Customer segmentation issues and strategies for an automobile dealership with two clustering techniques

Companies can use customer segmentation to group customers with similar characteristics together and identify the differences between groups to develop marketing strategies. This study investigates the problem of customer segmentation in relation to automotive customer relationship management and presents a real case study of an automobile dealer in Taiwan. Although several past studies have adopted different clustering techniques with which to group customer attributes, few have simultaneously considered customer transaction behaviour and customer satisfaction variables. In addition, most previous work has used only a single clustering method for customer segmentation, which results in unreliable results and leads to inadequate marketing decisions. Therefore, in this study, we consider two clustering techniques, k‐means and expectation maximization, and compare their results for correctness. The experimental results show that four customer groups are identified with both clustering methods: loyal, potential, VIP and churn customer groups. Based on the segmentation results, several customized marketing strategies aimed at each of the four customer groups are suggested to improve the quality of services for effective customer relationship management.     

基于信息融合的电信客户流失预测研究

针对数据挖掘方法在电信客户流失预测中的局限性,提出将信息融合与数据挖掘相结合,分别从数据层、特征层、决策层构建客户流失预测模型。确定客户流失预测指标;根据客户样本在特征空间分布的差异性对客户进行划分,得到不同特征的客户群;不同客户群采用不同算法构建客户流失预测模型,再通过人工蚁群算法求得模型融合权重,将各模型的预测结果加权得到预测最终结果。实验结果表明,基于信息融合的客户流失预测模型确实比传统模型更优。     

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.     

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.     

证券公司客户综合分析系统的设计与实现

介绍基于数据仓库与数据挖掘技术的证券公司客户综合分析系统的设计与实现,其中着重介绍了系统的设计原则、设计思想以及有证券特色的数据挖掘模型及其应用等重要内容。用k-Means聚类方法构建了客户偏好细分模型,将客户有效划分为8群;利用决策树及Logistic回归相结合构建了客户流失预警模型,结果表明该模型对客户流失捕获率有很大提升。     

Keep the conversations going: engagement-based customer segmentation on online social service platforms

Most businesses and organizations develop online services as a value-added offering, which is a significant revenue stream from their existing user base. Such services may be enhanced with social elements to serve as value-added tools for user attraction and retention. Social elements may allow users to post content, share information and directly interact with each other. Investments in these social features are for naught if they do not encourage users to engage on the platform effectively. However, common ways to segment customers by their engagement is hindered by the statistical nature of behavioral data based on social elements. To address this important concern, this paper presents a methodological framework for engagement-based customer segmentation able to appropriately consider signals from social elements. It argues why the traditional approaches for user segmentation is ill-suited and advocates for the integration of kernel functions with clustering to segment, identify and understand user engagement profiles. The framework is demonstrated with real data from a large, very active OSS.     

CIAS:一个客户智能分析数据挖掘平台

CIAS是将数据挖掘技术应用在CRM领域而开发的一个客户智能分析平台。它将数据挖掘划分为三个层次：算法层、商业逻辑层、行业应用层，构建了一种新型的数据挖掘系统体系结构。CIAS的商业逻辑层包括交叉销售、客户响应、客户细分、客户流失、客户利润，五个商业模型。通过在商业模型和挖掘算法之间建立映射，CIAS使得用户直接利用商业模型解决问题，而不是面对复杂的算法，从而提供友好、易用的数据挖掘应用环境。     

Dynamic micro-targeting: fitness-based approach to predicting individual preferences

It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. 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 clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro-targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization.

数据挖掘在移动通信业中应用研究综述

数据挖掘是人工智能、机器学习与数据库技术等多学科相结合的产物,移动通信业是数据挖掘技术当前重要的应用领域之一。本文重点介绍了数据挖掘技术在移动通信业中应用的客户描述、客户分群、与客户流失分析的最新研究方法与进展。     

Mining for trigger events with survival analysis

This paper discusses a new application of data mining, quantifying the importance of responding to trigger events with reactive contacts. Trigger events happen during a customer’s lifecycle and indicate some change in the relationship with the company. If detected early, the company can respond to the problem and retain the customer; otherwise the customer may switch to another company. It is usually easy to identify many potential trigger events. What is needed is a way of prioritizing which events demand interventions. We conceptualize the trigger event problem and show how survival analysis can be used to quantify the importance of addressing various trigger events. The method is illustrated on four real data sets from different industries and countries.     

Combined rough set theory and flow network graph to predict customer churn in credit card accounts

Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary churn and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

GHIC: a hierarchical pattern-based clustering algorithm for grouping Web transactions

Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GHIC, that groups customer transactions such that itemsets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions.     

A dynamic understanding of customer behavior processes based on clustering and sequence mining

In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.     

基于并行C4.5的铁路零散白货客户流失预测研究

为了提高铁路零散白货客户流失预测的准确性和高效性,根据铁路零散白货客户的流失特征,提出了基于CDL模型的客户流失识别方法,在此基础上,针对数据量大的问题,提出了基于Hadoop并行框架的C4.5决策树客户流失预测模型。通过仿真实验,证明该模型具有较好的准确性和预测能力,并且随着样本数量的增加,Hadoop并行框架的效率得到了明显的提升,且不影响客户流失预测模型的准确性和预测能力。     

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。     

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。     

基于动态聚类的证券业客户细分实证研究

在客户关系管理理论基础上,建立了一个包含13个行业特色指标的证券业客户多维细分模型,并利用聚类分析对国内某知名券商的具体客户信息和交易数据进行了实证研究,有效识别出了具有不同特征以及偏好的客户群,并在此基础上提出了相应的营销策略。     

聚类分析在电信家庭客户细分中的应用

电信行业的客户细分多数集中在政企客户,很少涉及到家庭客户,而家庭市场一直是电信运营商的大本营。该文采用数据挖掘中的K-means聚类算法,建立客户细分模型,对电信家庭客户进行细分,为进一步挖掘家庭信息服务需求,实现精细化营销奠定基础。