Visualization method for customer targeting using customer map

An important issue in customer-oriented marketing area is target selection which enables the determination of potential customers. In this paper, we suggest a novel customer targeting method: customer map for a service industry. The customer map is the visualization method for customer targeting. It depicts value distribution across customer needs and customer characteristics. To develop the customer map, we integrate numerous customer data from various data sources, perform data analyses using data mining techniques, and finally visualize the information derived by the former analyses. The customer map helps decision makers to build customer-oriented strategy under the unified goal of customer targeting. It also affords to monitor and perceive real time state and the change of customer value distribution based on their information without preconception. We apply the customer map to a credit card company, build web-based prototype system for the customer map and acquire managerial implications from this study.     

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。     

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。     

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.     

决策树技术在医院住院客户划分中的应用

本文以省妇幼保健院历史的住院业务数据为样本,通过数据挖掘的决策树建模方法建立其住院客户的划分模型,得到分类规则,在此基础上将住院客户划分为不同的群体。通过对客户的划分及其特征分析,医院可清楚的了解重点客户并给予重点客户群体以按需要定制的个性化服务,这将极大提升这部分客户的忠诚度和满意度,从而确保医院主流利润和收入来源的长期性和稳定性。     

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.     

Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction–loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction–loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.     

The Mechanisms through Which Certain Variables Influence Customer Loyalty: The Mediating Roles of Perceived Value and Satisfaction

This study surveyed 123 bank users who experienced problems with their banks’ online banking services, with the aim of understanding the mechanisms through which certain variables influence customer loyalty. Principally, the possible mediating roles of value and satisfaction on the relationships between recovery and loyalty and between ISO 9001 and loyalty were scrutinized. The dimensionalities of the scales were assessed using exploratory and confirmatory factor analysis. Thereafter, structural equation modeling and multiregression analyses were used to test the proposed model. The overall results showed that service recovery can be used to enhance customers’ satisfaction and perception of value and, therefore, customer loyalty. This study also confirmed the partial mediating roles of customers’ satisfaction and perception of value in the relationship between service recovery and loyalty. In contrast, ISO 9001 has no influence on perceived value, satisfaction, and loyalty; the mediating roles of both perceived value and satisfaction were not supported in the relationship between ISO 9001 and loyalty. In practice, even though ISO 9001 appeared to offer few extras to satisfy and retain customers, its use is still advisable because of the other potential benefits that it provides.     

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.

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.