Market Segmentation

Market Segmentation

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Targeting a segment of the market can be a powerful strategy. It's the concentration of marketing effort to dominate a market niche. Market segmentation is the process of identifying and targeting groups of individuals who are similar to one another. Markets can be segmented in many different ways: by product or service needs, by sensitivity to price, by geographic area, by demographic segment, or by psychographics and lifestyles. Successful segmentation depends on understanding what consumers need, how groups of consumers differ from one another, and how consumers decide among products.

Decision Analyst's Advanced Analytics Group searches for and identifies patterns in the data. Rigorous analytic techniques (including factor analysis, discriminant analysis, k-means and hierarchical clustering, latent class segmentation, and Factor Segmentation™) are used to organize consumers into groups with similar attitudes, needs, and desires. The size and market potential of each psychographic segment is determined, along with the positioning and appeals that should be employed to reach each segment.

Segmentation Methods

* Factor Segmentation™. Factor Segmentation™ begins with factor analysis (hence, the name). The model segments the respondents on a mutually exclusive basis (i.e., each respondent is assigned to one segment only) and may be followed by segmenting on a nonmutually exclusive basis to examine the overlap among segments. Factor Segmentation™ yields coherent clusters of respondents with very similar attitudes and perceptions, and is an important technique in developing targeting, positioning, and marketing strategies.

* K-means Cluster Analysis. K-means cluster analysis attempts to identify relatively similar groups of respondents based on selected characteristics, using an algorithm that can handle large numbers of respondents. This procedure attempts to identify similar groups of respondents based on selected characteristics.

* TwoStep Cluster Analysis. This procedure is relatively new. It uses hierarchical cluster analysis and is designed to handle very large data sets. The algorithm employed by this procedure has several desirable features that differentiate it from traditional k-means clustering techniques: the handling of categorical and continuous variables, and automatic selection of the number of clusters. By comparing the values of a model-choice criterion across different clustering solutions, the procedure can automatically determine the optimal number of clusters.

* Latent Class Cluster Analysis. Latent class cluster analysis produces an objective segmentation solution that optimizes the number of clusters and the fit of the segmentation model