Uncertainties: An investigation of aleatory and epistemic errors in market segmentation analysis

Abstract Despite the popularity of cluster analysis as a segmentation tool, its limitations continue to include the production of random solutions and the existence of uncertainties. This study aims to assist marketers in understanding the characteristics of festival goers based on music events in Malaysia. The present study investigates the existence and effect of uncertainties produced in cluster analysis results by using an artificial neural network (ANN). Four market segments are identified: the alarm hitter, the technology ticker, the plug puller, and the fuse blower. Error analysis results reveal that uncertainties may cause incorrect predictions. Academically, the limitations in existing market segmentation studies are highlighted by adding the process of ANN training and testing the segments generated from the cluster analysis. From the industry perspective, this approach introduces an important segmentation basis—technographic segmentation—to tap into the wired generation. Future research may extend this study and apply a nonprobabilistic neural network to eliminate the existence of errors in cluster analysis.