The rapid proliferation of the Internet and related services, especially social networks and user-generated content, has resulted in incredible amounts of digital information. This is better known as "Big Data" and enables new ways of marketing. However, analyzing massive amounts of data requires specialized tools and methods. Industry managers and researchers increasingly turn to technological innovations that make decision-making effective in Big Data.

The course covers three topics. Each of the three topics is covered in a separate block. Your progress will be assessed through theoretical and practical assignments you can complete at home. 

The goal of this course is to provide students with an understanding of how big data can be used for customer analytics (e.g., posting behavior), contextual marketing (e.g., creating personalized recommendations), and online communications management (e.g., reputation management, influencer marketing, and social media crises). Participants are shown how to analyze text social media posts in practical sessions. In this context, the language of Twitter, Reddit, and Amazon reviews is recognized, and moods and emotions are extracted. It will also show how these modern technologies can support management decision-making and optimize customer experience in a data-driven world.

After the course, you will be able to:

• Cite differences between sentiment, feelings, and emotions
• Analyze larger social media data sets
• Know how to leverage unstructured data (e.g., text)
• Be able to use SQL for basic data exploration
• To know the current state of research on "shitstorms" and social media marketing
• Know how to suggest automated content based on data

Attendance is compulsory in all course units. To pass the course, you must be present for at least 80% of the time in the units. If you are unable to attend a unit, please inform the course instructor before the unit. The content covered in a missed unit must be made up independently.

Learning methods include classical knowledge transfer, inquiry, and independent development of topics and issues. In addition, practical examples are demonstrated, which the students adopt.

Grading is based on the following components: Practical (practical assignments), theoretical assignments (theoretical assignments), and in-class participation.

Practical Assignments: practical assignments must be completed for each block. For example, students must complete small assignments. This part is to demonstrate the practical skills learned in this course.

Theoretical Assignments: For each block, students have to answer theoretical questions.

These grading components are added together to calculate the final grade, which is based on the following grading scheme:

To successfully pass this course, your cumulative points must exceed 60 points.

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Please note that the course has a strong analytical focus. Therefore, students are expected to be interested in data analysis and programming. However, prior knowledge in the areas is not required.

I will be happy to answer your questions. So email me or stop by my office by appointment if you want to speak with me. In addition, I will also try to be available after each online session.

The use of AI-based text generation software such as ChatGPT is not allowed. However, you can use AI-based tools for writing support (e.g., Grammarly).

Recommended Literature

• Seraj, Sarah, Kate G. Blackburn, and James W. Pennebaker. "Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup." Proceedings of the National Academy of Sciences 118.7 (2021).
• Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359.6380 (2018): 1146-1151.
• Kramer, Adam DI, Jamie E. Guillory, and Jeffrey T. Hancock. "Experimental evidence of massive-scale emotional contagion through social networks." Proceedings of the National Academy of Sciences 111.24 (2014): 8788-8790.
• Berger, Jonah, and Katherine L. Milkman. "What makes online content viral?." Journal of marketing research 49.2 (2012): 192-205.
• Resnick, Paul, and Hal R. Varian. "Recommender systems." Communications of the ACM 40.3 (1997): 56-58.
• Stephens-Davidowitz, Seth. Everybody lies: What the internet can tell us about who we really are. Bloomsbury Publishing, 2018.
• Varian, Hal R. "Big data: New tricks for econometrics." Journal of Economic Perspectives 28.2 (2014): 3-28.