In this course, we cover two main operational fields of companies in digital marketing: SEO (Search Engine Optimization), and SEA (Paid Search Engine Advertising) - both billion dollar industries and an absolute mainstay in practical marketing for companies. In SEO and SEA, content writing and optimization is a very important factor for improving performance and acquiring customers. We aim at providing students with the following knowledge and skill set:

1. Principles of practical SEO and SEA content optimization (basic principles of the field, technical SEO, basic mechanics of SEO content writing and optimization, mechanics of ad content writing and bidding optimization, etc.)
2. Advanced statistical understanding and analyses of SEO and SEA content (key performance indicators, principles of quantitative analyses and statistical methods)
3. Applied machine learning methods for SEO and SEA content optimality (basic principles of machine learning, analytic methods like Topic Modeling, Random Forests, k-Nearest Neighbors, and natural language generation methods like OpenAI's GPT)

That enables students to prevail in that highly competitive field by using the practical skills gained in the course, as well as by being capable of applying thorough quantitative analyses, domain-related programming skills and analytic and generative methods using R and Python.

When you successfully complete the course you should have:

• A good understanding of SEO and SEA practice, principles, and data
• The ability of writing compelling content and optimizing it
• A good understanding of statistical methods and experimental setups in the field
• A good understanding of machine learning methods to understand, shape and automate SEO and SEA processes
• The ability to independently and correctly apply quantitative methods in the field

Students are expected to attend all units of the course and are expected to participate in class. In exceptional cases (e.g., sick leave), students might inform the lecturer and are allowed to miss at most one unit.

The course takes place on campus and in presence mode.

We use a combination of material presented by the lecturer supported by practical examples, R and Python code, and data. In addition, students get the opportunity for hands on optimization and analyses using state-of-the-art methods and tools in each unit.

Students' performance is assessed based on various tasks listed below:

• In class exercises (10%):
  • practical application of quantitative methods and skills in the field and presentation of the research interest, concept, data and insights (10%)
• Project milestone 1 (40%):
  • a written research proposal (~1 page, including research interest, theory, hypotheses (derived from theory), data, method) (10%)
  • a presentation of the SEA or SEO research proposal (~5–10 minutes) (30%)
• Project milestone 2 (50%):
  • a written project paper (~3 pages, including research proposal, theory, hypotheses, data descriptives, analysis (application of a statistical or machine learning method), findings & discussion) (15%)
  • a final presentation of the written project report (~15–20 minutes) (35%)

The grading scheme is as follows:

• < 60%                  fail (5)
• 60% to 69.99%    sufficient (4)
• 70% to 79.99%    satisfactory (3)
• 80% to 89.99%    good (2)
• >= 90%                excellent (1)

Generally, the course is structured in a way that students with no prior programming skills are able to follow and successfully complete the course. However, prior quantitative knowledge and programming skills might be beneficial for students.