HlNoV: A new model to improve market segment definition by identifying noisy variables

Although cluster analysis is the procedure most frequently used to define d ata-based market segments, it is not without problems. This research addres ses one of its major problems: the selection of the "best" subset of variab les on which to cluster. If this selection is not made carefully, "noisy" v ariables that contain little clustering information can cause misleading re sults. To help isolate potentially noisy variables prior to clustering, the authors discuss a new algorithm, the Heuristic Identification of Noisy Var iables (HINoV). They demonstrate its robustness with artificial data. In ad dition, the authors illustrate the potential of HINoV to yield more manager ially useful market segments (clusters) when applied to two real marketing data sets. Implementation of HINoV is straightforward and will help avoid a major problem in using K-means cluster analysis for market segment definit ion, as well as for other similar types of research.