AI Applications in Marketing (MSB209)

This course aims to introduce students to novel quantitative approaches (such as artificial intelligence (AI) methods and text analytics) that can be applied to solve marketing problems and manage customer experience.

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by January 6th for the spring semester.


Course description for study year 2024-2025

Facts

Course code

MSB209

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

In today's digital age, marketing has transcended traditional boundaries and become a dynamic, data-driven discipline. Artificial Intelligence (AI) has emerged as a revolutionary force in this transformation, offering marketers unprecedented opportunities to optimize strategies, personalize customer experiences, and achieve remarkable results. This comprehensive course, "AI Applications in Marketing" is designed to guide you through the multifaceted world of AI and its profound impact on the marketing landscape.

Learning outcome

Knowledge

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

  • Foundations of AI (deep learning, neural networks, etc)
  • Data-driven approaches (such as AI methods, text analytics) applied to marketing data.
  • Implications of new technologies such as AI on customer experience and related ethical considerations (eg customer privacy implications, etc)

Skills

  • Analyze marketing data and apply AI methods and text analytics to support marketing decisions.
  • Discuss the implications of new technologies such as AI on customer experience and related ethical considerations

Required prerequisite knowledge

None

Exam

School exam (individual) and Portfolio (group)

Form of assessment Weight Duration Marks Aid
School exam 1/2 3 Hours Letter grades
Portfolio (group) 1/2 7 Days Letter grades

The final grade is based on an individual exam 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

Attendance (70%), Lab. exercises, Presentations

The following are mandatory course requirements:

  • Class participation (70%)
  • Lab exercises
  • 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 coordinator:

Auke Hunneman

Course teacher:

Mainak Sarkar

Course coordinator:

Mainak Sarkar

Study Program Director:

Ingeborg Foldøy Solli

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

Open for

Industrial Economics - Master of Science Degree Programme Master of Science in Accounting and Auditing Business Administration - Master of Science
Exchange programmes at UIS Business School

Course assessment

There must be an early dialogue between the course supervisor, the student union 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 subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

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