Магистратура
2024/2025
Наука о данных в маркетинговой аналитике
Статус:
Курс обязательный (Аналитика данных для бизнеса и экономики)
Направление:
38.04.01. Экономика
Кто читает:
Департамент менеджмента
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Снарский Ярослав Александрович
Прогр. обучения:
Аналитика данных для бизнеса и экономики
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
The course trains students to apply data-science methods to marketing and customer analytics on real and synthetic datasets, with a focus on decisions that maximize incremental profit under modern measurement constraints (privacy regulation, deprecation of third-party identifiers, attribution loss). The course is built around three pillars:
• (1) Experimentation and causal inference as the foundation of measurement;
• (2) Predictive customer analytics — customer lifetime value, churn, response, time-to-event;
• (3) Personalization and decision systems — uplift targeting, recommendation, contextual bandits.
The working stack is Python-first (pandas, scikit-learn, statsmodels, econml, causalml, pymc-marketing, lifetimes, lifelines, scikit-survival), with R used selectively where it remains best-in-class (notably Meta Robyn for MMM). All assignments are submitted as reproducible Git repositories. Prerequisites: an introductory course in statistics or econometrics and basic programming experience.
Learning Objectives
- • Design and analyze marketing experiments under realistic operational constraints, including variance reduction (CUPED), stratification, sample-ratio-mismatch detection, and sequential testing. • Build and evaluate causal and predictive models that drive marketing decisions, choosing the appropriate method for the available identification strategy. • Measure the incremental effect of marketing actions across channels, using both attribution methods and incrementality-based approaches (geo-experiments, synthetic control). • Develop predictive models of customer behaviour — lifetime value, churn, response probability, time to next event — and translate them into targeting decisions. • Design and evaluate personalization systems that combine uplift estimation, recommendation, and bandit-style exploration. • Produce reproducible analyses (Git, environment management, code review) that meet industry standards for data-science work.
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.
Interim Assessment
- 2024/2025 2nd module0.25 * Final Exam + 0.25 * In-class Assignments + 0.25 * Quizzes + 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
Recommended Additional Bibliography
- 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.