Customer Segmentation

The goal of segmentation is to divide the total marketplace into subgroups whose members are similar to one another and different from members of other subgroups.   The key to conducting successful segmentation research is to define “similar” and “different” in terms that are relevant to the client’s business.  For example, a segmentation based on gender may create segments that are in some ways similar within and different across, but if men and women don’t have different purchase patterns in the client’s category, this segmentation will not be useful.  A gender-based segmentation in the fashion apparel category may be useful but a gender-based segmentation in the smart phone category may not.

MACRO believes there is no single best method to segment the marketplace.  We use several advanced analytic approaches and search for a segmentation solution that is most actionable.

Our primary tools for conducting actionable segmentation are:

Latent Class Choice Models (LCCM)

Using the raw choice data from a traditional choice-based conjoint exercise, an LCCM can be constructed.  Such an LCCM simultaneously divides the survey sample into segments and estimates parameters for the predictor variables (conjoint attributes) in choice models for each segment.  The resulting segmentation solution is virtually assured of having significantly different drivers or hot buttons across segments.

Decision Pathway Segmentation (DPS)

DPS is a multi-stage approach.  First, a Structural Equation Model is developed for the total marketplace.  This model will provide substantial insight into overall market structure.  The predictor variables from this model are then used as the segmentation basis in a traditional segmentation scheme, eg, Latent Class Cluster Analysis, SPSS’s Two-Step Cluster Analysis, Hierarchical Cluster Analysis, etc.  The resulting segments are more likely to have different drivers across segments than with traditional segmentation.

Cluster Ensemble Analysis

Cluster Ensemble Analysis (CEA) is the latest advancement in segmentation.  With CEA, multiple independent segmentation solutions are combined.  This “consensus” solution has been shown to be a more accurate reflection of the underlying market structure than any of the single segmentations on which it is based.  Additionally, classification algorithms have been shown to be more accurate when assigning subjects to the CEA segmentation solution than to any other segmentation solution.

An important conceptual benefit of CEA is that CEA, by virtue of building consensus across solutions, tends to keep the basic characteristics of the separate segmentations on which it is based.  If one segmentation, for example, was structured based on key drivers of purchase interest and another was based on demographics and media habits, the CEA solution would attempt to retain the fundamental patterns and structures of both.