Department of Business Administration2024-11-0920071533-266710.1300/J366v06n03_022-s2.0-67650291074http://dx.doi.org/10.1300/J366v06n03_02https://hdl.handle.net/20.500.14288/14819Customer segmentation has virtually unlimited potential as a tool that can guide firms toward more effective ways to market products and develop new ones. As a conceptual introduction to this topic, we study how an innovative multi-national firm (Migros Turk) has developed an effective set of segmentation strategies. This illustrates how firms can construct novel and inventive approaches that provide great value. A-priori, and custom designed post-hoc methods are among the most important approaches that a firm should consider.We then review general approaches to customer segmentation, with an emphasis on the most powerful and flexible analytical approaches and statistical models. This begins with a discussion of logistic regression for supervised classification, and general types of cluster analysis, both descriptive and predictive. Predictive clustering methods include cluster regression and CHAID (Chi-squared automatic interaction detection, which is also viewed as a tree classifier). Finally, we consider general latent class models that can handle multiple dependent measures of mixed type. These models can also accommodate samples that are drawn from a pre-defined group structure (e.g., multiple observations per household). To illustrate an application of these models, we study a large data set provided by an international specialty-goods retail chain. © 2007 by The Haworth Press, Inc. All rights reserved.Business administrationReviewhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-67650291074anddoi=10.1300%2fJ366v06n03_02andpartnerID=40andmd5=f94072b25a611042ef695c14a60e7196N/A10889