Customers' Clustering Analysis and Corresponding Marketing Strategies based on Improved SOFM ANN in e-Supply Chain

Abstract

Abstract According to the similarity of data, the huge number of customers' data in e-supply chain can be clustered objectively and scientifically, and those customers are be clustered into corresponding groups based on SOFM ANN (Self-Organizing Feature Map Artificial Neural Network). Through recognizing and analyzing the different features of these different groups, adopting corresponding marketing strategies can enhance customers' satisfaction, and moreover, can realize the e-supply chain's benefit maximization. In this article, three aspects of improvement are made in the SOFM ANN that applied in customers' clustering analysis; the sample data comes from the Google group. The result shows that the improved SOFM ANN's performance is considerably better than the traditional one's performance. Customers' clustering analysis and corresponding marketing strategies based on the SOFM ANN is a comparatively new topic. Therefore, the result of the research in the article is only for reference.