Marketing Analytics
An introduction to systematically gaining consumer insights based on big structured and unstructured data
Review Re-take exam " Marketing Analytics " Winter Term 2025/2026
The review of the Re-take exam " Marketing Analytics " of the winter term 2025/2026 will take place on Thursday, February 05, 2026, starting at 09:00 a.m. in room 329, Ludwigstr. 28 RG/III (rear building, third floor).
Registration is mandatory, and please register via this form by February 3, 2026.
Please note that a review conducted by an authorized person is allowed. The authorized person must present a proxy and their own identification document.
Quick Info: Bachelor ׀ Lecture, tutorial ׀ English ׀ Summer term ׀ ECTS 6
General Information
| Target group | Bachelor students (B.Sc) |
|---|---|
| Teaching format | Lecture and tutorial |
| Hours per week | 2 (lecture) and 2 (tutorial) |
| Rotation | Summer term |
| Type of examination | Written exam (120 minutes) In the winter term a post-exam is set |
| Credit points | 6 ECTS in WP 43 (Marketing and Strategy III) - PSTO 2015 |
| Time / room | See LSF for lecture and tutorials |
| Course language | English |
Course description
This course provides an introduction to the systematic creation of consumer insights based on large structured and unstructured data that consumers generate in their journey across different channels and touchpoints with companies (e.g., ratings, reviews, web clickstream data, transactions). Students learn about different sources and types of data, about collecting, verifying, and using data for enhanced marketing decision-making.
In particular, the course presents a portfolio of tools and techniques that decision makers can use to prepare and transform different data types into adequate information to support marketing decisions. Data visualization tasks offering clear business insights will be specifically emphasized. Students’ work will be application-oriented, as they will analyze business cases and (real) datasets by using software such as JASP, SmartPLS, Python, DataRobot, Tableau.
Structure
- Sources and types of date
- Modeling types: Supervised and unsupervised learning
- Seven-step marketing analytics process
- Big data in marketing
- Data quality, preparation, and transformation
- Data visualization
- Regression analysis
- Neural networks
- Automated machine learning
- Cluster analysis
- Market basket analysis
- Natural language processing
- Social network analysis
- Customer mindset metrics
- Structural equation modeling