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2020/2021  BA-BHAAV6032U  AI for Sales Management

English Title
AI for Sales Management

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc in Economics and Business Administration
Course coordinator
  • Michel Van der Borgh - Department of Marketing (Marketing)
Main academic disciplines
  • Management
  • Marketing
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 13-02-2020

Relevant links
Learning objectives
At the end of the course the student should be able to:
  • Describe the impact of Artificial Intelligence on the sales and service functions.
  • Explain how AI job replacement will occur in frontline settings.
  • Understand, reflect upon, and contrast different forms of AI that can be used in the frontline setting.
  • Apply the theoretical knowledge on AI and sales and service management to practical business cases.
  • Describe the different Machine learning and AI techniques used in sales.
  • Discuss the impact of AI on privacy, ethics, and biases.
  • Follow academic conventions in your written presentation.
Course prerequisites
None.
Examination
AI for Sales Management:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Duration 2 weeks 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
Course content, structure and pedagogical approach

Artificial intelligence (AI), manifested by machines that mimic certain aspects of human intelligence (HI), is increasingly utilized in sales and service and considered the most impactful source of change. For instance, robots for have automated many parts of our lives, virtual bots turn customer service into self-service, big data AI applications are used to replace portfolio managers, and social robots such as Pepper are used to replace human greeters to welcome customers in customer-facing services. These developments clearly show that we are in the fourth industrial revolution in which technology is blurring the boundary between the physical, digital, and biological spheres.

 

Although AI affects multiple business areas, its biggest impact pertains to marketing and sales. Following this, the goal of this course is to equip students with relevant knowledge for contributing to the academic discourse on AI and how it affects the sales and service functions within and across companies.

 

The course will prepare students who want to work with AI in different commercial settings in their future career. It provides the basis for development of knowledge and understanding of how AI is developed and utilized in sales and service settings and prepares the student for a challenging career in digitally enabled business environments.
Description of the teaching methods
The course uses blended learning: that is, we combine online material and lectures with in-class discussions and workshops. Blended learning (the mix of online and offline platforms) creates a powerful leaning environment for students, which we intend to use to its fullest potential.

The course consists of online lectures and materials, and online/offline case-based and general discussions. The class is highly interactive both with a corresponding expectation that students engage in these interactions.
Feedback during the teaching period
Quizzes are used to give students a better overview of whether they are following the expected learning curve. During tutorial sessions students will get feedback from peers and the teachers. In an extended classroom-teaching situation groups can voluntarily present their projects. At the end of the course a Q&A session is planned.
Student workload
Preparation 120 hours
Exam 50 hours
Teaching 36 hours
Expected literature

Indicative literature (more literature will be announced upon enrollment):

  • Brynjolfsson, E., & Mcafee, A. N. D. R. E. W. (2017). The business of artificial intelligence. Harvard Business Review.
  • Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
  • Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
  • Singh, J., Flaherty, K., Sohi, R. S., Deeter-Schmelz, D., Habel, J., Le Meunier-FitzHugh, K., ... & Onyemah, V. (2019). Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions. Journal of Personal Selling & Sales Management, 39(1), 2-22.
  • Robinson, S., Orsingher, C., Alkire, L., De Keyser, A., Giebelhausen, M., Papamichail, K. N., ... & Temerak, M. S. (2019). Frontline encounters of the AI kind: An evolved service encounter framework. Journal of Business Research.
  • De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline Service Technology infusion: conceptual archetypes and future research directions. Journal of Service Management, 30(1), 156-183.
  • Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2019). From data to action: How marketers can leverage AI. Business Horizons.
  • Hagen, David, and Rick Stefanik. "Artificial intelligence dialogue processor." U.S. Patent Application 10/852,300, filed January 13, 2005.
Last updated on 13-02-2020