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Decision Sciences

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Market segmentation, Marketing research, Information theory, Mathematical programming, Marketing


State-of-the-art market segmentation often involves simultaneous consideration of multiple and overlapping variables. These variables are studied to assess their relationships, select a subset of variables which best represent the subgroups (segments) within a market, and determine the likelihood of membership of a given individual in a particular segment. Such information, obtained in the exploratory phase of a multivariate market segmentation study, leads to the construction of more parsimonious models. These models have less stringent data requirements while facilitating substantive evaluation to aid marketing managers in formulating more effective targeting and positioning strategies within different market segments. This paper utilizes the information-theoretic (IT) approach to address several issues in multivariate market segmentation studies. A marketing data set analyzed previously is employed to examine the suitability and usefulness of the proposed approach [12]. Some useful extensions of the IT framework and its applications are also discussed.


This is the accepted version of the article which has been published in final form at

An Information Theoretic Framework for Exploratory Multivariate Market Segmentation Research* Decision Sciences Jamshid C. Hosseini Robert R. Harmon Martin Zwick DOI: 10.1111/j.1540-5915.1991.tb01289.x



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