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。     

融合联机分析和聚类的电信客户细分建模方法与实现

应用联机分析处理技术选择用户ARPU值作为客户细分的维度．依据用户平均收入ARPU值进行分类．按客户的消费额高低将客户分成高中低几档客户．然后采用数据挖掘聚类分析中的K—means聚类算法．参照国际通行的数据挖掘CRISP—DM标准提出一种电信企业客户细分模型和细分方法。对电信企业大量现实数据的实验结果表明．利用该客户细分模型和技术获得了较好的挖掘结果．为电信运营商的经营和决策提供了有力的支持。     

基于数据挖掘技术的客户细分研究

随着客户关系管理(CRM)系统应用的逐步推广,企业"以产品为中心"到"以客户为中心"的经营模式的战略转变,客户细分作为客户关系管理系统的核心功能作用受到了充分的重视。该文综合分析了现有的客户细分方法,并着重对数据挖掘技术在客户细分领域的应用进行阐述。     

基于数据挖掘的客户细分框架模型

数据挖掘技术在客户关系管理领域的应用较广泛,能提高客户细分能力。针对目前客户细分研究缺乏统一研究框架的问题,分析现有的客户关系管理系统构架及其与客户细分的集成关系,对客户细分问题进行构架性研究,提出一种基于数据挖掘的客广细分框架模型,包括空间逻辑模型和数据-功能-方法模型。     

基于客户细分的电信数据挖掘

数据挖掘以其强大的数据处理能力和信息挖掘能力广泛应用于各行各业。在电信业可以应用这项技术进行客户细分的研究。文章重点阐述了应用数据挖掘进行电信行业客户细分的方法和步骤。     

数据挖掘技术在银行客户细分中的应用

该文将数据挖掘技术应用于银行客户关系管理中的客户细分,探讨了对银行个人客户进行细分的方法,并依据分类结果分析了这种方法的有效性。研究目的是利用决策树算法建立挖掘模型分析个人客户信息,将满足理财金账户的客户细分出来,并制定不同的销售措施及差别服务。此方法已经投入运行,实践证明该方法实用、可操作性强,对银行产生了积极的影响。     

数据挖掘技术在客户细分领域的应用

在市场经济发展过程中,以客户为中心的经营理念的转变,使得客户关系管理在研究与应用上受到了广泛的重视。作为客户关系管理的核心概念之一,客户细分已成为一种基础性的分析功能,并将为企业管理提供全面的信息支持。本文综合研究了现有的客户细分方法,并介绍了数据挖掘技术在客户细分领域的应用。     

基于聚类分析的客户细分研究

本文应用数据挖掘技术中的聚类分析,进行对客户细分的研究。介绍了K平均算法和K平均算法在客户细分中的应用,并提出了客户价值、消费特征和人口特征三个维度应是客户细分的主要内容。     

数据挖掘在CRM中的应用设计

讨论了在客户关系管理 (CRM)中用于客户细分和建立客户轮廓的数据挖掘技术。首先指出 CRM的概念和分类 ,然后分析了几种数据挖掘方法 ,最后提出面向 CRM的数据挖掘应用设计。     

基于聚类分析的客户细分研究

本文应用数据挖掘技术中的聚类分析,进行对客户细分的研究。介绍了K平均算法和K平均算法在客户细分中的应用,并提出了客户价值、消费特征和人口特征三个维度应是客户细分的主要内容。     

Customer segmentation based on survival character

Customer Segmentation is an increasingly pressing issue in today’s over-competitive commercial area. More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effectives. But most of them segment customer only by single data mining technology from a special view, rather than from systematical framework. Furthermore, one of the key purposes of customer segmentation is customer retention. Although previous segment methods may identify which group needs more care, it is unable to identify customer churn trend for taking different actions. This paper focus on proposing a customer segmentation framework based on data mining and constructs a new customer segmentation method based on survival character. The new customer segmentation method consists of two steps. Firstly, with K-means clustering arithmetic, customers are clustered into different segments in which customers have the similar survival characters (churn trend). Secondly, each cluster’s survival/hazard function is predicted by survival analyzing, the validity of clustering is tested and customer churn trend is identified. The method mentioned above has been applied to a dataset from China Telecom, which acquired some useful management measures and suggestions. Some propositions for further research is also suggested.     

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。     

Mining changes in customer behavior in retail marketing

During the past decade, there have been a variety of significant developments in data mining techniques. Some of these developments are implemented in customized service to develop customer relationship. Customized service is actually crucial in retail markets. Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in customer behavior. In the dynamic retail market, understanding changes in customer behavior can help managers to establish effective promotion campaigns. This study integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at different time periods. The proposed approach for mining changes in customer behavior can assist managers in developing better marketing strategies.     

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.     

Improving Personalization Solutions through Optimal Segmentation of Customer Bases

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization.     

一种基于AHP方法的客户价值细分研究

通过分析基于客户生命周期价值客户价值细分的各种方法,给出了一种简单易行的基于AHP(层次分析法)的客户价值细分方案,为客户价值细分提供了一种新的思路。该方案引入AHP通过领域专家的群体决策计算出RFM(最近购买时间、购买频率和总购买金额)的权重,根据加权的RFM变量来对客户群进行聚类分析,探讨了该方案的具体实施流程,并对方案中的关键方法做了详细的分析。最后通过实例分析,聚类结果表明了这种方案能够有效地对客户群体进行细分。     

基于客户细分的客户流失预测研究

分析了电信行业客户关系管理系统的数据独有特点,提出基于客户细分的客户流失预测模型.首先,采用模糊核C-均值聚类算法用于客户细分并对细分结果进行分析,发现高价值客户的群体特征.再利用企业历史数据建立基于SAS数据挖掘技术的客户流失预测模型.最后,把高价值客户作为预测目标数据应用于该模型当中预测出有流失倾向的客户.实验结果表明,该方法有效可行,可以为企业提供准确、有流失倾向的客户名单.     

Applying knowledge engineering techniques to customer analysis in the service industry

Using the customer relationship management perspective to investigate customer behavior, this study differentiates between customers through customer segmentation, tracks customer shifts from segment to segment over time, discovers customer segment knowledge to build an individual transition path and a dominant transition path, and then predicts customer segment behavior patterns. By using real-world data, this study evaluates the accuracy of predictive models. The concluding remarks discuss future research in this area.     

A transaction pattern analysis system based on neural network

Customer segmentation is a key element for target marketing or market segmentation. Although there are quite a lot of ways available for segmentation today, most of them emphasize numeric calculation instead of commercial goals. In this study, we propose an improved segmentation method called transaction pattern based customer segmentation with neural network (TPCSNN) based on customer’s historical transaction patterns. First of all, it filters transaction data from database for records with typical patterns. Next, it reduces inter-group correlation coefficient and increases inner cluster density to achieve customer segmentation by iterative calculation. Then, it utilizes neural network to dig patterns of consumptive behaviors. The results can be used to segment new customers. By this way, customer segmentation can be implemented in very short time and costs little. Furthermore, the results of segmentation are also analyzed and explained in this study.