MAR 6669 Spring 2023

Marketing Analytics 2 (R)

Overview | Course Calendar | Lecture Schedule

Overview

Data and analytic methods are driving forces of modern marketing decisions. This course is an introduction to both principles and practice of marketing data analysis. We will study statistical and data science techniques to gain insights into consumer behaviors and make informed decisions about marketing strategies. In the course, we will complete all the analytics process steps. At the end of this course, you will be familiar with how to collect and visualize different types of data, analyze data with statistical and machine learning methods, and turn the numerical results into marketing actions.

We will study marketing actions such as prediction, segmentation, recommendation, and targeting. You will learn skills to deal with different data types such as tabular data, sequential data, panel data, and unstructured text data. This course prepares you with machine learning skills such as supervised learning, unsupervised learning, reinforcement learning, and natural language processing algorithms for analyzing marketing data.

Course materials

For course policies, course requirements, and grading policies, please see the syllabus [link].

Lecture notes and assignmnets are at Canvas [link].

Instructor and Office Hours

  • Instructor: Mingzhang Yin

    • Course Calendar

    • Lecture: Mon/Wed 4:05 PM - 6:00 PM
    • Location: Hough Hall 340
    • Google Calendar: The Google Calendar below ideally contains all events and deadlines for student's convenience. Please feel free to add this calendar to your Google Calendar by clicking on the plus (+) button on the bottom right corner of the calendar below. Any adhoc changes to the schedule will be visible on the calendar first.

    Lecture Schedule

    The Schedule is subject to change.

    For each lecture, please choose one reading from the reading column.

    MRA = R for Marketing Research and Analytics by Chapman & Feit[link]
    HMA = Handbook of Marketing Analytics by Mizik & Hanssens [link]
    ISL = An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani [link]

    Date Topic Readings

    Lecture 1

    03/06

    Class logistics; Introduction to marketing analytics

    Lecture 2

    03/08

    Elements of machine learning; Predictive modeling; K-nearest neighbors

    ISL Sec. 2.1

    Lecture 3

    03/20

    Churn management; Logistic regression

    ISL Sec. 4.3

    Lecture 4

    03/22

    Market segmentation; Clustering

    ISL Sec. 12.4

    Lecture 5

    03/27

    Recommendation System; Content-based method; Collaborative filtering; Matrix factorization

    ISL Sec. 12.5.2

    Lecture 6

    03/29

    A/B testing; Multi-armed Bandits

    HMA Sec. 2

    Lecture 7

    04/03

    Data compression; Multidimensional scaling; Principal components analysis

    ISL Sec. 12.2

    Midterm

    04/05

    In class

    Lecture notes 1-6

    Lecture 8

    04/10

    Principal components analysis cont.; Factor Analysis

    Chapter 16 of https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/

    Lecture 9

    04/12

    User-generated content; Text analysis

    HMA Sec. 20

    Lecture 10

    04/17

    Topic model

    Tutorial http://www.cs.columbia.edu/~blei/talks/Blei_ICML_2012.pdf

    Lecture 11

    04/19

    Causal inference; Uplift modeling

    HMA Sec. 6

    Lecture 12

    04/24

    Panel Data; Diff-in-diff

    Chapter 10 https://bookdown.org/paul/applied-causal-analysis/

    Final project

    04/26

    Take home

    Lecture notes and code 1-12

    Acknowledgements

    The course materials are adapted from the related courses offered by Jim Hoover, Kathy Li, Jason Duan, and David M. Blei.