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.

摘要

尽管聚类分析作为一种分割工具很受欢迎，但它的局限性仍然包括随机解的产生和不确定性的存在。本研究旨在帮助营销人员根据马来西亚的音乐活动了解节日观众的特征。本研究使用人工神经网络 (ANN) 调查聚类分析结果中产生的不确定性的存在和影响。确定了四个细分市场：报警器、技术自动收报机、插头拔出器和熔断器。错误分析结果表明，不确定性可能导致错误的预测。在学术上，现有市场细分研究的局限性通过添加 ANN 训练过程和测试聚类分析生成的细分来突出。从行业的角度来看，这种方法引入了一个重要的细分基础——技术细分——以进入有线一代。未来的研究可能会扩展这项研究并应用非概率神经网络来消除聚类分析中存在的错误。