Effective marketing decision-making requires accurate data, careful analysis of these data, and adequate information on the underlying marketing problem to be solved. This course focuses on how marketing decisions are supported by research techniques. We will discuss different research designs, data collection methods, measurement and scaling techniques, as well as methods for analyzing empirical data (i.e., hypothesis testing, regression techniques, dimensionality reduction). The course helps you to acquire a thorough understanding of the marketing research process and the analytical techniques that are frequently used by marketing analysts to support marketing decisions, such as marketing-mix planning, market segmentation, brand positioning, and new product development. Further, we focus on how to use and to interpret the information provided by these techniques in practical business settings.

The aims of this course are to teach students the methods, principles, and theories of modern marketing research and to apply these to practical business settings. The objectives of the course are:

• To develop an understanding of the marketing research process and the most commonly used research techniques
• To learn how information is obtained and delivered to solve marketing problems
• To enhance your analytical skills, to develop the ability to translate business problems into actionable research questions and to design an adequate research plan to answer these questions
• To understand methods and tools for data collection and analysis
• To train your ability to analyze and interpret marketing research data using R, a leading software package for statistical data analysis
• To improve your communication, presentation and team working skills

Attendance is compulsory in all course lectures. To pass the course, you must be present for at least 80% of the time during the lectures. If you are unable to attend a lecture, please inform the instructors before the unit. You must catch up on the content covered in a missed lecture by yourself.

The course is taught using a combination of interactive lectures, class discussions, case analyses, computer exercises, and student presentations. The sessions will be held in the computer lab and include an introduction to questionnaire design, online survey methods, data handling, and data analysis using the statistical software package R. The aim is to familiarize students with the conceptual foundations of the analytical techniques before applying the acquired knowledge to real-world data sets. To achieve this goal, a flipped classroom teaching method will be used. This means that students are required to familiarize themselves with the contents by means of self-study before each session (i.e., by going through the materials on their own). This process will be aided by an online learning tutorial featuring textual descriptions of the contents along with R-code snippets and pre-recorded explanatory videos (see https://wu-rds.github.io/MA2024/). To be prepared for class, you must work through the material assigned for the week and be ready to answer questions about it. During the live sessions in the classroom, we will focus on live problem-solving and work through applications related to the contents and clarify points that require further discussion. It is suggested to come with questions or comments about the material that you think might be interesting and helpful to the class. 

The weekly readings will be complemented by case studies and computer exercises, which aim to transfer the acquired knowledge to new business settings. These completed take-home assignments need to be handed in via Canvas WU within the indicated deadlines. Furthermore, the course comprises a group assignment in which students need to design and conduct their own market research project. The results of this project should be summarized in the form of an in-class presentation. To facilitate the interaction among students, there will be an online forum for discussions about the contents. Attendance and participation in class discussions is critical to the success of the course and will be part of your grading. 

The course covers practical applications of data analytics for which the software "R" is required. R is a powerful tool for data analytics and visualization, which will be pre-installed on the computers in the lab where the sessions will be held. However, since R is an open source software, you may also choose to download and install the software on your computer for free. We recommend to use R via the integrated development environment (IDE) RStudio, which is also available for free:

• Download R: https://cran.r-project.org/ 
• Download R Studio: https://www.rstudio.com/products/rstudio/download/ 

Please also make use of the abundance of web resources regarding R (e.g., http://r4ds.had.co.nz/). For students who would like to further train the materials covered in class, we recommend DataCamp (www.datacamp.com), an online platform that offers interactive courses in data science at different levels. To facilitate the learning process you will obtain full access to the entire DataCamp course curriculum for the duration of the course. 

Grading is based on the following components:

• Market research group project (questionnaire design, data collection & analysis, reporting & presentations) [weight: 30%]
• Individual take-home computer exercises (statistical analysis of data sets) [weight: 30%]
• Final exam (concepts & methods) [weight: 30%]
• Class participation (quantity & quality of contributions in class) [weight: 10%]

These grading components will be weighted with the respective weights to arrive at the final grade percentage.

Please note that to ensure an equal contribution of group members for the group assignment, a peer assessment will be conducted among group members, which enters into the computation of the individual grades for the project. This means that the members of a group are required to assess other students regarding their relative contribution. 

The following grading scheme is used:

>= 90%                      excellent (1)

80% to 89%               good (2)

70% to 79%               satisfactory (3)

60% to 69%               sufficient (4)

< 60%                        fail (5)

To successfully pass this course, your weighted final grade needs to exceed 60%.

This course is designed for students of the WU Master's Program (MSc) in Marketing. Admittance to the program is a prerequisite for successful participation of the course.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

I am happy to answer your questions, so feel free to send me a short email (nils.wloemert@wu.ac.at). However, please note that before you contact me regarding specific questions, you should try to solve problems on your own first (e.g., by using the online tutorial, doing research online, or using the online forum).