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Capturing and Evaluating Segments: Using Self-Organizing Maps and K-Means in Market Segmentation

並列摘要


Market segmentation is a vital part of an organization's marketing because it provides the fundamental framework necessary for effective marketing efforts. In recent years, due to their high performance in engineering, artificial neural networks have also been applied in management research. Self-organizing maps, a technique of unsupervised neural networks, are often used for clustering or dimensional reduction. This study employs a modified two-stage approach (SOMs and K-means) to group customers, compares the performance between the tandem approach and direct K-means clustering, and tests for the existence of clusters and segments. The test results show that a media promotion variable would be a basis for segmentation. Based on the segmenting results, a marketing communication strategy is presented to cope with customers' expectations.

被引用紀錄


Wu, Y. C. (2015). 消費者 在虛擬品牌社群參與與再購意願和網路口碑關係之研究 [master's thesis, Feng Chia University]. Airiti Library. https://doi.org/10.6341/fcu.M0215214
白宸瑋(2010)。以資料探勘發掘行動加值服務市場之消費者特徵〔碩士論文,長榮大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0015-2007201013555800

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