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The Use of Artificial Intelligence in Marketing

Учебный год
Обучение ведется на английском языке
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль


Course Syllabus


This programme aims to bridge the gap between business and technology. The course is the first step for understanding the digital transformation strategy. Designed for masters in business & administration seeking to understand the possibilities of AI in marketing practices across multiple functions and industries. It is also applicable for technical professionals, IT managers, senior and middle managers and business analysts looking to better understand how AI can be implemented within organisations. During the course fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language will be introduced. The link to the MOOC course Python for Everybody: https://www.coursera.org/specializations/python#courses
Learning Objectives

Learning Objectives

  • To form applied skills of using artificial intelligence in business practices, for example, in building and analyzing marketing strategy, the product commercial creative of digital services.
  • To form the skills of an accessible explanation of marketing proposals to business clients.
  • To achieve the basic understanding of solving the business tasks via proposed technologies.
Expected Learning Outcomes

Expected Learning Outcomes

  • Knowing the basic concepts of artificial intelligence in business
  • Knowing the basic terms related to artificial intelligence
  • Knowing the Box Plot - statistical tool to reveal outliers
  • Knowing the transformation of the marketing mix due to the use of artificial intelligence
  • Knowing the advantages and disadvantages of clustering methods.
  • Can explain the possible set of methods for the given data types.
  • Knowing the aims of basic classification methods
  • Understanding Artificial Intelligence tasks
  • Knowing the key libraries in Python
  • Knowing and understanding the criteria for applying AI in business
  • Understanding the neural network learning algorithm
  • Knowing how to evaluate the quality of a neural network model
  • Knowing the Open Data Sources.
  • Undertanding the Web Scrabbing method
  • Can interpret the results
Course Contents

Course Contents

  • Artificial intelligence in marketing. Introduction
    The concept of artificial intelligence and its application in marketing. The transformation of the marketing mix with the emergence of artificial intelligence in business. AI-assisted tasks. Which data and resources are needed for effective implementation of AI? Development of artificial intelligence in the company as a strategic tool; competitive advantage.
  • Data verification
    AI problem statement. Deep learning neural networks. Verification algorithms for data provided by business sector. Qualities check methods of scientific and applied papers for using results in marketing strategies. An introduction to the Jupiter Notebook as a tool for generating tech analytics marketing reports and as a calculation tool using Python.
  • AI task # 1. Classification "A or B" and Clustering "How is it organized?"
    Data classification to reveal the increasing business profits strategies. Revealing the data structure for product diversification, as well as marketing insights for the brands communication strategies. Customer segmentation.
  • AI task # 2. Implicit outliers from the population sample "Is this strange?"
    Search for the data anomalies, followed by the formation of marketing insights for the brands communication strategies.
  • AI task # 3. Predictive models of the first type: "How much/many?"
    Predictive models for the formation of new products, including the digital industry.
  • Data Finding
    Open Data Sources. Web Scrabbing as a method of finding marketing data: variants, limitations, practice.
  • Artificial intelligence in business and society. AI in Marketing practices. Machine learning in business.
    The main directions of international and Russian regulation of AI applications. Ethical issues of AI application and development. Human-machine relationship. Responsibility issues. Planning and risk assessment of AI implementation in business. Case studies. Exploring the basic concepts of machine learning. Comparison of existing methods with the goals and objectives of a company and a marketing department. Opportunities of using machine learning methods.
Assessment Elements

Assessment Elements

  • non-blocking Individual participation in the discussions and statistical game
  • non-blocking Group project
    The group of 5-6 Students needs to apply their knowledge to solve a companies' task or business case,. As a result the group has to submit a report and present the results. Grading: 60% project grade + 40% individual performance during the project defence (questions and answers)
  • blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.4 * Exam + 0.42 * Group project + 0.18 * Individual participation in the discussions and statistical game


Recommended Core Bibliography

  • Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317
  • Metcalf, L., Askay, D. A., & Rosenberg, L. B. (2019). Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making. California Management Review, 61(4), 84–109. https://doi.org/10.1177/0008125619862256
  • Provost, F., & Fawcett, T. (2013). Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking (Vol. 1st ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=619895

Recommended Additional Bibliography

  • Harvard Business Review Press. (2019). Artificial Intelligence : The Insights You Need From Harvard Business Review. La Vergne: Harvard Business Review Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2003692
  • Harvard Business Review, David Weinberger, Tomas Chamorro-Premuzic, Darrell K. Rigby, & David Furlonger. (2020). The Year in Tech, 2021: The Insights You Need From Harvard Business Review. Harvard Business Review Press.
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910