The course Analytical CRM deals with methods and techniques for analysis concerning the management and improval of customer relationships. Knowledge about customers is aggregated and used for enterprise decision problems like product line planning, customer loyality, etc. A necessary precondition for these analyses is the transformation of data stemming from operative systems into a common data warehouse that assembles all necessary information. This requires transformation of data models and processes for creating and managing a data warehouse, like ETL processes, data quality and monitoring. The generation of customer oriented and flexible reports for different business purposes is covered. The course finally treats several different statistical analysis methods like clustering, regression etc. that are necessary for generating important indicators (like customer lifetime value, customer segmenatation). As external data source, customer surveys are introduced.

Learning objectives:

The Student

• understands the principal scientific methods from statistics and informatics used in analytical CRM and their application to enterprise decision problems and independently applies these methods to standard cases,
• understands the components for creating and managing a data warehouse from operative system sources including the processes and steps involved and applies these methods to a simple example, and
• uses his knowledge to conduct a standard CRM analysis on enterprise data for a busines decision problem and deduces and justifies a recommendation for appropriate action.

Workload:

The total workload for this course is approximately 135 hours (4.5 credits):

Time of attendance

• Attending the lecture: 15 x 90min = 22h 30m
• Attending the exercise classes: 7 x 90min = 10h 30m
• Examination: 1h 00m

Self-study

• Preparation and wrap-up of the lecture: 15 x 180min = 45h 00m
• Preparing the exercises: 25h 00m
• Preparation of the examination: 31h 00m

Sum: 135h 00m

Exam:

Assessment consists of a written exam of 1 hour length following §4 (2), 1 of the examination regulation.

The exam is passed, if at least 50 out of 100 points are acquired in the written exam. In this case, all additional points (up to 5) from excersise work will be added.

Grade: Minimum points

• 1,0: 95
• 1,3: 90
• 1,7: 85
• 2,0: 80
• 2,3: 75
• 2,7: 70
• 3,0: 65
• 3,3: 60
• 3,7: 55
• 4,0: 50
• 5,0: 0

Ponnia, Paulraj. Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals. Wiley, New York, 2001.

Duda, Richard O. und Hart, Peter E. und Stork, David G. Pattern Classification. Wiley-Interscience, New York, 2. Ausgabe, 2001.

Maddala, G. S. Introduction to Econometrics. Wiley, Chichester, 3rd Ed., 2001.

Theil, H. Principles of Econometrics. Wiley, New York, 1971.