Marketing Analytics (MSB202)

"The world’s most valuable resource is no longer oil, but data" - The Economist

Marketing is not just an art; it requires the use of data and quantitative techniques to support marketing decisions and strategies in a rapidly changing market environment. As data has become one of the key factors shaping today’s businesses, firms are increasingly seeking managers and analysts with advanced background in marketing models to make effective data-driven marketing decisions.


Course description for study year 2023-2024. Please note that changes may occur.

Facts

Course code

MSB202

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English

Offered by
Time table

View course schedule

Content

Marketing managers make frequent decisions about product features, prices, distribution options, and advertising budgets. This course provides an understanding of key marketing decisions as well as how data and marketing analytical tools can help managers make better decisions. This course thus takes a user’s perspective and shows how users can enhance their decision-making by translating data and modeling results into action. The course is particularly relevant for students with an interest in quantitative research in marketing and related areas. It is also recommended for practice-oriented students who wish to improve their methodological knowledge. The course also aims to prepare students who are writing their master thesis in marketing and students who wish to pursue a research career by entering a PhD program.

Learning outcome

Knowledge

Upon completion of the course, students will have knowledge of:

  • Key analytical models in marketing
  • The strengths and weaknesses and when to employ the different models
  • The challenges and necessary considerations for various model specifications

Skills

After completion of this course, students will be able to:

  • Analyze marketing data and apply marketing models using programming language R
  • Interpret results from marketing models and support managerial recommendations with justifications
  • Demonstrate ability to communicate results and recommendations effectively
  • Evaluate the strengths and weaknesses of different models in a marketing decision context

Required prerequisite knowledge

None

Exam

Take-home exam (individual) and Portfolio of mandatory work components (group)

Form of assessment Weight Duration Marks Aid
Take-home exam (individual) 1/2 14 Days Letter grades
Portfolio of mandatory work components (group) 1/2 7 Days Letter grades

The final grade is based on a take-home exam (individual) and a portfolio of mandatory work components, including group assignments.Students failing the portfolio evaluation will be granted the opportunity of taking a deferred exam. This exam will take the form of new written individual assignments.

Coursework requirements

Mandatory exercises

The following are mandatory course requirements: class participation (70%), lab exercises, and presentations. In order to take the exams, students must pass all coursework requirements.

70% attendance at all mandatory sessions starting from week 1 of teaching.

Course teacher(s)

Course teacher:

Mainak Sarkar

Course coordinator:

Mainak Sarkar

Study Program Director:

Yuko Onozaka

Method of work

Lectures and tutorials, labs, group work, and independent study. The estimated distribution of ECTS hours are as follows:

1. Lectures and tutorials: 50 hours

2. Group work and labs: 110 hours

3. Independent study of course material 120 hours

Overlapping courses

Course Reduction (SP)
Advanced Marketing Analytics (MØA202_1) 10

Open for

Admission to Single Courses at UiS Business School
Master of Science in Accounting and Auditing Business Administration - Master of Science Business Administration - Master of Science (5 years)
Exchange programmes at UIS Business School

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

The syllabus can be found in Leganto