企业营销决策支持系统应用研究

发布时间：2018-06-27 22:45

本文选题：决策支持 + 销售预测　；参考：《浙江理工大学》2017年硕士论文

【摘要】：随着我国信息技术的高速发展,企业要想快速提高其在市场中的核心竞争力,需要有效利用自身积累的海量数据来不断科学合理的制定各种决策。因此,研究能够辅助企业管理者进行科学决策的决策支持系统显得尤为重要。本文针对决策支持系统的国内外现状,对销售预测与客户细分两个核心内容进行了研究,研究内容如下:1)销售预测。预测是指在自身所掌握信息的基础上,按照一定的方法和规律对未来进行测算。准确的销售预测不仅有利于企业进行科学决策,而且还有利于企业获取最大利润。本文首先提出了选择对产品销量影响较大的因素作为BP神经网络模型的输入参数;然后针对BP模型难以确定隐含层结点个数、易陷入局部极值等问题,构建了遗传算法优化BP网络预测模型;最后,通过把本文构建的预测模型与未优化的BP模型进行比较和预测分析,证明了BP神经网络在遗传算法的优化下能够得到较高的预测精确率。2)客户细分。客户细分能够使企业更精准的把握客户群,为企业展开精准化营销提供决策支持,稳定核心客户,吸引新客户,最大限度的提高企业效益。本文从客户忠诚度和客户价值两个维度进行客户细分,构建了多维交叉客户细分模型。首先,客户忠诚度选择决策树方法基于“最近购买时间”、“购买金额”和“购买频次”,将客户分为流失客户、浮动客户与忠诚客户;然后,针对客户价值本文提出了近邻传播(Affinity Propagation)算法优化k-means算法对客户进行聚类分析,将客户分为低价值客户、普通客户、主要客户与高价值客户;最后,对两个维度进行交叉获得更精确化的客户细分模型,并针对每一类细分客户提供适当营销策略供决策者参考。
[Abstract]:With the rapid development of information technology in our country, if enterprises want to improve their core competitiveness in the market quickly, they need to effectively use the massive data accumulated by themselves to make all kinds of decisions scientifically and rationally. Therefore, it is very important to study the decision support system which can assist enterprise managers to make scientific decision. Aiming at the present situation of decision support system at home and abroad, this paper studies the two core contents of sales forecast and customer segmentation. The research contents are as follows: 1) sales forecast. Prediction is to measure the future according to certain methods and laws on the basis of the information we have. Accurate sales forecast is not only helpful for enterprises to make scientific decision, but also for enterprises to obtain maximum profit. In this paper, the factors that have a great influence on the sales volume of the product are proposed as the input parameters of BP neural network model, and then the BP model is difficult to determine the number of hidden layer nodes and is prone to fall into local extremum. The genetic algorithm optimized BP network prediction model is constructed. Finally, the prediction model constructed in this paper is compared with the unoptimized BP model. It is proved that BP neural network can obtain a high prediction accuracy rate of .2) customer segmentation under the optimization of genetic algorithm. Customer segmentation can make the enterprise grasp the customer group more accurately, provide decision support for the enterprise to launch precision marketing, stabilize the core customers, attract new customers, and maximize the efficiency of the enterprise. Based on customer loyalty and customer value, this paper constructs a multi-dimensional cross-customer segmentation model. First, the customer loyalty selection decision tree method is based on "recent purchase time", "purchase amount" and "purchase frequency", which divides customers into lost customers, floating customers and loyal customers. In this paper, Affinity propagation algorithm is proposed to optimize the k-means algorithm for customer value clustering analysis. Customers are divided into low value customers, ordinary customers, main customers and high value customers. The two dimensions are crossed to obtain a more accurate customer segmentation model, and appropriate marketing strategies for each type of subdivision customers are provided for the reference of decision makers.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274;TP18

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