20757 - CAUSAL INFERENCE FOR MARKETING POLICIES
Department of Marketing
Mission & Content Summary
During interactive lectures you will learn how to think about marketing analytics and how to write your own code. In the first half of the course we will learn the fundamentals of regression analysis, decision trees and the R language. In the second half of the course we will cover a variety of more complex methods for data analysis and prediction. Students will present the findings of a group project they designed and analyzed. We plan to have guest speakers who will give students a better understanding of what data skills and approaches are desired by companies.
Topics that can be expected during the course:
- Regression analysis
- Regression and decision trees
- Model assessment
- Random forest and Lasso
- Causal forests
- Models for individual level and aggregate level data
- Selected topics: e.g. online reviews, product search, recommendation systems
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
- Understand the importance of data analysis in marketing
- Have a good intuition what methods should be selected and applied in a given marketing context
- Be able to write their own computer code to analyze data
APPLYING KNOWLEDGE AND UNDERSTANDING
- Evaluate what type of analysis the company needs to do in a given setting
- Have a good overview about the major empirical tools industry experts and academics use
- Be able to present the findings of their own data analysis such that both experts and not experts can learn from that
- Develop recommendations to managers on what type of data needs to be collected
- Develop recommendations on what type of marketing campaign could be profitable in a given context
- Face-to-face lectures
- Guest speaker's talks (in class or in distance)
- Exercises (exercises, database, software etc.)
- Group assignments
We will have interactive lectures during which students are encouraged to ask questions, add comments and participate actively.
I aim to invite guest speakers who will share their experience on working in companies as managers or analysts, and who can give a good understanding to students regarding the expectations from employees.
We will cover a variety of coding exercises in R, either in class or as home assignment.
Students will work in groups on home assignments and on their final project.
Continuous assessment | Partial exams | General exam | |
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Attending students will be evaluated based on their group assignments and a final written exam.
Student groups with the best final presentations according to their peers can receive extra points.
The written exam includes questions referring to cases, talks and related concepts, models and tools presented and discussed in class. It assesses students’ ability to apply the methods learned in the course.
NOT ATTENDING STUDENTS
Not attending students will be evaluated based on a final written exam.
The questions are aimed at verifying the ability to apply the knowledge students learned when studying the teaching material.
ATTENDING AND NOT ATTENDING STUDENTS
- Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics.
- Békés, G., & Kézdi, G. (2021). Data Analysis for Business, Economics, and Policy. Cambridge University Press.
- Further readings (mostly academic papers) assigned to each class