基于灰色关联度和K-Means++的电子商务客户价值分类

现有的模型大多采用RFM模型和K-means对客户价值进行分类,对指标权重的确定大多采用AHP法,没有考虑到RFM模型指标相互之间的联系.首先根据RFM模型选择平均购买时间间隔,客户在一定时间内的购买频率,平均每笔订单交易金额和客户的活跃时间构造RFMT模型来衡量客户价值.其次使用灰色关联度确定各指标权重.最后针对K-means的缺点,运用改进K-means (K-means++)和肘部法则对RFMT模型进行聚类分析.该模型能对客户群进行更加细致的划分,既能帮助电子商务企业识别出需要重点关注的客户即已流失客户和新客户群体,同时将该企业客户划分为价值由高到低的客户群,对不同客户群提出具体的营销建议.     

基于RFA模型和聚类分析的百度外卖客户细分

针对百度外卖行业具有的客户数量大、消费数据多、维度多等特点,提出一种基于客户消费行为视角的改进RFM模型。采用层次分析算法确定模型中各个变量的权重,并在此基础上采用K-Means聚类算法进行客户细分,计算确定客户对于商家的个人价值。数据分析结果表明,基于改进RFM模型的客户细分方法可以使商家对不同价值的客户采取针对性的策略。     

基于RFM和粗糙集的客户分类规则提取

通过分析现有的分类规则提取方法,给出了一种提取客户分类规则的方法,该方法对客户的RFM属性进行K-均值聚类以确定客户价值,利用粗糙集完成规则提取,为客户分类提供了一种新的思路.通过实例验证了这种方法能够有效地对客户进行细分、提取分类规则,并提高了分类准确性.     

基于SOM神经网络的移动客户细分研究

利用基于RFM模型的自组织特征映射神经网络(Self-Organizing Feature Map,SOM)对移动客户进行细分,可以有效地解决各类别特征不明显、特征参数相互交错、非线性分布的类型识别问题。研究过程中将客户的属性划分为近度、频度、值度三个指标,模拟专家分类的功能,根据各个客户簇的特征进一步分析客户的终身价值,量化分析客户的重要性。最后利用相关的市场营销知识对各个客户类别提出相应的营销策略方案。     

基于数据挖掘的客户细分方法的研究

客户细分是客户关系管理中基础的、重要的内容。全面考虑了客户生命周期价值,基于群体决策技术和数据挖掘技术提出了一种新的客户细分方法。在群体决策的基础上,确定影响客户细分的变量,利用层次分析法,确定各个变量的权重。利用数据挖掘的聚类技术,进行客户细分。用某橡胶企业的数据进行了验证,结果表明,该方法能够有效地支持企业的客户细分,为企业的决策提供依据。     

双聚类算法在电信高价值客户细分的应用

针对传统客户价值细分方法在高价值客户细分时不够精细化的问题,引入了大均值子矩阵(LAS)双聚类算法。该方法在客户样本和消费属性两个维度上对消费记录进行双向聚类,可以挖掘出高消费、高价值的客户群体。以某电信公司的高价值客户细分为实例,通过定义一个价值尺度和构建一个PA指标,将所提算法与K均值(K-means)算法进行性能比较,实验结果表明,所提算法能挖掘出更多的高价值客户群体,且能够对客户属性进行更加精细的划分,因此它更适合应用于高价值客户市场的识别和细分。     

基于AHP的供应链物流客户价值评估模型研究

通过分析现代供应链物流客户价值评估管理的特点,提出了利用层次分析法（AHP）来评估供应链物流的客户价值,研究了相应的客户价值评估模型。结合AHP多目标决策分析感性与理性两方面评估的特点研究了供应链物流客户价值的评估过程,并建立了评估系统的应用框架。最后,结合某光电行业供应链物流管理信息平台数据库对基于AHP的评估模型进行了实证。     

基于聚类算法的零售业CRM数据挖掘的应用

把数据挖掘中K-中心点聚类算法应用于基于客户价值矩阵的客户价值细分中,建立一种零售业客户细分方法,为零售超市客户保持和营销提供决策依据,并用样本进行实验,得出结论.     

