| No. | Type(s) | Class Title | |
| 5621 | PI | Digital Marketing Simulation 
Registration via LPIS from 2024-02-16 14:00 to 2024-02-22 23:59 De-registration via LPIS from 2024-02-16 14:00 to 2024-03-16 23:59 |
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| Instructor(s) | Alicja Grzadziel, MSc. |
| Subject(s) Bachelor Programs | Specialization in Business Administration Course III - Digital Marketing Course III - Digital Marketing Course III - Digital Marketing |
| Credit hours | 2 |
| Language of instruction | English |
| Detailed schedule | ||||
| Tue, | 2024-03-19 | 13:00-16:00 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-04-09 | 13:00-16:00 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-04-16 | 13:00-16:00 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-04-23 | 13:00-15:30 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-05-07 | 13:00-15:30 | TC.3.02 (Map) | |
| Tue, | 2024-05-14 | 13:00-15:30 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-05-21 | 13:00-16:00 | LC.2.064 Computer Lab (Map) | |
| Tue, | 2024-06-04 | 13:00-16:00 | LC.2.064 Computer Lab (Map) | |
| Download schedule (ical) | Subscribe schedule | ||||
| Further information | https://learn.wu.ac.at/vvz/24s/5621 |
| Contact details: | ||
| alicja.grzadziel@wu.ac.at | ||
| Contents: | ||
Imagine the following scenario: You are working for Buhi Supply Co., a bag and apparel company that’s been doubling in size for the past five years. You're a new digital marketing analyst at Buhi Supply Co., and you've been tasked with helping the company improve the performance of its digital marketing campaigns. This is all part of a simulation called Mimic Analytics, which allows you to transform, integrate, and analyze data. This simulation gives students access to large data sets, allowing them to use the latest analytics tools, run cluster analyses, implement A/B testing, allocate campaign budgets, and more. It’s an engaging, hands-on way to boost student résumés and prepare them for the workforce. |
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| Learning Outcomes: | ||
YOU WILL LEARN TO:● Demonstrate an understanding of the processes and techniques of marketing data collection, analysis, and visualization. ● Explain and apply the logic of optimization and attribution in marketing analytics. ● Explain the terminology and tools of marketing analytics. ● Apply the practical tools and technique of marketing analytics. ● Understand the roles of data technologies, data management systems, and data visualization in marketing. ● Study and practice programming tools and structured query language. ● Engage in social listening and content analysis. ● Run field experiments in digital environments, including A/B testing. ● Understand marketing mix models.
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| Attendance requirements: | ||
This course will be held in presence mode. Attendance of at least 7 out of the 8 classes is required. In special instances, there will be a possibility to compensate for one additional absence on an individual basis. |
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| Teaching/learning method(s): | ||
This course will involve a variety of different learning formats:
Each unit will comprise both theoretical contents (based on the recommended reading) and interactive, more practically-oriented activities. |
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| Assessment: | ||
Weights for each grading component: • 30% of the final grade is based on presentations about articles. • 10% of the final grade is based on active participation in the classes. • 45% of the final grade is based on the results and rank in the 10 rounds of the simulation. • 15% of the final grade is based on the final presentation & peer assessment.
Bonus Task: In addition, you can receive a maximum of 10 percentage points for a bonus task. More info in class.
The transformation of the grade: • 1 (sehr gut / excellent): 100% - 90% of all points • 2 (gut / good): 89% - 80% of all points • 3 (befriedigend / satisfactory): 79% - 70% of all points • 4 (genügend / sufficient): 69% - 60% of all points • 5 (nicht genügend / fail): Below 60% of all points |
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