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基于聚类结果解释方法的客户群特征研究
Study on the Customer Group Characteristics Based on the Method of Clustering Results Explanation
【作者】 马岳猛;
【导师】 李宝家;
【作者基本信息】 哈尔滨工业大学 , 技术经济及管理, 2011, 硕士
【摘要】 市场经济的不断发展使得客户关系管理在企业经营管理中的重要性日益突出。客户细分作为客户关系管理的重要方法,可以将企业的客户进行有效划分,使企业能够根据不同的客户群特征制定相应的管理策略。在客户需求个性化和多样化的形势下,客户群特征的提取成为重要的研究课题。针对细分后客户群特征难以提取问题,提出了基于聚类结果解释的特征提取方法。该方法将因子分析与聚类分析相结合,利用因子分析具有提取可解释因子的作用,对客户细分指标进行特征因子的提取,并计算客户聚类后各个客户群的因子得分的平均值,以表格和图形的方式简洁表示计算的结果。根据各个客户群特征因子值的大小及特征因子包含的客户指标对客户群特征进行分析说明。对方法的适用性及优越性进行了比较分析,说明了该方法在客户群特征研究中的合理性及科学性。将提出的客户群特征分析方法应用于电信行业客户关系管理实证分析中,得到了最佳客户群及客户群特征因子值,对其进行分析不但能够对各个客户群特征清晰认识,解决了客户指标复杂化带来的客户群特征难以提取的问题;而且根据特征因子值的大小及特征因子包含的客户指标高度相关性原则,分析说明了各个客户群的特征,为企业制定交叉销售、捆绑销售以及开发新产品提供有价值的信息。相对以往的客户群特征提取和分析更有实用价值。
【Abstract】 The importance of Customer Relationship Management (CRM) is growing in the company Management because of the accelerated development of the market economy. As the primary method of CRM, customer segmentation can help enterprises do customers division effectively, thus enterprises can make corresponding management strategy according to different customer group characteristics. In the situation of customer demand with personalization and diversification, the extraction of customer group characteristics has become an important research subject.A method of feature extraction based on explanation of clustering results is proposed, due to the problem of having difficulty in extracting customer group characteristics after subdivision. Combined with factor analysis and cluster analysis and taking advantage of one point that factor analysis can extract explained factors, this method can extract characteristic factors from customer segmentation indexes, and calculate the average factor score of each customer group after customer clustering, then show calculation results by tables and charts. In this way, customer group characteristics can be analysed and interpreted in the light of different factor value in each customer group characteristics and customer indexes included in characteristic factors. After comparing and analysing its applicability and superiority,the method is proved reasonable and scientific in the study of the customer group characteristics.When the method is applied to empirical analysis of telecom customer relationship management, we can get optimum customer groups and their characteristics’ factor value. Through analysing the results, we can not only know better each customer group characteristics, solving the problem of having difficulty in extracting customer group characteristics that is brought about by the complication of customer indexes; but also analyse each customer group characteristics according to the factor value and information contained in factors highly correlated with customer indexes, providing valuable information for enterprises to make cross-selling, bundling selling and develop new products. Compared with the previous methods, this method doing customer group characteristics’ extraction and analysis has more practical value.
【Key words】 Customer Relationship Management; clustering results explanation; factor analysis; customer group characteristics;
- 【网络出版投稿人】 哈尔滨工业大学 【网络出版年期】2012年 05期
- 【分类号】F274;F224
- 【被引频次】1
- 【下载频次】118