GRA 4139 Marketing Analytics

GRA 4139 Marketing Analytics

Course code: 
GRA 4139
Department: 
Marketing
Credits: 
6
Course coordinator: 
Auke Hunneman
Course name in Norwegian: 
Marketing Analytics
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2019 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

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. This course aims to cover topics in marketing analytics, an area that is of utmost importance for the development of sound marketing strategies and tactics. It will be particularly useful for students who want to pursue a career in quantitative marketing and marketing consulting.

Learning outcomes - Knowledge
  • Develop an understanding of relevant applications of analytics in marketing;
  • Know how to use statistical tools to evaluate the impacts of marketing decisions on relevant outcome variables like revenues, profits or customer lifetime value;
  • Understand which analytical tools are used for which marketing problems;
  • Gain an understanding of the data requirements and limitations of different marketing analytical tools.
Learning outcomes - Skills
  • Being able to use marketing models to support relevant marketing decisions;
  • Advice decision makers in the organization about the outcomes of the analyses (e.g., regarding budget allocation);
  • Determine the return on investment of several types of marketing investments.
Learning Outcome - Reflection
  • Understand the limitations of certain types of data (e.g., customer-level data) and marketing analytical tools.
  • To evaluate data quality and decide whether it is meaningful to the problem being analyzed.
Course content

Topics covered in this course include:

  1. Model specification, estimation, validation and use
  2. Market response models
  3. Choice models
  4. Customer base analysis, including latent-attrition models
  5. Spatial models
Learning process and requirements to students

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 that is not included on itslearning or text book

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 components will get a lower grade or may fail the course. Information about the point system and the cut off points with reference to the letter grades, will be given when the course starts.

All components must, as a main rule, be retaken during next scheduled course.

Software tools
R
SPSS
Qualifications

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.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
20
Grouping: 
Group/Individual (1 - 3)
Duration: 
2 Week(s)
Exam code: 
GRA 41391
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)
Exam code: 
GRA 41391
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: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
3 Hour(s)
Comment: 
Written examination under supervision.
Exam code: 
GRA 41391
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam organisation: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Sum workload: 
0

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.