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Магистратура 2021/2022

Маркетинговая аналитика на основе больших данных

Направление: 38.04.05. Бизнес-информатика
Когда читается: 2-й курс, 1, 2 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Прогр. обучения: 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

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

Expected Learning Outcomes

  • Gain an overview of marketing analytics frameworks, methods and tools
  • Choose appropriate data sources and select appropriate analytical tools to solve a marketing problem and design a sophisticated analytical study
  • 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
  • Translate the output from analyses into managerial insights that are understandable to marketing managers
  • Competently and confidently communicate the analytical research findings
  • Demonstrate an ability to organize work in teams
Course Contents

Course Contents

  • Introduction to Marketing
    The types of marketing. B2B and B2C marketing. The key marketing frameworks. Product launch framework. Strategic marketing framework. Data driven marketing concept, the role of analytics in marketing. The influence of Big Data on marketing frameworks
  • Data in marketing
    Types of data in marketing, data scales. Data sources overview. Data gathering methods. Data processing tools and methods
  • Marketing analysis and research methods
    Market analysis and segmentation. Customer segmentation, the types of customer segmentation. Cohort analysis. RFM segmentation. Products segmentation. ABC analysis. Data science methods for advanced segmentation
  • Dynamic Pricing
    Pricing objectives and strategies. Pricing frameworks. Dynamic pricing definition and benefits. Dynamic pricing using AI
  • Customer lifecycle management and marketing (CLM)
    Lifecycle models RACE, RADRA, ARICR. Marketing activities and instruments for each stage of the customer lifecycle. Unit economics definition and key metrics. The importance of unit economics. Customer Lifetime Value, CLV modeling. The Cost of Acquiring New Customers (CAC) analysis and forecasting. Customer churn prediction
  • Product marketing
    The product lifecycle. What is MVP and why marketers need it. The product launch methods and models. Customer journey map. User stories. Product metrics. Web and app analytics, web funnels. Conversions
  • CRM
    The definition of CRM, building blocks of CRM. Types of CRM in business. Loyalty programs analytics. Metrics for loyalty measurement. CRM automation using AI. CRM for real-time marketing, tools, CEPs
  • Analytics in Digital marketing
    The types of marketing channels. Omnichannel approach. SMM analytics. Paid Search, SEO and SEM analytics. Email marketing. Promotion strategy in digital channels. AI application in digital marketing. A/B testing and digital campaigns analysis. Campaign ROI forecasting
  • Influence and reputational marketing
    Brand management. Innovation diffusion framework. The influence marketing definition and benefits. The ways and tools to find the most appropriate influencer. Reputational marketing definition and the role in brand management. Reputational marketing tools. The role of AI in influence marketing and reputational marketing, the cases of application
  • Personalized marketing
    Goals and benefits of personalization. Ethical aspects of personalization. The role of recommendations in marketing. Recommender systems algorithms. RC metrics gathering optimization
Assessment Elements

Assessment Elements

  • non-blocking Activity during the classes (participation in discussions)
  • non-blocking Class and home mini cases
    Mini cases are based on the materials discussed during the classes and can be assessed p2p or by the lecturer
  • non-blocking Home task 1 (is done in groups of 3-4 students) - Customer segmentation
    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
  • non-blocking Home task 2 (is done in groups of 3-4 students) – A/B testing
    Each group 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
  • non-blocking Group project - building a recommender system
    Is done in teams of 3-4 members. Students have to prepare a presentation with the project results, Colab notebook with the solution (Python program code, visualizations if needed), comments and summary
  • Partially blocks (final) grade/grade calculation Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.1 * Activity during the classes (participation in discussions) + 0.1 * Class and home mini cases + 0.4 * Exam + 0.2 * Group project - building a recommender system + 0.1 * Home task 1 (is done in groups of 3-4 students) - Customer segmentation + 0.1 * Home task 2 (is done in groups of 3-4 students) – A/B testing
Bibliography

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

  • Don Peppers, & Martha Rogers. (2011). Managing Customer Relationships : A Strategic Framework: Vol. 2nd ed. Wiley.
  • 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
  • Max Fatouretchi. (2019). The Art of CRM : Proven Strategies for Modern Customer Relationship Management. Packt Publishing.
  • Nicholas Papagiannis. (2020). Effective SEO and Content Marketing : The Ultimate Guide for Maximizing Free Web Traffic. Wiley.