Master
2020/2021
Data Science for Marketing Analytics
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type:
Elective course (Management and Analytics for Business)
Area of studies:
Management
Delivered by:
Department of Management
When:
2 year, 2 module
Mode of studies:
offline
Instructors:
Evgeny A. Antipov
Master’s programme:
Management and Analytics for Business
Language:
English
ECTS credits:
3
Contact hours:
24
Course Syllabus
Abstract
The course provides a unique training in applications of data science methods to marketing and customer analytics using real-world, as well as synthetic datasets. The course is intended for those who have taken at least an introductory course in statistics or econometrics. The focus of this course is on the most actionable data-driven approaches to support profit-maximization marketing decisions. During the first two weeks of the course students master advanced data management and supervised machine learning skills with the help of the DataCamp platform, where they are required to complete 3 small courses (free subscription is provided by the lecturer). This allows ensuring that the exposure of students to general technical skills is sufficient for mastering specific application and for being good at handling data and model-building. The second part is devoted to causal inference in marketing analytics and deals with various problems of assessing the incremental effects of marketing efforts using marketing mix, attribution modeling, and uplift modeling. The third part covers key predictive customer analytics models based on regression, classification and survival models.
Learning Objectives
- Manipulate large customer datasets
- Collect, store, process and analyze data according to high standards
- Conduct empirical analysis of customer data
- Develop and apply new research methods by combining and modifying existing techniques
- Solve CRM analytics problems using best practices of data analysis using modern computational tools
- Measure the effectiveness of marketing efforts
Expected Learning Outcomes
- Students will be able to prepare large datasets for analysis using R
- Students will master basic supervised learning techniques
- Students will be able to assess the incremental role of each marketing mix component
- Students will be able to evaluate the incremental role of each touchpoint in providing various marketing outcomes
- Student will master causal inference techniques for revealing the type of customers that are likely to have the largest treatment effect (uplift) if exposed to some marketing material
- Students will be able to build predictive models of various business outcomes using supervised learning methods
Course Contents
- Advanced data manipulations in R. Data exploration, taming, tidying and transformation.
- Marketing mix modeling. Ad stock variables. Modeling uncertainty.
- Attribution modeling. Model-based attribution.
- Uplift Modeling. Causal inference in Marketing. Generalized random forests.
- Regression models for customer analytics. Modeling Customer Lifetime Value.
- Classification models for customer analytics. Modeling responses, churn, purchase probability, etc.
- Survival models for customer analytics. Modeling time to reorder.
Interim Assessment
- Interim assessment (2 module)0.25 * Exam + 0.25 * In-class Assignments + 0.25 * Kahoot + 0.25 * Midterm exam based on Data camp
Bibliography
Recommended Core Bibliography
- Ledolter, J. (2013). Data Mining and Business Analytics with R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=587979
- René Michel, Igor Schnakenburg, & Tobias von Martens. (2019). Targeting Uplift : An Introduction to Net Scores (Vol. 1st ed. 2019). Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2247428
- Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl, K. C. (2017). Data Mining for Business Analytics : Concepts, Techniques, and Applications in R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1585613
- Venkatesan, R. (2014). Cutting Edge Marketing Analytics : Real World Cases and Data Sets for Hands On Learning. Upper Saddle River, N.J.: Pearson FT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1600652
Recommended Additional Bibliography
- Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics (Vol. Second edition). Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2093001
- Ole Nass, José Albors Garrigós, Hermenegildo Gil Gómez, & Klaus-Peter Schoeneberg. (2020). Attribution modelling in an omni-channel environment – new requirements and specifications from a practical perspective. International Journal of Electronic Marketing and Retailing, 1, 81.