MRK 3561 Marketing Analytics
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 data-driven 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 on 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 to support marketing decision-making and strategy recommendations.
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 find, interpret, reconcile, and assess key numbers and metrics from marketing-relevant data analysis.
- 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 the specified software for data preparation, analysis, and graphical representation of marketing data.
- carry out marketing analytics using real-life data and a statistical analysis package.
- validate, assess, and interpret the results of data analyses.
- communicate and appropriately report the results of data analyses for marketing decision-making.
- justify decision-making using the outcomes of a data analysis and formulate strategy recommendations.
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
- Managing customer heterogeneity (segmentation, targeting, and positioning)
- Managing customer dynamics (e.g., predictive modelling, customer lifetime value)
- Managing sustainable competitive advantage (e.g., related to brand, offerings, customer relationships)
- Managing resource trade-offs (e.g., resource allocation models, A/B testing)
- Marketing data sources and 3C analysis
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 (e.g., SPSS, Excel, or 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.
E-Learning
Where the course is delivered as an online course, the lecturer will, in collaboration with the study administration, arrange an appropriate combination of digital learning resources and activities. These activities will correspond to the stated number of teaching hours delivered on campus. Online students are also offered a study guide that will provide an overview of the course and contribute to course progression. The total time students are expected to spend completing the course also applies to online studies.
- This course will be using the 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.
Re-sit examiniation
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.
Higher Education Entrance Qualification
Disclaimer
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 | 5 | 3 | Course related data analyses assignments assessed as approved / not approved. |
| Assessments |
|---|
Exam category: Submission Form of assessment: Structured test Invigilation Weight: 100 Grouping: Individual Support materials:
Duration: 2 Hour(s) Exam code: MRK 35611 Grading scale: ECTS Resit: Examination every semester |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 40 Hour(s) | |
Prepare for teaching | 40 Hour(s) | |
Group work / Assignments | 60 Hour(s) | Working on the mandatory assignments |
Student's own work with learning resources | 58 Hour(s) | Incl. exam preparations |
Examination | 2 Hour(s) |
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.