Publication: Approaches to customer segmentation
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KU Authors
Co-Authors
Keiningham, Timothy L.
Cooil, Bruce
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Embargo Status
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Abstract
Customer 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.
Source
Publisher
Taylor and Francis
Subject
Business administration
Citation
Has Part
Source
Journal of Relationship Marketing
Book Series Title
Edition
DOI
10.1300/J366v06n03_02