客户价值评价体系的设计与实现

针对企业在客户营销中如何根据客户价值进行客户细分的问题,提出了一套客户价值评价体系。该体系首先确定了客户价值评价模型及如何运用层次分析法确定其影响因子的权重。在实现阶段阐述了利用规范属性值方式实现数据预处理及聚类分析实现客户细分。该体系在整体上具有科学性,可操作性强等特点。     

改进RFM模型的汽车4S店客户细分研究

针对传统RFM模型存在用户特征不能充分提取的问题,提出改进的RFM模型.通过IV值进行指标选择,在传统RFM模型三个指标基础上加入新指标,采用熵权法对各指标赋权,同时采用数据分箱减少模型离散特征.通过K-Means聚类对分箱后的数据聚类分析,进行客户特征提取,制定相应的挽留措施,以实现基于客户细分的精准营销.结果表明,改进模型较原模型有显著提升.     

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.     

集成AHP和数据挖掘的个性推荐系统研究

提出了一个基于层次分析和数据挖掘的个性推荐系统。运用层次分析法来评价顾客生命周期价值中每一个RFM变量的重要程度,根据加权的RFM来对顾客进行聚类分析,通过关联规则挖掘从顾客簇中抽出频繁购买模式,根据簇中关联规则向顾客推荐相关商品。实验表明性能优于相等权重的聚类方法和不进行聚类直接从所有顾客中进行关联规则挖掘的方法。     

Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of quantile regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with linear regression. Moreover, we show that in the common situation where interest is in a top-customer segment, quantile regression outperforms linear regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers.     

Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.     

基于消费数据挖掘的多指标客户细分新方法

提出一种过程完整的针对消费数据挖掘的客户细分新方法。设计了包含3种类型10个指标的客户细分模型, 并采用因子分析法从中提取细分变量, 再使用基于划分的聚类算法进行客户细分。通过对某大型纸巾生产企业100万销售数据的计算分析, 得出了有效客户类别, 表明了本方法具有更强的客户细分能力和客户行为特征的解释能力。     

基于RFM模型的随机森林算法对民航客户的流失分析

近几年,随着航空市场的快速发展,对于航空公司而言,如何在增加市场占有率的同时,对客户的流失进行有效的控制也刻不容缓.基于随机森林算法,根据航空客户数据,建立流失预测模型,对客户是否已流失进行预测研究,将传统的RFM客户价值模型进行改进,结合随机森林算法对客户流失进行预测.实验结果表明,基于RFM模型的随机森林算法构建的...     

Discovering recency,frequency, and monetary (RFM) sequential patterns from customers’ purchasing data

In response to the thriving development in electronic commerce (EC), many on-line retailers have developed Web-based information systems to handle enormous amounts of transactions on the Internet. These systems can automatically capture data on the browsing histories and purchasing records of individual customers. This capability has motivated the development of data-mining applications. Sequential pattern mining (SPM) is a useful data-mining method to discover customers’ purchasing patterns over time. We incorporate the recency, frequency, and monetary (RFM) concept presented in the marketing literature to define the RFM sequential pattern and develop a novel algorithm for generating all RFM sequential patterns from customers’ purchasing data. Using the algorithm, we propose a pattern segmentation framework to generate valuable information on customer purchasing behavior for managerial decision-making. Extensive experiments are carried out, using synthetic datasets and a transactional dataset collected by a retail chain in Taiwan, to evaluate the proposed algorithm and empirically demonstrate the benefits of using RFM sequential patterns in analyzing customers’ purchasing data.     

客户细分中聚类的应用

客户细分是企业识别客户类别、把握客户特征的重要方法。文章简单介绍了当前常用的客户细分的方法,针对电信企业提出了基于客户价值和客户行为的客户细分模型,采用K-means算法对电信企业客户进行聚类,并提出提升各类客户价值相应的策略。