Improved Multi-index Customer Segmentation Model Research

Abstract

Customer segmentation helps the company's strategy formulation and competitiveness enhancement. To better meet customer needs and preferences, companies must recognize the differences of customers and formulate precise marketing strategies. This article focuses on the current customer segmentation background and combines Data mining tools, proposed a multiindex customer segmentation model. Considering the micro and macro perspectives, the traditional indicators are refined, and new segmentation indicators are added. The indicators are weighted by the entropy method. To reduce the time complexity of clustering, factor analysis is used to reduce the data dimension. Finally, the improved K-means clustering algorithm is used to optimize the determination of the K value and the selection of the initial center point to determine the customer segmentation results. The empirical research results on the segmentation of a retailer's membership data show that the improved algorithm is superior to the classic customer segmentation method in terms of clustering compactness and feature division capabilities. With this, it can help companies to improve the level of customer relationship management and the quality of decision-making.