基于改进SOM神经网络的异网电信用户细分研究

Classification Study of Differential Telecom Users Based on SOM Neural Network

在对用户价值认知的基础上,电信运营商对用户进行正确分类是其了解用户的重要手段。电信运营商可以将用户分为不同的类别,并以此制定差别化服务政策,从而进行差异化营销来提高企业效益。本文首先对异网电信用户进行了细分研究,为提高分类的准确率,在传统自组织映射神经网络基础上,对学习速度和权重向量初始值的确定进行了改进,提出了改进的自组织映射神经网络;同时采用改进的自组织映射神经网络对某省电信运营商提供的用户数据进行仿真。仿真结果表明:改进的自组织映射神经网络在兼顾稳定性的同时,很好地解决了自组织过慢问题,提高了用户分类的准确率,大幅度减小误差。最后根据分类结果为电信运营商实施差异化营销提供了基本规则。

Based on the value cognition of consumers,it is a critical approach for telecom operators to classify consumers correctly in order to know them better.Telecom operators can divide the consumers into different categories and develop different service policies as well,so that they can advance the differentiated marketing to improve business benefits.Firstly,consumers of different telecom networks are classified in detail.In order to improve the classification accuracy,this research improves the determination of learning rate and the initial value’s weight vectors based on the traditional self-organizing map neural network,proposing an improved self-organizing map neural network(ISOMNN).Then this ISOMNN is applied to the simulation experiment by using the consumer data provided by a telecom operator of a province.The simulation results reveal that ISOMNN can not only take the stability into account,but also solve the slow self-organizing problem well,boosting the accuracy of consumer-classification and reducing errors significantly.Finally,based on the results of classification,this paper provides the basic principles for the telecom operators,which will help them to implement differential marketing.