SEMESTER: Spring 2020

COURSE NUMBER: EWMBA263-1 1

COURSE TITLE: Marketing Analytics

UNITS OF CREDIT: 3 Units

INSTRUCTOR: Przemek Jeziorski

E-MAIL ADDRESS: przemekj@haas.berkeley.edu

PREREQUISITE(S): EWMBA 206 Marketing

CLASS FORMAT: Mixture between lectures, cases, projects, and exercises

REQUIRED READINGS: Cases, course reader

BASIS FOR FINAL GRADE: Mixture between exercises, project, and class participation.

ABSTRACT OF COURSE'S CONTENT AND OBJECTIVES:
"Marketing Analytics" addresses how to use customer information and the technology to process it in order to learn about and market to individual customers.

Advances in the technology to process individual-level customer information have had major effects for Marketing. Many firms now possess much more information about consumers' choices and reactions to marketing campaigns than ever before. However, few firms have the expertise to intelligently act on such information. The goal of this course is to help students develop this expertise. Specifically, the course will teach what it takes to collect, analyze, and act on customer information.

The lectures would give you necessary skills to navigate data-driven decision making withing the modern organizations. Specifically, we will learn how to use Python to build and execute state-of-the-art data analytics, including: logistic regression, neural networks, machine learning, A.I. and deep learning. The course takes a very hands-on approach and equips students with tools which can be used immediately on the job. No prior programming and statistics experience is necessary.

In the today's world data-driven decision making is ubiquitous, thus, you should take this course even if do not plan career data science.

Frequently Asked Questions:

Q: "Do I have to know a lot of statistics to succeed?"
A: Absolutely not. While we will use statistics to analyze customer information and many of the assignments require you to use statistical techniques, all you need will be introduced in class with plenty of opportunity to get familiar with it.

Q: "Do I need to have experience with Python or any type of programming language?"
A: You do not need any prior programming experience. The lectures would take you step-by-step from launching the software, through building your first predictive model, to
developing cutting-edge neural network models.

Q: "How does "Marketing Analytics" differ from "Marketing Research"?"
A: "Marketing Research" is a broad course that introduces students to a variety of research methods, such as psychological measurement, research design, survey methods, experimentation, etc. In doing so, Marketing Research focuses strongly on collecting data about consumers to understand their overall preferences. In contrast, "Marketing Analytics" starts with the idea that you have a (potentially huge) database containing each individual customer and teaches you how to market to these customers using sophisticated techniques. The two courses complement each other very well. However, you don't need one to take the other.

BIOGRAPHICAL SKETCH:

Professor Jeziorski is an empirical economist specializing in industrial organization, regulation and quantitative marketing. His work spans across variety of topics including: mergers and acquisitions, on-line advertising, mobile banking systems and cancer prevention. Recently, Professor Jeziorski has been working with Gates Foundation to establish the impact of transaction cost and vendor lock-in on diffusion of mobile peer-to-peer payment systems in Africa.

In another project, Jeziorski works with Microsoft Research to describe an interaction between conventional branding and the efficacy of on-line sponsored search advertising. In particular, he describes that strong conventional brands do not experience so-called position effect, that is, ads of strong brands do not benefit from top advertising slots as much as ads of weak brands. As a consequence, strongly branded advertisers should have similar willingness to pay for top ad slots and for inferior ad slots, whereas weak brands should strongly prefer top slots.

Going beyond business applications of marketing analytics, Professor Jeziorski has been working with the cancer registry in Singapore to establish a causal impact of early breast cancer screening on cancer mortality rates.