Please note this portion of the course description is from Spring 2016 and is for reference only. Spring 2018 course topics may differ.

COURSE NUMBER: MBA263-2

COURSE TITLE: Marketing Analytics

UNITS OF CREDIT: 3 Units

INSTRUCTOR: Przemek Jeziorski

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

PREREQUISITE(S): MBA 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

Please note this portion of the course description is from Spring 2016 and is for reference only. Spring 2018 course topics may differ.

ABSTRACT OF COURSE'S CONTENT AND OBJECTIVES:
"Marketing Analytics" addresses how to use customer information and the technology to process it (i.e. databases, analytics, computing systems) 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. For example, we will use sophisticated targeting models to increase marketing ROI in direct marketing campaigns. While we will use many quantitative methods in the course, the goal is *not* to produce experts in statistics. Instead, the goal is to train students to be able to comfortably interact with and manage a marketing analytics team.

Marketing is going through an evolution from having been primarily an art to becoming a science. This course teaches students a crucial part of the "science" approach to marketing. We will use a combination of lectures, cases, projects, and exercises to learn the material. This course takes a very hands-on approach and equips students with tools which can be used immediately on the job.

Frequently Asked Questions:

Q: "How does “Marketing Analytics” differ from "High-Tech Marketing"?"
A: The courses have no overlap. 'High-Tech Marketing' is about marketing high-technology products. "Marketing Analytics" is about using customer information and "technology" (i.e. databases, analytics, computing systems) to market to consumers.

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.

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.

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.