GRA 6437 Marketing Research
GRA 6437 Marketing Research
Due to technological developments and the proliferation of high-quality data, marketing is becoming an increasingly quantitative profession. This means that marketing professionals should not only be creative, they also must have a solid background in marketing analytical tools in order to make sense of all the available data. In this course, you will learn how to analyze secondary data using multivariate techniques with the purpose of enhancing marketing decisions.
The focus in the course is be able to determine which marketing problem requires which particular analytical approach. The students will solve real-life marketing problems through the analysis of secondary data. They will have to come up with managerial recommendations based on their findings and they will learn how to communicate these findings effectively to a management audience with the use of Powerpoint presentations and by means of a written report. Hence, this course is not just a statistics course; the emphasis is on the managerial aspects of the statistical tools.
Given the rapidly changing technological environment, as indicated by the buzzwords "Big Data", marketing accountability, data science, etc., this course will summarize the most recent developments in marketing research in general and introduce the students to state-of-the-art analytical methods in particular.
Students should have working knowledge of SPSS before the course starts.
The overall learning goal is to be able to see the benefits of analytical decisions in marketing, namely how it can lead to better decisions contributing to the firm's goals. Closely related to this ability is the ability to discern and choose between possible techniques as well as the ability to apply this technique appropriately, given a particular marketing problem. Finally, the aim is to convincingly communicate the findings to the firm's decision makers in an understandable, non-technical language.
To achieve the learning outcomes, the students must be able to:
1) Explain the differences and similarities between key techniques.
2) Understand each technique in terms of:
- Its data requirements
- The type of problems it can be used for
- The underlying statistics
- The assumptions/limitations
- Its relationship with other techniques
3) Interpret the outputs (results) and derive managerial implications
4) Oral and written communication of the results
Students should be able to:
- Effectively communicate the outcomes of their statistical analyses to decision makers.
- Utilize statistical packages like SPSS and Excel to analyze secondary data.
- Choose the appropriate statistical technique for the marketing research problem at hand.
- Decide which data / variables may give answers to relevant marketing research problems.
Students should be able to:
- Understand and respect the privacy of the subjects of the analyses.
- Be fully transparent with regard to how data are collected and analyzed.
- Be aware of the limitations of statistical technique / multivariate method.
- A refresher in inferential statistics
- Analysis of Variance (ANOVA) and related methods
- (Logistic) Regression analysis
- Factor analysis
- Cluster analysis
- Conjoint analysis
- Presentation and write up of findings
This course outline may be subject to changes.
The learning process in this course takes place through 1) lectures about different multivariate techniques and how they can be employed to solve marketing problems, and 2) lab sessions in which the students practice the application of these techniques to real-life marketing problems using secondary data. Class attendance is strongly recommended but not required; participation in lab sessions however is mandatory.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.
At resit, all exam components must, as a main rule, be retaken during next scheduled course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group/Individual (1 - 3) Duration: 2 Week(s) Comment: Assignment (hand out in the beginning of the semester) Exam code: GRA 64371 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group/Individual (1 - 3) Duration: 2 Week(s) Comment: Assignment (hand out in the middle of the semester) Exam code: GRA 64371 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: Written examination under supervision. Exam code: GRA 64371 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Group work / Assignments | 50 Hour(s) | |
Student's own work with learning resources | 40 Hour(s) | |
Prepare for teaching | 34 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.