Statistical Learning in Marketing
Faculteit | Economie en Bedrijfskunde |
Jaar | 2021/22 |
Vakcode | EBM214A05 |
Vaknaam | Statistical Learning in Marketing |
Niveau(s) | master |
Voertaal | Engels |
Periode | semester I a (en semester II a) |
ECTS | 5 |
Rooster | rooster |
Uitgebreide vaknaam | Statistical Learning in Marketing | ||||||||||||||||
Leerdoelen | 1). Have a good understanding of the essential elements of good (marketing) research, thereby taking into account the possible impact of research outcomes; 2). Describe different forms of marketing research; 3). Explain what the requirements are for different types of analysis techniques; 4). Critically assess which statistical learning technique should be used in which marketing-related situation using a dataset; 5). Perform statistical learning techniques in a marketing context. These techniques include factor analysis, cluster analysis, advanced regression analysis including time-series analysis, among others; 6). Critically interpret and assess the output of a statistical learning technique in marketing; 7). Use the output of each of the above mentioned techniques to write a report which translates research results into managerially relevant and actionable insights and recommendations. |
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Omschrijving | Building on methodological knowledge which is standard in business-related bachelor degrees, this course takes the extra mile, and focuses on statistical learning techniques applied in marketing research. These include data reduction techniques like principal component analysis and factor analysis; advanced regression techniques like the general linear model; time-series analysis techniques like VAR/VEC; and cluster analysis. These techniques are used in marketing for example for segmentation, targeting, positioning, marketing effectiveness evaluation, and forecasting. This course will use R-based software, and starts with an introduction to R. Lectures, and teacher-assisted hands-on interactive classes will support the students in solving the assignments. | ||||||||||||||||
Uren per week | |||||||||||||||||
Onderwijsvorm | -computer practicum, -hoorcollege , computer practica | ||||||||||||||||
Toetsvorm | -groepsopdracht, -individuele opdracht | ||||||||||||||||
Vaksoort | master | ||||||||||||||||
Coördinator | prof. dr. M.J. Gijsenberg | ||||||||||||||||
Docent(en) | prof. dr. M.J. Gijsenberg , S.M. Rizio | ||||||||||||||||
Verplichte literatuur |
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Entreevoorwaarden | There are no formal pre-requisites for this course. However, basic knowledge on e.g. methodology and the testing of hypotheses as can be found in Malhotra (2009) chapters 1-15 is assumed. In addition, a basic knowledge of R (software) is strongly recommended. | ||||||||||||||||
Opmerkingen | Secretary Marketing (B. Wever): phone +31(0)50 3637065, e-mail marketing.education@rug.nl, room 5411-0334. |
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