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Course 2021-2022 a.y.

20757 - CAUSAL INFERENCE FOR MARKETING POLICIES

Department of Marketing

Course taught in English

Go to class group/s: 31

CLMG (6 credits - II sem. - OP  |  SECS-P/08) - M (6 credits - II sem. - OP  |  SECS-P/08) - IM (6 credits - II sem. - OP  |  SECS-P/08) - MM (6 credits - II sem. - OP  |  SECS-P/08) - AFC (6 credits - II sem. - OP  |  SECS-P/08) - CLELI (6 credits - II sem. - OP  |  SECS-P/08) - ACME (6 credits - II sem. - OP  |  SECS-P/08) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/08) - EMIT (6 credits - II sem. - OP  |  SECS-P/08) - GIO (6 credits - II sem. - OP  |  SECS-P/08) - DSBA (6 credits - II sem. - OP  |  SECS-P/08) - PPA (6 credits - II sem. - OP  |  SECS-P/08) - FIN (6 credits - II sem. - OP  |  SECS-P/08)
Course Director:
MARTON VARGA

Classes: 31 (II sem.)
Instructors:
Class 31: MARTON VARGA


Mission & Content Summary
MISSION

Understanding the data and the causal relationships between variables are key elements in any well-designed marketing policy. Successful analysts know how to make sense of the data and how to derive meaningful inference from those. Successful managers know what type of analysis to ask for in order to raise profit and understand the methods data scientists and economists apply. After the course, students will be equipped with a toolkit with which they will be able to analyze the effectiveness of marketing campaigns and interventions.

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
  • Matching
  • Difference-in-differences
  • Endogeneity
  • 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
At the end of the course student will be able to...
  • 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
At the end of the course student will be able to...
  • 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

Teaching methods
  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Group assignments
DETAILS

Lectures

We will have interactive lectures during which students are encouraged to ask questions, add comments and participate actively.

 

Guest speakers

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.

 

Exercises

We will cover a variety of coding exercises in R, either in class or as home assignment.

 

Group assignments

Students will work in groups on home assignments and on their final project.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
  • Group assignment (report, exercise, presentation, project work etc.)
  • x    
  • Peer evaluation
  • x    
    ATTENDING STUDENTS

    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. 


    Teaching materials
    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
    Last change 22/06/2021 17:51