Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 10/03/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 10/10/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 10/17/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 10/24/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 10/31/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 11/07/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 11/14/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 11/21/24 | 09:00 AM - 11:00 AM | D2.0.030 |
| Thursday | 12/05/24 | 09:00 AM - 11:00 AM | D2.0.392 |
| Thursday | 01/16/25 | 08:00 AM - 12:00 PM | TC.1.02 |
| Thursday | 01/23/25 | 08:00 AM - 05:00 PM | TC.2.02 |
Digital technologies have profoundly changed the way and pace of companies interacting with their customers and innovating their products. For instance, today's marketers and researchers are faced with an enormous amount and variety of data that consumers share online - from product feedback via text messages and reviews, to images and videos posted by branded experiences on social media. At the same time, companies also strive to interact with their customers on various social media channels by producing compelling and relevant multimedia content for their target audiences, and to incorporate customer feedback and needs in their product development.
This course intends to provide the theoretical foundations behind state-of-the art marketing and innovation in the digital economy to address these challenges in digital management practice. Our course will cover the following modules:
- Marketing strategy implications of moving from the traditional to the digital economy
- Idea generation and innovation processes
- Insights from data: Analyzing digital customer journeys and how leverage the abundance of (customer) data and the latest AI developments for marketing and innovation decisions
- Social media marketing: Social network theory, digital customer-brand relationships, user-generated content and mechanisms behind virality of digital content
- Measuring success of your marketing measures: Social monitoring, social listening, and metrics to measure success on social media
- Customers as co-producers, feedback validation loops, and the minimum viable product
The course will be based on marketing/management articles in leading academic journals, case studies and guest lectures from marketing and innovation practice.
After completing the course, you will have gained proficiency in analyzing customer decision making and relevant trends in marketing and innovation. This course also intends to develop in-depth knowledge for the assessment of opportunities and threats and for making well-grounded predictions of market outcomes.
Specifically, students will be able to:
1. Analyze and apply processes and techniques for generating and communicating innovative ideas;
2. Understand and apply methods to generate insights from customer data
3. Understand concepts behind social media interactions, social networks and network related phenomena, and how these can impact customer behavior
4. Infer strategic marketing/innovation decisions from empirical findings
5. Develop a consistent argument from having an initial idea to a business pitch, supporting it with empirical evidence
6. Evaluate innovations based on their potential (feasibility, marketability, desirability) and select appropriate courses of action.
Participation in all sessions is mandatory.
You may miss up to max. 20% of class hours, but please note that content is build-up in a modular way. According to WU rules, you have to attend 80% of the sessions in this course.
· Self-study (reading of scientific articles, case studies, and own research of articles to prepare group project case study)
· Lectures and in-class discussions
· Team presentation of group project
Weights for each grading component used to compute your final grade:
- Group case presentations: 40%
- Assignments: 40%
- Attendance/Participation: 10%
- Peer review: 10%
The quizzes and in-class participation is graded at the individual level, while the group project work is graded at the group level.
We also want to ensure equal contribution of group members to the group work. Thus, a peer assessment will be conducted among group members, which enters into the computation of the individual grades for your group work. This means that the members of a group are required to assess other students regarding their relative contribution.
The policies for the use of AI tools will be provided in the instructions for every assignment individually.
No specific analytical skills and software knowledge required.
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