Магистратура
2024/2025
Маркетинговая аналитика на основе больших данных
Статус:
Курс по выбору (Бизнес-аналитика и системы больших данных)
Направление:
38.04.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Писаренко Вероника Вадимовна
Прогр. обучения:
Бизнес-аналитика и системы больших данных
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
Today’s marketing managers need to be able to use big data analytics for better understanding of the consumer purchase journey, and therefore, improve the effectiveness of their marketing strategy by offering innovative products and enhanced consumer experience. On the contrary, the data specialists nowadays have to demonstrate deep knowledge of business context (possess T-shaped skills) to cope with marketing issues. This course unlocks a huge range of quantitative techniques to expand students’ experience in marketing analytics. Students are exposed to a range of statistical tools and techniques, from classical statistical tools to emerging big data techniques. The emphasis is not on formulae of statistical tools, but on application and interpretation of a range of statistical techniques to help answer marketing-related questions. The course is organized around daily marketing problems. Moreover, widely used software (Python/R, Microsoft Excel) is used to implement the analysis. These arrangements ensure that the knowledge and skills the students learn from this course are work-ready for a wide range of business, from local small business to multinational giants. In the course, students are strongly encouraged to start thinking as marketers by asking questions of their data, setting their own direction for the analysis in the project and thinking about how a company could utilise the results in practice.
Learning Objectives
- The key objective is to develop capabilities of the students in using advanced analytical tools and techniques to address various marketing problems and to help better decision-making in sales and marketing
Expected Learning Outcomes
- Choose appropriate data sources and select appropriate analytical tools to solve a marketing problem and design a sophisticated analytical study
- Competently and confidently communicate the analytical research findings
- Demonstrate an ability to organize work in teams
- Gain an overview of marketing analytics frameworks, methods and tools
- Translate the output from analyses into managerial insights that are understandable to marketing managers
- Use analytical tools and methods to analyze a variety of marketing data
- Use descriptive statistics and predictive models to interpret and forecast different marketing phenomena
Course Contents
- Introduction to Marketing
- Data in marketing
- Marketing analysis and research methods
- Dynamic Pricing
- Customer lifecycle management and marketing (CLM)
- Product marketing
- CRM
- Analytics in Digital marketing
- Influence and reputational marketing
- Personalization in Marketing
Assessment Elements
- A/B testingEach team should analyze a dataset containing customers' responses on a digital campaign and conclude whether the campaign was successful or not and calculate the financial effect (sales uplift) using Python.
- Class and home mini casesMini cases are based on the materials discussed during the classes and can be assessed p2p or by the lecturer.
- Activity during the classes (participation in discussions)
- Exam
- Customer segmentation (made in groups)Each group should analyze a dataset containing customers (customers’) demographic data, transaction activity and other behavioral attributes using statistical tools and perform customer segmentation using Python. The description of the segments and the suggestions on promo campaigns for each segment are needed.
- Analysis of the buzz around the brand (group project)Is done in teams of 4-6 members. Students must prepare a presentation with the project results, Colab notebook with the solution (Python program code, visualizations if needed), comments and summary.
- Class and home mini casesMini cases are based on the materials discussed during the classes and can be assessed p2p or by the lecturer.
- Activity during the classes (participation in discussions)
Interim Assessment
- 2024/2025 2nd module0.1 * A/B testing + 0.05 * Activity during the classes (participation in discussions) + 0.05 * Activity during the classes (participation in discussions) + 0.2 * Analysis of the buzz around the brand (group project) + 0.05 * Class and home mini cases + 0.05 * Class and home mini cases + 0.1 * Customer segmentation (made in groups) + 0.4 * Exam
Bibliography
Recommended Core Bibliography
- Buttle, F., & Maklan, S. (2019). Customer Relationship Management : Concepts and Technologies (Vol. Fourth edition). London: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1983276
- Hemann, C., & Burbary, K. (2013). Digital Marketing Analytics : Making Sense of Consumer Data in a Digital World. Que Publishing.
- Jack J. Phillips, Frank Q. Fu, Patricia Pulliam Phillips, & Hong Yi. (2021). ROI in Marketing: The Design Thinking Approach to Measure, Prove, and Improve the Value of Marketing. McGraw-Hill Education.
- Kotler, P., Kartajaya, H., & Setiawan, I. (2017). Marketing 4.0 : Moving From Traditional to Digital. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1424256
- Tommy Blanchard, Debasish Behera, & Pranshu Bhatnagar. (2019). Data Science for Marketing Analytics : Achieve Your Marketing Goals with the Data Analytics Power of Python. Packt Publishing.
Recommended Additional Bibliography
- 9781789531237 - Fatouretchi, Max - The Art of CRM : Proven strategies for modern customer relationship management - 2019 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=2143915 - nlebk - 2143915
- John Goodman. (2014). Customer Experience 3.0 : High-Profit Strategies in the Age of Techno Service. AMACOM.
- 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
- Nicholas Papagiannis. (2020). Effective SEO and Content Marketing : The Ultimate Guide for Maximizing Free Web Traffic. Wiley.