A decision tree approach to modeling the private label apparel consumer

Purpose – The purpose of this paper is to profile the private label apparel consumer using demographic and behavioral predictors. The paper also aims to examine cross‐shopping behaviors among purchasers of private label apparel across the five top US private label apparel retailers. Design/methodology/approach – Decision tree analysis is used to model the impacts of demographics and behaviors on private label purchasing. A secondary database ( n =1,289) of US private label purchasers provides data for the analysis. Findings – Findings indicate demographic predictors as important drivers of private label apparel purchase among retailers positioned as providers of value, while behavioral drivers are more common among patrons of retailers that are differentiated on service or brand. Cross‐shopping is more common among the retailers positioned on value. Research limitations/implications – The research design provides a profile of the private label consumer but does not explain why this consumer chooses private labels over national brands. The data‐mining approach provides an innovative tool for identifying the drivers of private label consumption. Future research should investigate these drivers more deeply, to establish a fuller understanding of this consumer. The sample is limited to US consumers. Practical implications – Findings suggest that retailers positioned on value/low price need to differentiate private labels to deter cross‐shopping. Likewise, comparatively upscale retailers need to continue to be sensitive to the behavioral demands of their respective target market. Originality/value – Results provide a profile of the private label consumer and offer insight into private label cross‐shopping using an innovative modeling approach that facilitates examination of actual purchase data.