15.825 MARKETING DECISION SUPPORT
FALL 1998
Mon-Wed 1:00-2:30 E51-057
(Updated 9/8/98)
Description
A marketing information revolution has taken place in which large
databases, models, and supporting information technology play a major
role in many marketing decisions. The goal of the course is to give
students an understanding of these developments and hands-on
experiences with a number of them. We address four broad topics:
Scanner Data in Modern Brand Management
Data collected by scanning bar codes on grocery products has become
basic to understanding customer behavior and market trends in the
consumer packaged goods (CPG) industry. All major packaged goods
companies in the US acquire this data to support their marketing and
sales operations. Corresponding data is available in Europe, Japan,
and many other countries. Scanner data provides remarkably detailed
information on sales, share, and retail price for all the products of a
company and its competitors. In addition, the data-supplying companies
collect and deliver supplemental information on in-store displays,
weekly newspaper advertisements, and other marketing activities. The
suppliers also provide models and analyses to measure the effectiveness
of promotions conducted by supermarkets. These include newspaper
feature ads, in-store displays and temporary price cuts. Special
studies measure the effectiveness of TV advertising.
The course takes up the contemporary use of scanner data in packaged
goods marketing. We have access to commercial data and software and
will use it hands-on. Invited speakers from data suppliers and package
goods companies will describe the use of decision support tools. We
shall also read relevant papers from the literature, including current
developments that are likely to influence future industry practice.
Database Marketing
In the normal course of business, organizations such as banks, mutual
funds, credit card companies, hospitals, catalog marketers, and many
others accumulate large files of customer transaction data. The use of
such data to guide further communication with customers and potential
customers and to develop effective two-way relationships with them is
known as database marketing or relationship marketing. The techniques
of analysis are often called data mining. We shall review the field and
build customer-based product choice models to improve targeting and
sales response.
Marketing Models for Decision Making
Most marketing decisions have both a qualitative and quantitative component. Judgment, experience, and creativity play strong roles, as do data, models and analysis. Some consulting companies gain a competitive edge through their expertise in applying models and measurements to marketing problems. We shall review basic modeling approaches and their application to key decision areas such as price, promotion, advertising, distribution, and sales force. Emphasis is on models that have had an impact on practice. Outside speakers will describe modeling projects they have done. Assignments include readings and exercises on model building and use.
Decision Support and the Internet
Hey, the world is changing. Get with it, or get off. Everybody uses a browser. Let people receive what they need over inter/intranets. Post good stuff on your site and many people will come and get it. Or let them specify what they want delivered and push it out to them. Whole new clienteles can be reached inexpensively in a timely way; for example, sales forces can receive customer data just before making calls. In addition, the web is rapidly becoming a significant retailing channel, e.g., amazon.com, Internet Shopping Network, and Virtual Vineyards. But what about decision support for the customer?
Assignments: Readings and problem sets - including computer exercises using commercial data. Computer assignments will be done in teams of two or three people. Ten-page term paper.
Grading: 60% on problem sets, 25% on term paper, 15% on class participation.
Prerequisites: 15.810 and either 15.075 or 15.061 (or equivalents), or permission of the instructor.
Required text: Lilien, Gary L. and Arvind Rangaswamy (1998), Marketing Engineering: Computer Assisted Marketing Analysis and Planning, Addison Wesley, Reading, MA (book, tutorial, and CD).
Recommended books:
Blattberg, Robert C., Rashi Glazer, and John D.C. Little, eds. (1994), The Marketing Information Revolution, Harvard Business School Press.
Lilien, Gary L., Philip Kotler, and K. Sridhar Moorthy (1992), Marketing Models, Prentice Hall, Englewood Cliffs, New Jersey.
Berry, Michael J.A., and Gordon Linoff (1997), Data Mining Techniques, John Wiley, New York.