Marketing Analytics
An introduction to systematically gaining consumer insights based on big structured and unstructured data
Quick Info: Bachelor ׀ Lecture, tutorial ׀ English ׀ Summer term ׀ ECTS 6
Important! Re-take exam "Marketing Analytics" Winter Term 2023/2024
The re-take exam of the course “Marketing Analytics” will take place on December 01, 2023, from 01:00-03:00 pm in room Geschw.-Scholl-Pl. 1 (B) - B 201. Please note that you should arrive in the room no later than 12:30 pm. Please bring your Student ID with you.
The registration time slot for the re-take exam of Marketing Analytics in the winter term 2023/2024 will be open from October 17, 2023, to November 10, 2023. If you want to participate in the aforementioned re-take exam, please register for it on LSF.
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