MAN 5221 AI for Strategic Marketing Leadership
MAN 5221 AI for Strategic Marketing Leadership
Despite the surge in interest around artificial intelligence within the business community, many implementations still fall short of expectations. Often, this is because the focus remains heavily on data and methodologies, while real value creation is overlooked. Additionally, a communication gap exists between data and computer scientists and decision-makers, as they don’t always speak the same language. This course is designed to bridge that divide. With a managerial focus, it equips future leaders with the knowledge to leverage AI’s potential in addressing critical marketing challenges. Specifically, you’ll discover how AI can drive customer acquisition, boost retention, and deepen customer relationships. The curriculum offers insight into a wide range of AI techniques, emphasizing intuitive understanding and practical requirements without discussing the technical details.
- Develop a thorough understanding of how AI can transform marketing strategy, with particular focus on customer acquisition, retention, and relationship management.
- Demonstrate in-depth knowledge of key AI methodologies, including text mining, ad response modeling, and conjoint analysis, and critically assess their relevance for addressing complex marketing challenges.
- Evaluate the potential of AI to enhance business performance, through data-informed decision-making, improved customer experiences, and more effective allocation of marketing resources.
- Develop the ability to identify complex marketing challenges that can be effectively addressed with AI and communicate these needs clearly to both technical and managerial stakeholders.
- Critically evaluate AI tools and platforms, assessing their alignment with strategic marketing goals, business objectives, and organizational capabilities.
- Assess the feasibility of applying AI techniques by analyzing data requirements, computational constraints, and the potential to generate meaningful business value.
A central theme of this course is developing a thorough understanding of how AI can create value for a company. Students will be encouraged to critically explore the data requirements, regulatory and ethical considerations, and strategic purposes underlying the application of AI to marketing challenges. They will also gain insight into the potential value these techniques can generate, while developing an awareness of their limitations and appropriate use cases.
The course is structured around the three themes below:
A: Matching offerings to customer needs
- Market response modeling for resource allocation
B: Find products for a customer
- Cluster analysis for segmentation
- Text mining for analyzing user-generated data (reviews, etc.)
- PCA for dimensionality reduction
- Network analysis for understanding innovation diffusion
C: Determine offering properties
- Choice modeling for churn analysis
- Conjoint analysis for new product development
The course consists of 2 modules with physical teaching (3+3 days) in addition to digital teaching, a total of 75 hours. The teaching consists of a combination of physical lectures, webinars, group work and exercises.
In addition to lectures and guest presentations, this course will feature hands-on exercises based on real-life case studies. The emphasis will be on identifying marketing problems that can be effectively addressed with AI solutions. Instead of delving into technical details, the primary focus will be on interpreting the results of AI analyses and implementing actionable strategies based on those insights.
The course will use the AI tool ChatGPT UiO.
The students are evaluated through a term paper, counting 60% of the total grade and a portfolio exam counting 40%. The term paper is written in groups of 1-3 students. All evaluations must be passed to obtain a certificate for the course.
For the term paper, the guidance/supervision offer is estimated at 2 hours per. group.
Please note that while attendance is not compulsory in all modules, it is the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ itslearning or other course materials.
The term paper is included in the degree’s independent work of degree, cf national regulation on requirements for master’s degree, equivalent to 9 ECTS credits per. 15 credits course. For the Executive Master of Management degree, the independent work of degree represents the sum of term papers from the courses/programmes.
In all BI Executive courses and programs, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Bachelor degree, corresponding to 180 credits from an accredited university, university college or similar educational institution
The applicant must be at least 25 years of age
At least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 60 Grouping: Group (1 - 3) Duration: 1 Semester(s) Comment: Term paper, counts 60% of the total grade. Exam code: MAN 52211 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Portfolio Assessment PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Individual Duration: 1 Semester(s) Comment: Portfolio exam, counts 40% of the total grade. The portfolio exam consists of two submissions where formative feedback is given so that students can improve, correct and adjust. Towards the end of the course, one of the submissions will be selected and graded. Exam code: MAN 52212 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 48 Hour(s) | |
Webinar | 27 Hour(s) | Webinars, group work and exercises |
Prepare for teaching | 75 Hour(s) | |
Digital resources | 50 Hour(s) | |
Group work / Assignments | 50 Hour(s) | |
Examination | 150 | Work with portfolio exam and term paper |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 15 ECTS credit corresponds to a workload of at least 400 hours.
