Big Data Based Marketing Analytics

Master 2024/2025

Type: Elective course (Business Analytics and Big Data Systems)

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Veronica Pisarenko

Master’s programme: Business Analytics and Big Data Systems

Language: English

ECTS credits: 6

Full 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 testing
Each 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 cases
Mini 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 cases
Mini 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 module
0.1 * 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.

Authors

Beklarian Armen Levonovich
PISARENKO VERONIKA VADIMOVNA

Course Syllabus