Please note this course description is from Spring
2016 and is for reference only. Spring 2018 course topics may differ.
COURSE NUMBER:
MBA263.1
This course
is cross-listed with the Evening-Weekend MBA program.
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
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.