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BUS 544: Marketing Analytics Consultancy Course
A current hot topic in the world of marketing is the use of information (big data) to address marketing
problems. The marketing analytics consultancy course is designed to provide students with the tools,
experience and communication skills necessary to address these current and future marketing projects.
The course is project focused so student’s specific experiences will differ based on their nature of the
project and industry they select but all groups will operate within the same basic structure. We will
begin the course with an emphasis on skill development. This will include coverage of common
statistical models, introduction to the SAS software and a general discussion related to the construction
of marketing models (models based on consumer psychology). The middle portion of the course will
consist of small group meetings between the students, the instructor (Mike Lewis) and firm
representatives. This material will be customized based on the nature of each unique project. For
example a CRM project might focus on determining linkages between marketing actions and customer
lifetime value while a dynamic pricing project might emphasize the estimation of price elasticity as a
function of environmental variables. The final portion of the class will focus on identifying valuable
insights, developing marketing decision support systems and communication of technical material to
managerial audiences. A goal of the course is to have students present their work at the annual Emory
Marketing Analytics Conference.
The following text is the document we have been using to initiate conversations between Emory and
sponsor firms. This information should provide further insight into the goals of the course and give a
sense of the types of projects. If students have any questions they should contact Mike Lewis at
[email protected].
Marketing Analytics Consultancy Course
We are currently in the planning stages for offering a course on Marketing Analytics for the Spring 2013
semester. Broadly, the goal of the course will be to provide students with direct experience on
marketing analytics projects. Our feeling is that Marketing Analytics is a topic that should be taught
using a combination of traditional coursework and more open ended projects. The consultancy course is
intended to provide a platform where students can apply their skills and training to interesting and
challenging real world projects. A secondary objective of the course is to provide a mechanism for
Emory MAC sponsors to identify talented students (and maybe future employees).
Course Structure
I envision the course as having three main components. The first element will focus on skill
development. Depending on the nature and requirements of a given project this may involve acquiring
skills with specific software packages such as SAS. We will also devote attention to making linkages
between consumer behavior theory and model building. The intention is to ensure that the analyses
conducted would go beyond data mining and would be suitable for guiding marketing decisions.
The second (and largest) segment of the course will focus on problem solving. We will begin by focusing
on data management issues. We will then identify and implement appropriate statistical models. As
noted above, we will emphasize the need for the models to be consistent with solid consumer behavior
theory and designed to support marketing decisions. The statistical portion of the class will be
customized to support each specific project.
The third portion of the class will focus on the conversion of the analytical work into managerial
wisdom. We will focus on how to communicate technical material to a more general audience. As part
of this component, we are also tentatively planning to have student groups present their findings at the
annual Marketing Analytics Conference.
Projects
The course will be able to support a wide range of projects. The following are examples of potential
project types:
Database Marketing

These projects would examine the relationship between marketing activities, customer
characteristics and customer response. For example, a catalog company might wish to
understand how shipping and handling fees influence order incidence and order size.
Customer Lifetime Value

There is currently great interest in forecasting the long-term value of firm’s customer
relationship assets. A basic project might create a CLV forecasting model. This information
could then be used to segment customers into different CLV categories. A more advanced
project would focus on understanding the link between marketing decisions and CLV.
Demand Forecasting

A common application of demand forecasting is sales promotion analysis. In these projects
student groups might examine market level demand as a function of a firm’s market activities.
For example, a retailer might be interested in understanding the profit implications of engaging
in various types of promotions.
Price Optimization

Dynamic pricing has become a hot topic over the past few years as firms have acquired the data
to infer micro level demand conditions and the means to customize prices. Applications of
dynamic pricing could range from determining the level of acquisition discounts needed to
acquire new customer, to a sports franchise interested in customizing prices based on opponent
quality.
Loyalty Program Analysis

Loyalty programs are both common and challenging to evaluate. The main analysis challenge is
that these programs may include incentives that have both short and long-term effects. A
loyalty program analysis project might focus on how the program design elements (reward
levels, reward thresholds, time requirements) change consumer behavior. A project could also
examine how loyalty program events such as earning a reward or cashing in a reward influence
customer loyalty.
Data
A strict requirement for the projects is that the students have access to appropriate data. Ideally, the
firm will make available data that includes measures of customer response and marketing activities. The
type of data needed will, of course, depend on the goals of the project. For example, a project focused
on the drivers of customer lifetime value will require fairly extensive customer transaction histories
while a price optimization project would require data on past prices and demand levels.
Faculty Role
The faculty role in the course will be to provide guidance and support to student groups. My
expectation is that students may be overwhelmed by the amount of data they are asked to work with
and may need significant support on the statistical analysis side. As the primary faculty member for the
course, I will make sure that groups overcome these challenges. In addition, I envision my role as also
providing support to the sponsoring firms. In particular, I am willing to play a very active role in terms of
defining projects and identifying appropriate types of data.