Study On Market Segmentation Method Of Logistics Customer Based On Business Intelligence

With economic development and accelerate the economic integration process, in urgent need of modern logistics in support of advanced information technology, "in logistics management, information comes first" has become a global logistics company consensus. And more and more the beginning of the logistics managers with business intelligence technology to logistics operators found problems in the process, through data collection, collation, mining, analysis, logistics enterprises at all levels within the organization to provide information to enhance business decision-making ability, speed of decision-making to ensure the accuracy of decision-making in order to find favorable logistics solutions.In this paper, the rapid development of China's logistics industry background, the absorption of the international logistics theory and advanced logistics experience in the practice of managers, mainly from the logistics customer needs analysis and business intelligence methods of market segmentation for logistics-related issues. Standing on the point of view of logistics enterprises to explore business intelligence based on the logistics problem of market segmentation.This article introduced the relevance of market segmentation study of the literature set the status quo, leads to a breakdown of the logistics market problems and the impact of factors, detailed information on the basis of market segmentation and the segmentation variables, and analysis on the advantages and disadvantages of the traditional methods. On this basis, put forward a business intelligence analysis of the clustering mining customers to introduce a logistics industry market segmentation applications, will Conjoint analysis and cluster analysis to link decision-making system as a whole to find out which attributes the effectiveness of the product expectations and the concept of attribute importance, through some necessary concepts and a hypothesis, to build customer segmentation models, and through the introduction of an example, combined with hierarchical clustering and the average sum of squares deviation method to verify the findings of a comparative analysis rationality and effectiveness.