MRK 3561 Marketing Analytics
Marketing is more data intensive than ever before and the modern marketing manager needs to have a working knowledge of what marketing data looks like, how it can be handled, analyzed, and presented. This class is an introduction to the analysis of marketing relevant data and decision-making from a business perspective. This course provides students with central knowledge of generating data driven marketing insights. It demonstrates the application of statistical and econometric concepts to marketing problems in the below specified software environments. The students learn to select suitable methods and to implement them in marketing data using relevant software. A central component is the transfer of theoretical knowledge about data analysis methods to practical applications while focusing on business problems rather than on research problems. Another central component is strengthening student’s communication skills with respect to reporting the results of marketing relevant data analyses.
Students that successfully complete this course should know:
- sources of marketing-relevant primary and secondary data, both internal and external to the firm.
- data requirements, (basic) assumptions, and outcomes of different analytical approaches.
- connections between marketing-specific decision problems, required data, and suitable methods.
- how to interpret marketing data and use it to create appropriate visualizations.
- how to support marketing decision-making using basic data analysis.
The students that successfully complete this course should be able to:
- identify suitable data and methods for given marketing decision-problems.
- use software for data preparation and graphical presentation of marketing data.
- carry out marketing analytics using real-life data and a statistical analysis package (e.g., SPSS, Excel, JMP, or Marketing Engineering).
- communicate and appropriately report the results of data analysis for marketing decision-making.
- justify decisions-making using the outcomes of their data analysis.
The students are expected to
- develop a solid conceptual understanding of the topics as well as feel comfortable with the applications of the different techniques using the software specified.
- reflect on the limitations and assumptions underlying the discussed methods as well as ethical implications of data analysis in marketing.
- reflect on the advantages and limitations of data-driven decision-making in marketing.
The following gives a brief overview of the topics that will be discussed in the course. This list is tentative and could be subject to change.
- Introduction to Marketing Analytics
- Statistics basics for Marketing Analytics (e.g., inference, significance, mean, median, variance, etc.)
- Secondary Data Sources and 3C Analysis
- Survey data
- Managing Customer Heterogeneity (Segmentation, Targeting, and Positioning)
- Managing Customer Dynamics (e.g., Predictive Modelling)
- Managing Sustainable Competitive Advantage (e.g., related to Brand, Offerings, Customer Relationships)
- Managing resource trade-offs (e.g., Resource Allocation Models, A/B testing)
The course will use a combination of lectures, software demonstrations and exercises. Lectures will focus on introducing different type of marketing data, marketing decision problems, and corresponding data analysis methods. The software demonstrations will illustrate how to use software such as SPSS, Excel, JMP, or Marketing Engineering/Enginius to implement the methods on marketing data and interpret the outcomes. In addition, students will work in class and at home on small data analyses assignments and submit some of these as part of their work requirements (graded as pass/fail). The solutions of these assignments will be discussed in class.
In course delivery as online courses, lecturer will, in collaboration with the student administration, organize an appropriate course implementation, combining different learning activities and digital elements on the learning platform. Online students are also offered a study guide that will contribute to progression and overview. Total recommended time spent for completing the course also applies here.
- This course will be using the Marketing Engineering/Enginius marketing analytics suite, comprising the textbook, data, case studies, and the statistical software. As per BI's current policy, students will need to purchase access at their own expense. The price is comparable to or lower than that of stand-alone textbooks.
- There could be COVID-induced deviations from course description.
Students that have not gotten approved the coursework requirements, must re-take the exercises during the next scheduled course.
Students that have not passed the written examination or who wish to improve their grade may re-take the examination in connection with the next scheduled examination.
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
- Introductory statistics
- Introduction to marketing
|Mandatory coursework||Courseworks given||Courseworks required||Comment coursework|
|Mandatory||6||4||Small data analyses assignments graded as approved / not approved.|
|Comment coursework:||Small data analyses assignments graded as approved / not approved.|
|Exam category||Weight||Invigilation||Duration||Support materials||Grouping||Comment exam|
Form of assessment:
Examination every semester
|100||Yes||3 Hour(s)||Individual||(Changed from Written submission to Structured test 21/04/2023)|
|Form of assessment:||Structured test|
|Support materials:|| |
|Comment:||(Changed from Written submission to Structured test 21/04/2023)|
|Exam code:||MRK 35611|
|Resit:||Examination every semester|
Prepare for teaching
Group work / Assignments
Student's own work with learning resources
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.