2022/2023 DIP-D1FMV5022U Artificial Intelligence in Marketing
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Artificial Intelligence in Marketing |
Course information |
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Language | English |
Course ECTS | 5 ECTS |
Type | Elective |
Level | Graduate Diploma |
Duration | One Semester |
Start time of the course | Spring, Autumn |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Study board |
Study Board for Graduate Diploma in Business
Administration
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Course coordinator | |
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Teaching methods | |
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Last updated on 27-09-2022 |
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Learning objectives | ||||||||||||||||||||||||||||
On completion of this course, students will be
able to:
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||||||
Aim of the course:
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, 76% of the surveyed executives report that they struggle with how to scale it. Moreover, a recent McKinsey analysis of more than 400 actual AI use cases shows that marketing is the functional area in an organization where AI would contribute the greatest value. Given the growing importance of AI in business and especially for marketing activities, it seems fundamental for today’s marketers 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 a marketing context. 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.
Content and structure:
The course focuses on how and where AI can be applied in the field of marketing. Programming or methodological skills are not required for this course.
The course starts by introducing students to the basics of marketing and AI, followed by a discussion of how AI evolved in the marketing field over the last decades, and an illustration of the impact AI has on today's marketing to create value for both organizations and consumers. Students become familiar with the main tools AI relies on and learn what role accurate data play for an effective AI implementation. The course further concentrates on strategic and operational marketing elements and how AI can support these elements. For example, students gain an understanding of the importance of AI for marketing organization, marketing automation, and market segmentation. Moreover, by integrating several case studies, students learn how and where AI can be implemented and scaled in different marketing decisions, including product marketing (e.g., improving fit of product offering and consumer preferences), pricing (e.g., dynamic and intelligent pricing), communication (e.g., automated digital campaigns), sales (e.g., sales forecasts), and customer relationships (e.g., chatbots for customer support). The course also covers psychological elements of AI by examining consumers' interaction with AI and the benefits and costs it may have for consumers. |
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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 present their findings in class and receive feedback either directly or collectively to allow students to learn from the discussion. Moreover, every week, students will be able to perform learning check activities to reflect on the topics discussed throughout the course. A peer-graded group assignment with group feedback will give students additional opportunities for feedback. | ||||||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||||||
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