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Discriminant Analysis for Marketing Research Applications

Part 2. Marketing Research

  1. Fred M. Feinberg

Published Online: 15 DEC 2010

DOI: 10.1002/9781444316568.wiem02029

Wiley International Encyclopedia of Marketing

Wiley International Encyclopedia of Marketing

How to Cite

Feinberg, F. M. 2010. Discriminant Analysis for Marketing Research Applications. Wiley International Encyclopedia of Marketing. 2.

Author Information

  1. University of Michigan, Ann Arbor, MI, USA

Publication History

  1. Published Online: 15 DEC 2010


Among marketers' main tasks is segmentation: breaking consumers, products, and firms into meaningful groupings. Marketing data often appear in discrete buckets, like “light,” “medium,” and “heavy” users, and marketers need to understand and predict which consumers (or products, or firms, etc.) fit into which group. That is, they would like to use available information (predictor variables) help explain how, and perhaps why, these groupings come out the way they do. Common examples include distinguishing new versus returning customers, explaining which stores different consumers chose to shop at, using demographics to predict various kinds of shopping behavior, or how to entice “loyal” versus “switcher” customer groups. Multiple discriminant analysis (MDA) allows marketers to do several important things: distinguish among two or more known groups, using available predictor variables; classify new items into those known groups; verify whether there actually are significant differences across the groups; and test for which specific predictor variables best account for between-group differences. We illustrate the meaning and use of the linear discriminant function in marketing applications, as well as how managers can interpret DA output to make better segmentation decisions. Connections to other methods included in this volume are highlighted throughout.


  • cluster analysis;
  • decision-making;
  • discriminant analysis;
  • econometrics;
  • marketing research;
  • marketing;
  • segmentation;
  • statistical methods