Data Science for Marketing Analytics

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент менеджмента

Статус:

Курс по выбору

Когда читается:

2-й курс, 1 модуль

Преподаватель

Антипов Евгений Александрович

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

• 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 assess the incremental role of each marketing mix component
• Students will be able to build predictive models of various business outcomes using supervised learning methods
• Students will be able to evaluate the incremental role of each touchpoint in providing various marketing outcomes
• Students will be able to prepare large datasets for analysis using R
• Students will master basic supervised learning techniques

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.

Assessment Elements

• In-class Assignments
• Kahoot
• Midterm exam based on Data camp
• Final Exam

Interim Assessment

• 2021/2022 1st module
  The final grade is the simple average of 4 components. As each component is evaluated on the 100-point scale (without rounding to the nearest integer), the final grade is first obtained as a continuous number on the 100-point scale (not rounded to an integer) and then converted to the 10-point according to the rules for converting the 100-point scale final grade to the 10-point scale: [95,100)=10 [90,95)=9, [80,90)=8, [70,80)=7, [60,70)=6, [50,60)=5, [40,50)=4, [30,40)=3, [20,30)=2, [10,20)=1, [0,10)=0

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.

Authors

• ANTIPOV EVGENIY ALEKSANDROVICH