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2023/2024  BA-BHAAV2304U  Artificial Intelligence for Marketing: Practical Applications and Use Cases (Online)

English Title
Artificial Intelligence for Marketing: Practical Applications and Use Cases (Online)

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for BSc in Economics and Business Administration
Course coordinator
  • Johannes Hattula - Department of Marketing (Marketing)
Main academic disciplines
  • Innovation
  • Management
  • Marketing
Teaching methods
  • Online teaching
Last updated on 08-02-2023

Relevant links

Learning objectives
On completion of this course, students will be able to:
  • understand the role of AI in a marketing context
  • design the structure for an effective implementation of AI in marketing
  • understand the value of AI in personalizing the consumer experience and journey
  • effectively incorporate AI into marketing strategy (including in product, price, communication, and sales marketing) 
  • critically evaluate the benefits and risks of AI for companies and for consumers 
Course prerequisites
Artificial Intelligence for Marketing: Practical Applications and Use Cases:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 48 hours to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

Students have 48 hours at home to answer a range of course-relevant questions, some of which will be in short essay form. Examples of exam questions will be shared with the students during the exam preparation class.   

Course content, structure and pedagogical approach

What is the main purpose of this course?

This course is designed to provide undergraduate students with basic understanding how artificial intelligence (AI) is changing the marketing environment and how it can be applied in various marketing contexts. The main purpose of the course is to learn from best-practice use cases (e.g., Amazon, Netflix, Starbucks) in order to develop and implement an AI-based strategy in a firm. This course is not about programming AI tools or statistical issues behind AI; the course takes a more practical approach and looks at how and where AI can be applied in today's marketing.


Why is this course relevant?

Artificial intelligence (AI) is changing the way how businesses interact with consumers and vice versa. According to a recent Accenture global survey among 1500 C-suite executives from companies across several industries, 84% of executives believe they will not achieve their growth objectives without scaling AI in their organization. Importantly, a large majority of the surveyed executives report that they struggle with how to scale it. Moreover, a recent McKinsey analysis of more than 400 actual use cases shows that marketing is the functional area in an organization where AI contributes the greatest value. Given the growing importance of AI in business and especially for marketing activities, it seems fundamental to understand how and where AI can be applied in the field of marketing to provide substantial benefits for companies (and consumers) and to stay competitive in the market. This course is designed to provide such understanding and help students to get an overview on how AI can be effectively used and applied in today's marketing contexts.


What topics will be discussed in this course?

The course introduces the students to the history and meaning of AI, how it evolved in marketing, what impact it has on today's marketing strategy to create value for both organizations and consumers, and what role data play for an effective AI implementation. The course discusses both strategic and operational elements of AI in marketing. For example, the followiong topics will be covered:

  • Strategic elements of AI
    • AI and marketing research
    • AI and market segmentation
    • AI and marketing automation (standardization, personalization)
  • Operational elements of AI
    • AI in product management (e.g., improving fit of product offering and consumer preferences)
    • AI in price management (e.g., automated pricing decisions)
    • AI in communication management (e.g., automated social media campaigns)
    • AI in sales management (e.g., sales forecasts)
    • AI in customer relationships (e.g., chatbots for customer support)
  • Psychological elements of AI (e.g., benefits and costs for consumers)
  • Ethical and privacy issues of AI
Description of the teaching methods
This course is delivered in an online learning format, integrating various lectures, materials, activities (e.g., online discussions), and guest talks. The class is designed to be highly interactive with a corresponding expectation that students engage in these interactions.
Feedback during the teaching period
Several feedback activities are included to increase the learning experience. For example, during a class, there will be several breakout sessions to give students the opportunity to debate cases and business examples of AI in marketing. Groups will then have the option to voluntarily present their findings in class and receive feedback either directly or collectively to allow students to learn from the discussion. Moreover, students will be able to perform learning check activities to reflect on the topics discussed throughout the course.
Student workload
Class teaching 38 hours
Readings and class preparation 120 hours
Exam and preparation 48 hours
Expected literature

Selected readings:

  • Dawar and Bendle (2018), Marketing in the Age of Alexa, Harvard Business Review, 96, 3, 80-86.
  • Iansiti and Lakhani (2020): Competing in the Age of AI, Harvard Business Review, 98, 1, 60-67.
  • Huang and Rust (2021): A Strategic Framework for Artificial Intelligence in Marketing, Journal of the Academy of Marketing Science, 49, 30-50.
  • Hagiu and Wright (2020): When Data Creates Competitive Advantage, Harvard Business Review, 98, 1, 94-101.
  • Kumar, Rajan, Venkatesan, and Lecinski (2019): Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing, California Management Review, 61, 4, 135-155.
  • Rodgers (2021): Themed Issue Introduction: Promises and Perils of Artificial Intelligence and Advertising, Journal of Advertising, 50 (1), 1-10.
  • Paluch and Wirtz (2020): Artificial Intelligence and Robots in the Service Encounter, Journal of Service Management Research, 4, 2-8.
  • Luo, Tong, Fang, and Qu (2019): Frontiers: Machines versus Humans: The Impact of AI Chatbot Disclosure on Customer Purchases. Marketing Science, 38, 6, 937-947.
  • Mende, Scott, van Doorn, Grewal, and Shanks (2019): Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses, Journal of Marketing Research, 56, 4, 535-556.
  • Puntoni, Walter Reczek, Giesler, and Botti (2021): Consumers and Artificial Intelligence: An Experiential Perspective, Journal of Marketing, 85, 1, 131-151.


Specific reading instructions will be given at the beginning of and throughout the course on Canvas.

Last updated on 08-02-2023