Quantitative Marketing Fellows

Quantitative marketing is an approach to marketing that relies on computer based models and statistical, econometric and data mining methods to understand and analyze why, which, when and how much products and services are being bought by consumers and firms. The aim is to make better forecasts, to learn about new marketing opportunities, to enable managers to ask "what if" questions, and to make better decisions on pricing, segmentation, advertising and promotions, distribution, positioning, customer relationship management, and product and service design, among others.

The objective of this fellows program is to provide students with an understanding of the role of analytical techniques and computer models in enhancing marketing decision making. In this program, students will learn both the quantitative techniques used for conducting and analyzing custom design surveys as well as the quantitative techniques used for mining and extracting new knowledge from large scale commercial databases.

Dr. Diane Whitney and Dr. Wolfgang Jank are the Faculty Champions for this program, click their names to view their respective profiles.

Curriculum - 2 year program

  • BMGT 458I  (Marketing Intelligence) - Spring Junior year                                                       o The first couple weeks of this course will provide a basic framework for Quantitative Marketing. As such, this course will be designed to provide students with their first exposure to how computer based models are used to analyze data and make marketing decisions. This class will also be used to distinguish between survey data and large scale corporate databases so students understand that different statistical techniques/models are used depending on the type of data.                                                   o The remainder of the course will provide students with an understanding of the methods and hands-on experience analyzing large scale commercial databases. As part of this course, students will complete a series of data mining exercises and a comprehensive data mining project of a large scale marketing database, possibly from a corporate sponsor, and an in-class practicum.

  • Approved Internship - Summer after Junior year

  • BMGT 452 (Marketing Research) - Fall Senior year

Pre-requisites:

  • Business Statistics (BMGT230) - Freshman or Sophomore year

  • Principles of Marketing (BMGT350) - Fall Junior year

Approved Internship - Summer after junior year: Students are expected to obtain their own internship placement, and to submit a proposal which will be approved by the Faculty Champion. Proposals should be submitted to the Undergraduate Studies Office not later than February 15, for approval  by the end of February. Approved internships should involve data mining with marketing applications. Internships need not be for academic credit; however, in order to fulfill fellows program requirement, students will be required to submit a final project report and will present their work at a conference and reception in fall semester, senior year.  Alternatively, students may substitute BMGT402 during fall or spring semester senior year.

NOTE: BMGT 402 is recommended but not required

Co-curricular activities/events

  • In-class practicum in marketing intelligence
  • Optional summer internship
  • Lectures
  • Conference and reception

Fellows Application Eligibility

  • Must be a Business Major (preference for Marketing and IS majors)
  • Junior standing; rising juniors may apply
  • Minimum 3.0 cum gpa preferred
  • Grade “B+” or better in BMGT 230
  • Resume detailing work experience

Application & Selection Process

  • This Application is now closed. Applications for Fall 2009 cohort will open in Spring 2009.
  • You can work on your application in any order you choose
  • You can save your application as a draft (before making a final submission). Your saved application is located in your "Profile" under "My Applications" in UNet.   Please remember not to submit your application until you have finished entering all parts of your application .  You will only be able to submit this application once and are then unable to return to make edits.