Though it's not widely known among segmentation analysts, having too many basis variables can work against the objective of finding well-differentiated segments. We tested three different variable selection strategies when facing a mix of quantitative and categorical basis variables and we found a clear winner among the strategies.

Many discussions of segmentation delve into a red herring topic about kinds of basis variables: should you segment on demographic variables, on psychographic or attitudinal variables, or maybe on needs or behaviors. Nothing at all, however, prevents us from segmenting our data on any combination of the above. In thinking about “types of segmentation,” it makes more sense to consider where the segmentation data comes from and how the segmentation variables relate to one another. Knowing these two things helps point us to the analysis methods that will best meet our study’s objectives.

Segmentation helps marketers understand how groups of customers differ with respect to the products, messaging or positioning that appeal to them. Understanding these differences gives marketers more leverage in designing or selling products to their customers. Unfortunately, formidable problems face the segmentation analyst. This paper describes what factors make segmentation difficult and what we can do to counteract those factors and produce successful segmentation despite them.

Cluster Ensemble Analysis is a relatively new technique to market researchers. The process involves combining existing cluster or segmentation solutions (an ensemble of candidate segmentation solutions) to produce a consensus solution that has stronger characteristics than any of the separate candidate solutions.

Retzer et al. from Maritz test different approaches to developing consensus solutions: Direct Approach, Feature Based Approach, Pair-Wise Approach, and the Sawtooth Software method as implemented in CCEA software. The authors conclude: "It's clear from the results that, for these data, the Direct and Sawtooth approaches outperform all others." They also demonstrate a graphical method to depict ensemble diversity. It's important that the candidate solutions in the ensemble be of good quality, and it is also important that they be diverse. The authors comment that the CCEA software "proved quite capable of handling the difficult and critical task of generating a diverse ensemble."

Convergent Cluster & Ensemble Analysis (CCEA) is software for doing cluster and cluster ensemble analysis. CCEA uses k-means as its standard cluster algorithm. However, the newer Ensemble Analysis included in the software is shown to produce better results for artificial datasets generated with known group membership. The procedure for the ensemble analysis is described in detail. Comparisons to k-means as provided by our previous CCA software are shown.