External validity of market segmentation methods

Abstract

Purpose The purpose of this paper is to compare and validate the results of two clustering methods for the segmentation of the market for prestige cosmetics in Korea, and to draw conclusions about their general practical usability. Design/methodology/approach Segmentation schemes based on scaled data collected by questionnaire from 480 female shoppers, selected by the mall intercept procedure, were analysed for validity, model fit, definability of profiles, and usability of results. Findings Segmentation by traditional K‐means clustering was not judged useful, whereas segments generated by the innovative alternative of mixture regression modelling had clear marketing strategy potential. Research limitations/implications Given the single‐country and single‐market context of the study, its outcomes and implications must be generalised cautiously. Practical implications Mixture regression can make a significant contribution to the implementation of segmentation strategies based on deliverable consumer benefits, by helping academics and practitioners to better understand, explain and predict patterns of consumer behaviour. Originality/value A segmentation model with proven validity offers a sound basis for such marketing strategies as, for example, positioning.