You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Joint Segmentation on Distinct Interdependent Bases with Categorical Data
Venkatram Ramaswamy, Rabikar Chatterjee and Steven H. Cohen
Journal of Marketing Research
Vol. 33, No. 3 (Aug., 1996), pp. 337-350
Published by: American Marketing Association
Stable URL: http://www.jstor.org/stable/3152130
Page Count: 14
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
The authors discuss a latent class framework for market segmentation with categorical data on two conceptually distinct but possibly interdependent bases for segmentation (e.g., benefits sought and usage of products and services). The joint latent segmentation model explicitly considers potential interdependence between the bases at the segment level by specifying the joint distribution of latent classes over the two bases, while simultaneously extracting segments on each distinct basis. An EM algorithm is used to estimate the model parameters. The authors present an empirical application, using pick-any data collected by a regional bank on two popular, conceptually appealing, and interdependent bases for segmenting customers of financial services-benefits (i.e., desired financial goals) and product usage (of an array of banking services). A comparative evaluation of the approach on synthetic data demonstrates the ability of the modeling framework to detect and estimate the interdependence structure underlying the two segmentation bases and thereby provide more accurate segmentation than "traditional" (single-basis) latent segmentation methods.
Journal of Marketing Research © 1996 American Marketing Association