基于购买行为的客户分群研究

发布时间：2018-03-23 07:14

本文选题：购买行为　切入点：客户分群　出处：《深圳大学》2017年硕士论文　论文类型：学位论文

【摘要】：互联网的快速发展和市场的不断完善成熟,将全球经济推进到了电子商务时代。于此同时,企业提供的商品和服务差别逐渐减小,客户的购买途径变得更为丰富,客户选择更加多样化,市场竞争更加激烈,企业的市场战略必须由过去以生产为中心、销售为目的向现在以客户为中心、服务为目的转变,营销模式必须由大众化营销向差异化营销转变。有效的建立和完善以客户和服务为中心的营销体系,最重要的就是对客户进行分群。客户分群方法概括起来主要有两类,第一类是基于客户的社会属性和消费额度等常规信息进行分群,第二类是基于客户的购买行为进行分群。基于购买行为分群也即基于交易数据分群,它是采用数据挖掘的方法对客户分群,这也是当前研究的热点。调研发现,当前基于购买行为的客户分群方法存在缺陷和不实用,它们只考虑客户购买的商品,没有考虑商品类别,而企业商品种类很多并且划分的很细致,这就导致群内紧密度不高,实用价值不大。有些基于RFM模型的分群也没有考虑到同组客户购买商品的差异性。本文针对上述基于购买行为的客户分群中存在的问题,提出一种新的客户分群模型,并基于该模型提出了新的客户分群方法,完成了以下三个创新工作:1.用购买树模型表示客户购买信息。购买树是根据每个客户购买商品类别建立对应的层次结构树,树的叶子节点表示商品,内部节点表示商品类别。在计算客户距离的时候,利用购买树加入了内部类别的距离,能够从不同类别层次粒度分析客户,提升客户分群结果的实用价值。2.基于购买树模型,提出一种基于购买树的层次聚类客户分群方法。该聚类方法对客户按购买树的层次从高到低逐层进行聚类划分,层内用图模型的方法进行划分,提高聚类效果。3.针对传统基于购买行为的客户分群通常只考虑客户购买的商品,没有考虑商品价值的弱点。提出了在购买树中引入RFM值的客户分群方法。对每个客户创建一颗RFM购买树,然后进行聚类分群,从而实现对客户分群时考虑到商品的价值。采用真实数据对上述分群方法进行了实验,结果表明,新方法取得比已有方法更好的客户分群效果,为客户分群的应用提供了新的工具。
[Abstract]:The rapid development of the Internet and the maturity of the market have pushed the global economy to the era of electronic commerce. At the same time, the differences in goods and services provided by enterprises are gradually decreasing, and the way customers buy has become more abundant. The choice of customers is more diversified and the market competition is more intense. The market strategy of enterprises must be changed from production-centered and sales to customer-centered and service-oriented in the past. The marketing mode must be changed from popular marketing to differentiated marketing. To establish and perfect the customer and service centered marketing system effectively, the most important thing is to divide the customers. The first is based on the customer's social attributes and consumption quota, the other is based on the customer's purchase behavior, which is based on the transaction data. It is a hot topic in the current research. It is found that the current customer clustering method based on purchasing behavior is flawed and impractical, and they only consider the goods purchased by customers. Not taking into account commodity categories, and enterprises with a large variety of commodities and very detailed division, which leads to low tightness in the group, Some clusters based on RFM model do not take into account the difference of the same group of customers to buy goods. In this paper, a new customer clustering model is proposed to solve the problems in the customer clustering based on purchasing behavior. Based on this model, a new customer clustering method is proposed, and the following three innovative tasks are accomplished: 1.The purchase tree model is used to represent the customer purchase information. The purchase tree is to establish the corresponding hierarchy tree according to each customer purchase item category. The leaf node of the tree represents the commodity, and the internal node represents the category of the item. When calculating the distance between the customers, the purchase tree is used to add the distance of the internal category, and the customer can be analyzed from different class levels of granularity. 2. Based on the purchase tree model, a hierarchical clustering method based on the purchase tree is proposed. In order to improve the clustering effect, the traditional customer clustering based on purchasing behavior is usually considered only the goods purchased by customers. Without considering the weakness of commodity value, this paper proposes a customer clustering method which introduces RFM value into the purchase tree. A RFM purchase tree is created for each customer, and then cluster clustering is carried out. In this way, the value of goods is taken into account in customer clustering. The experimental results show that the new method can achieve better customer clustering effect than the existing methods. It provides a new tool for the application of customer clustering.
【学位授予单位】：深圳大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13

【相似文献】