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Method of Enterprise Marketing Risk Early-warning and The Index System Construction

机译:企业营销风险预警方法与指标体系构建

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The avoiding of marketing risk is the critical safeguard for the existence and development of enterprises, it is also an important content in modern business management. The risk early-warning indexes act as the carriers of the information in enterprise marketing activities and take the duty of providing the information for the enterprise marketing early-warning system. The index is also the basis and core of effective operation in the system. The index system describes a comprehensive goal, it aims to seek a group to have qualitative representative and at the same time it can comprehensively reflect the synthesis goal and the requested characteristic index from various aspects, these indexes and the combinations can appropriately express the synthesis judgment of people, that is also the purpose of the establishment of the index system. The article had analyzed the enterprise marketing risk influence factor from the interior and exterior perspective. It constructed the risk early-warning index system by using the system analysis and the AHP method, rejected the small technical progress index factor through the principal components analysis method, and carried on the actual description of the concrete content of each index. Comparing with the existent index system, the index system constructed in this article is not only relatively succinct in exterior target, it increases the establishment of enterprise interior marketing risk index at the same time, this index system is relatively comprehensive, and in the end, using the BP artificial neural networks, it obtained satisfactory result
机译:规避营销风险是企业生存和发展的重要保障,也是现代企业管理中的重要内容。风险预警指标在企业营销活动中充当信息的载体,并负责为企业营销预警系统提供信息。索引也是系统有效运行的基础和核心。指标体系描述了一个综合目标,旨在寻找一个具有定性代表的群体,同时可以从各个方面全面反映综合目标和所要求的特征指标,这些指标和组合可以适当地表达综合判断力。对人来说,这也是建立指标体系的目的。本文从内部和外部的角度分析了企业营销风险的影响因素。通过系统分析和层次分析法,构建了风险预警指标体系,通过主成分分析法剔除技术进步指标较小的因素,并对各项指标的具体内容进行了实际描述。与现有的指标体系相比,本文构建的指标体系不仅在外部目标上相对简洁,而且在增加企业内部营销风险指标建立的同时,该指标体系较为全面,最终,使用BP人工神经网络,取得了满意的结果

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