Modul
Data Science: Advanced CRM [M-WIWI-101470]
Credits
9Recurrence
Jedes SemesterDuration
1 SemesterLanguage
GermanLevel
4Version
6Responsible
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
Bricks
Identifier | Name | LP |
---|---|---|
T-WIWI-111219 | Artificial Intelligence in Service Systems - Applications in Computer Vision | 4.5 |
T-WIWI-102762 | Business Dynamics | 4.5 |
T-WIWI-102848 | Personalization and Services | 4.5 |
T-WIWI-103549 | Intelligent CRM Architectures | 4.5 |
T-WIWI-105778 | Service Analytics A | 4.5 |
T-WIWI-102847 | Recommender Systems | 4.5 |
T-WIWI-111267 | Intelligent Agent Architectures | 4.5 |
T-WIWI-109921 | Advanced Machine Learning | 4.5 |
T-WIWI-110915 | Intelligent Agents and Decision Theory | 4.5 |
Competence Certificate
The assessment is carried out as partial exams (according to Section 4(2), 1 or 2 of the examination regulation) of the single courses of this module, whose sum of credits must meet the minimum requirement of credits of this module. The assessment procedures are described for each course of the module seperately.
The overall grade of the module is the average of the grades for each course weighted by the credits and truncated after the first decimal.
Competence Goal
The student
- understand service competition as a sustainable competitive strategy and understand the effects of service competition on the design of markets, products, processes and services,
- models, analyzes and optimizes the structure and dynamics of complex business applications,
- develops and realizes personalized services, especially in the field of recommendation services,
- analyzes social networks and knows their application field in CRM,
- works in teams.
Prerequisites
None
Content
Building on the basics of CRM from the Bachelor's degree program, the module "Data Science: Advanced CRM" is focusing on the use of information technology and its related economic issues in the CRM environment.The course "Intelligent CRM Architectures" deals with the design of modern intelligent systems. The focus is on the software architecture and design patterns that are relevant to learning systems. It also covers important aspects of machine learning that complete the picture of an intelligent system. Examples of presented systems are "Taste Map"-architectures, "Counting Services", as well as architectures of "Business Games".The impact of management decisions in complex systems are considered in the course "Business dynamics". The understanding, modeling and simulation of complex systems allows the analysis, the goal-oriented design and the optimization of markets, business processes and regulations throughout the company.Specific problems of intelligent systems are covered in the courses "Personalization and Services", "Recommender Systems", "Service Analytics" and "Social Network Analysis in CRM". The content includes procedures and methods to create user-oriented services. The measurement and monitoring of service systems, the design of personalized offers, and the generation of recommendations based on the collected data of products and customers are discussed. The importance of user modeling and -recognition, data security and privacy are adressed as well.
Recommendation
None
Workload
The total workload for this module is approximately 270 hours. For further information see German version.