Customer Relationship Management and Direct Marketing (WiSe 2024/25)

Course schedule

Notice

Contact person: Nadine Eckel, M. Sc.

Lecturer: Dr. Claudio Felten

This course takes place in the second term of the winter semester.

Course grade: Group work (100%)
Please register at the examination office for the regular examination period.

Credit points: 6 ECTS (PO BWL 2019)

During the course, please communicate and stay updated via the course page on Learnweb. Announcements, lecture slides and any additional material will be published there.

The password for all lecture materials will be sent via Email. Email and telephone enquiries regarding the password will not be answered.

Description

Registration

The course is limited to a maximum of 30 participants. If more than 30 people want to attend the course, the course leaders will make a selection. Interested students have to upload a current CV, a short letter of motivation (max. 1 page long, stating why you are interested in the course and why it is a good match to your curriculum) and a transcript of records (documents can be in English or German) on the respective application website. The application period starts on October 1 and ends on October 29, 2024 (23:59). In case students have not performed any examinations during their master studies so far, they are invited to send their bachelor transcript. Students for whom the course is mandatory will be preferred. This applies in particular to master students in Information Systems with the selection of the Track Marketing, for whom up to 15 places are reserved.

Content and learning objectives

The module covers aspects for developing and designing value-adding relationships between customers and companies. Thereby, conceptual and methodical basics of customer relationship management (CRM/Customer Management) and direct marketing (DiMa) are presented. During the course students deal with current topics, concepts, and instruments of customer management and work on those in detail in a group assignment, which they present in front of the class and an expert panel. The participants receive a comprehensive overview of the planning, management, implementation, and controlling of customer relationship and direct marketing activities. In addition, the participants acquire knowledge, experience, and impulses in the three key competencies for successful CRM and DiMa: Expertise, statistics competence, and IT/data competence. The module consists of three teaching and learning formats (lectures, speed research, case study) and follows an interactive approach.

The following topics are, among others, covered in the course:

• Introduction, overview, basics, and methods of CRM and DiMa
• Concepts and tools of CRM and DiMa (customer experience management, journey mapping, lift, RFM, CLV, campaign control, personas, segmentation, CHAID etc.)
• Interaction of customer management and direct marketing
• Scope, management and controlling in CRM and DiMa

The aim of the course is to give students a profound and progressive understanding of customer relationship management and direct marketing. Thereby, it focuses on opportunities and challenges in data-driven companies.

Acquired skills

Professional skills:

• Students are able to evaluate customers using a variety of methods (customer lifetime value (CLV), recency, frequency, monetary value (RFM)).
• Students are able to plan and conduct direct marketing campaigns.
• Students learn how to handle data available in companies (legal, methodical, strategic).

Soft skills and key qualifications:

• Cooperation and collaboration: The tasks are conducted in group work.
• Presentation techniques: The tasks must be presented in front of the course.
• Communication skills: Fast capturing, processing, and preparing of content as well as the ad hoc presentation and discussion of it within the scope of the Speed ​​Research Day.
• Analytical skills: Understanding and deriving key insights as well as strategies from a Business Case.

Module Prerequisites

Students are recommended to have basic knowledge in the use of statistical methods, such as regression analysis. In addition, the ability to use a software for statistical analysis like Stata, SPSS, R or Python is beneficial. If the given recommendations are not met prior to the attendance of the course, they can be obtained during the semester via self-study (see recommended books).

Recommended readings:

• Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson (2006), Multivariate data analysis, Eight edition, Pearson new international edition. Upper Saddle River, N.J.: Pearson Education.
• Hayes, Andrew F. (2018), Introduction to mediation, moderation, and conditional process analysis. A regression-based approach. Methodology in the social sciences, Second edition. New York, London: The Guilford Press.

Students who can speak German may as well refer to Backhaus et al. (2018, 2015) instead of Hair et al. (2006).

• Backhaus, Klaus, Bernd Erichson, Wulff Plinke, and Rolf Weiber (2018), Multivariate Analysemethoden. Eine anwendungsorientierte Einführung, 15., überarbeitete und aktualisierte Auflage. Berlin, Heidelberg: Springer Gabler.
• ———, ———, and Rolf Weiber (2015), Fortgeschrittene Multivariate Analysemethoden. Eine anwendungsorientierte Einführung, 3., überarbeitete und aktualisierte Auflage. Berlin, Heidelberg: Springer Gabler.

Lecturers

• Dr. Claudio Felten (responsible)
• Nadine Eckel (accompanying)