Применение искусственного интеллекта в маркетинге

Магистратура 2020/2021

Статус: Курс обязательный (Маркетинг)

Направление: 38.04.02. Менеджмент

Кто читает: Департамент маркетинга

Где читается: Высшая школа бизнеса

Когда читается: 2-й курс, 1, 2 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Ромеро Рейес Илякай Владиславовна

Прогр. обучения: Маркетинг

Язык: английский

Кредиты: 4

Контактные часы: 32

Full Syllabus

Abstract

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

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

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

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

Individual participation in the discussions and statistical game
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)
Exam

Interim Assessment

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

Bibliography

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

Course Syllabus