abstract

In this course students will learn how customer data can be leveraged to aid decision making on customer acquisition, retention and generating higher revenues per customer. Students will do a lot of database manipulations and statistical modeling using the R language. A large number of data-based problems will be considered including but not limited to RFM-style segmentations, churn modeling and next-product-to- buy modeling. By building segmentation, scoring and lifetime value models students will learn to identify customers who are likely to churn, as well as those who are likely to generate the highest profits. Such analytics will lead to better segmentation, targeting and - as a result - more focused marketing actions.
Learning Objectives

Learning Objectives

  • Manipulate large customer datasets
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical analysis of customer data
  • Develop and apply new research methods by combining and modifying existing techniques
  • Solve CRM analytics problems using best practices of data analysis using modern computational tools
Expected Learning Outcomes

Expected Learning Outcomes

  • Manipulate large customer datasets
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical analysis of customer data
  • Solve CRM analytics problems using best practices of data analysis using modern computational tools
  • Develop and apply new research methods by combining and modifying existing techniques
Course Contents

Course Contents

  • Statistical segmentation. RFM Analysis. Hierarchical cluster analysis. Kmeans cluster analysis.
  • Managerial Segmentation.
  • Targeting and Scoring Models. Predictive Modeling.
  • Customer lifetime value. Transition matrix and probabilities.
  • Modeling Customer Lifetime Value with Linear Regression.
  • Logistic Regression for Churn Prevention.
  • Modeling Time to Reorder with Survival Analysis.
  • Customer Satisfaction. Net Promoter Score and Importance-Performance Analysis.
  • Cohort Analysis.
  • Attribution Modeling.
  • Exploratory analysis of transactional datasets using R. Descriptive analysis, database queries and feature engineering.
Assessment Elements

Assessment Elements

  • non-blocking Grade_Case
  • non-blocking Grade_Test
  • non-blocking Grade_Kahoot
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.25 * Exam + 0.25 * Grade_Case + 0.25 * Grade_Kahoot + 0.25 * Grade_Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737
  • Rao, U. H., & Nayak, U. (2017). Business Analytics Using R - A Practical Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1406793

Recommended Additional Bibliography

  • Beaujean, A. A. (2014). Latent Variable Modeling Using R : A Step-by-Step Guide. New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=777658
  • Kumar, V., & Petersen, J. A. (2012). Statistical Methods in Customer Relationship Management. Chichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=470590
  • Malthouse, E. C., & SAS Institute. (2013). Segmentation and Lifetime Value Models Using SAS. Cary, N.C.: SAS Institute. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=607170