The Research Of Marketing Channel Risk Identification And Its Alarming System

With the rapid development of the marketing and the homogeneity of the product, the market gradually transforms from the product-driven to the channel-driven, and the importance of the marketing channel becomes more and more obvious, and channel becomes the key factor of the competition among enterprises. In the competitive market, who have mastered the channels will master the initiative. To maintain the stability and unimpeded of the channel become an unavoidable topic of the enterprise.This thesis researches on the risk of marketing channel, based on the marketing channel management theory, risk management theory and risk early-warning theory, analyzes the marketing channel risk and the inducement roundly. Then, an index system of risk early-warining theory was established, finally achieves successful results.First of all, this thesis analyzes the development of marketing channel management and risk management comprehensively, including the development history of current research focus, and development trend of both marketing channel management and risk management.Secondly, this thesis analyzes the inducement factors of marketing channel management, and proposes the study hypotheses of inducement of risk. Based on the hypotheses, survey questionnaire has been designed. Through making statistic analysis on the results of questionnaire, rationality of hypotheses of inducement of crisis has been estimated. After eliminating and categorizing the inducement factors, inducement factors system has been proposed.According to the index system design strategy, this thesis indexes the inducement factor of marketing channel risk on the basis of scientific analysis of inducement factors of marketing channel. Besides, every index has been discussed and the early-warning index system of marketing chain risk has been established.Finally, early-warning model of marketing chain risk has been created based on support vector regression, which is a variant of support vector machine. Support vector machine is based on statistics learning theory and structure risk minimization. They represent special advantages when solving the pattern recognition problems with finite training set, non-linear and high dimension. In the end, this thesis choose the Shengxiang group Co., Ltd to verify the early-warning model.