Editing Market segmentation (section)

=== Post-hoc segmentation ===

In contrast, post-hoc segmentation makes no assumptions about the optimal theoretical framework. Instead, the analyst's role is to determine the segments that are the most meaningful for a given marketing problem or situation. In this approach, the empirical data drives the segmentation selection. Analysts typically employ some type of clustering analysis or structural equation modeling to identify segments within the data. Post-hoc segmentation relies on access to rich datasets, usually with a very large number of cases, and uses sophisticated algorithms to identify segments.<ref>Wedel, M. and Kamakura, W.A., ''Market Segmentation: Conceptual and Methodological Foundations,'' Springer Science & Business Media, 2010, pp 24-26.</ref>

The figure alongside illustrates how segments might be formed using clustering; however, note that this diagram only uses two variables, while in practice clustering employs a large number of variables.<ref>Constantin, C., "Post-hoc Segmentation using Marketing Research," ''Economics'', Vol 12, no 3, 2012, pp. 39–48.</ref>