363-1043-00L Marketing Analytics
|Semester||Spring Semester 2020|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|363-1043-00 S||Marketing Analytics|
19.02.2020 13:00-17:00 WEV F 109-111
04.03.2020 13:00-17:00 WEV F 109-111
11.03.2020 13:00-16:00 tba
18.03.2020 13:00-14:30 tba
01.04.2020 13:00-14:30 tba
22.04.2020 13:00-14:30 tba
06.05.2020 13:00-14:30 tba
13.05.2020 13:00-14:30 tba
20.05.2020 13:00-14:30 tba
27.05.2020 13:00-17:00 tba
|Abstract||Students will use extensive customer data from an insurance company in order to develop prediction models for e.g. customer revenue and churn in a prediction challenge. They will work in groups and give a final presentation.|
The class will be held by Andrea Ferrario (Mobiliar Lab for Analytics/Chair of Technology Marketing) and Sebastian Tillmanns (Chair of Technology Marketing).
|Objective||- Participants of this class will gain an understanding, how value can be generated out of customer data.|
- Participants will learn how to prepare real customer data.
- Participants will be able to develop prediction models autonomously.
|Content||Students of this class will gain an understanding how to extract value from customer data autonomously by participating in a prediction competition. Therefore, they receive real customer data from an insurance company. Students are free to prepare the provided data and develop prediction models in the way they consider the best. Their freedom of choice covers all statistical methods, software packages and data that are available to them. At the end of the class, their predictions will be compared with the real development of the customers in the provided sample. Furthermore, students will give final presentations at the end of the class, which will be joined by representatives of the insurance company. Students will have to write a short paper, in which they describe how they proceeded. We expect that students test different prediction models against each other to justify their proceeding.|
At the beginning of the class, students will be able to visit several lectures, which will help to work on the given prediction task. These lectures involve fundamentals of marketing analytics and data analytics with common software packages. Throughout the lecture, several time slots are provided, where students can discuss their prediction models with the lecturers Andrea Ferrario (Mobiliar Lab for Analytics/Chair of Technology Marketing) and Sebastian Tillmanns (Chair of Technology Marketing).
The data handling and prediction skills students achieve in this class are not limited to marketing applications, but can be easily extended to other fields where predications of continuous or binary metrics are useful.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||3 credits|
|Type||graded semester performance|
|Language of examination||English|
|Repetition||Repetition only possible after re-enrolling for the course unit.|
|Additional information on mode of examination||Students have to form groups and turn in prediction models and a paper about these models.|
|No public learning materials available.|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|
|Management, Technology and Economics Master||Recommended Elective Courses||W|