Mining customer knowledge for electronic catalog marketing

Direct marketing is the use of the telephone and non-personal media to communicate product and organizational information to customers, who then can purchase products via mail, telephone, or the Internet. In contrast, catalog marketing is a type of marketing in which an organization provides a catalog from which customers make selections and place orders by mail or telephone. However, most catalogs for retailing firms are presented to customers in the format of paper catalogs without strategic segmentation design and implementation. In this regard, electronic catalog design and marketing could be a method to integrate the Internet and catalog marketing using market segmentation in order to enhance the effectiveness of direct marketing and sales management in retailing. This paper uses data mining based on association rules from relational database design and implementation for mining customer knowledge. As result, marketing knowledge patterns and rules are extracted for the electronic catalog marketing and sales management of a retailing mall in Taiwan.     

A Dominance-based Rough Set Approach to customer behavior in the airline market

Market segmentation is a crucial activity in the present business environment. Data mining is a useful tool for identifying customer behavior patterns in large amounts of data. This information can then be used to help with decision-making in areas such as the airline market. In this study, we use the Dominance-based Rough Set Approach (DRSA) to provide a set of rules for determining customer attitudes and loyalties, which can help managers develop strategies to acquire new customers and retain highly valued ones. A set of rules is derived from a large sample of international airline customers, and its predictive ability is evaluated. The results, as compared with those of multiple discriminate analyses, are very encouraging. They prove the usefulness of the proposed method in predicting the behavior of airline customers. This study demonstrates that the DRSA model helps to identify customers, determine their characteristics, and facilitate the development of a marketing strategy.     

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.     

Organizing Multiple Data Sources for Developing Intelligent e-Business Portals

Enterprise applications usually involve huge, complex, and persistent data to work on, together with business rules and processes. In order to represent, integrate, and use the information coming from the huge, distributed, multiple sources, we present a conceptual model with dynamic multi-level workflows corresponding to a mining-grid centric multi-layer grid architecture, for multi-aspect analysis in building an e-business portal on the Wisdom Web. We show that this integrated model will help to dynamically organize status-based business processes that govern enterprise application integration. We also present two case studies to demonstrate the effectiveness of the proposed model in the real world. The first case study is about how to organize and mine multiple data sources for behavior-based online customer segmentation, which is the first crucial step of personalization and one-to-one marketing. The second case study is about how to evaluate and monitor data quality, which in return can optimize the knowledge discovery process for intelligent decision making. The proposed methodology attempts to orchestrate various mining agents on the mining-grid for integrating data and knowledge in a unified portal developed by a service-oriented architecture.     

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。     

Product development with data mining techniques: A case on design of digital camera

Many enterprises have been devoting a significant portion of their budget to product development in order to distinguish their products from those of their competitors and to make them better fit the needs and wants of customers. Hence, businesses should develop product designing that could satisfy the customers’ requirements since this will increase the enterprise’s competitiveness and it is an essential criterion to earning higher loyalties and profits. This paper investigates the following research issues in the development of new digital camera products: (1) What exactly are the customers’ “needs” and “wants” for digital camera products? (2) What features is more importance than others? (3) Can product design and planning for product lines/product collection be integrated with the knowledge of customers? (4) How can the rules help us to make a strategy during we design new digital camera? To investigate these research issues, the Apriori and C5.0 algorithms are methodologies of association rules and decision trees for data mining, which is implemented to mine customer’s needs. Knowledge extracted from data mining results is illustrated as knowledge patterns and rules on a product map in order to propose possible suggestions and solutions for product design and marketing.     

基于数据挖掘技术的营销模式

该文运用聚类分析、关联规则和决策树等数据挖掘技术,力图创新出以消费者为导向,以交叉销售为特征的一种新的营销模式。新的营销模式分运用聚类分析建立客户细分数据库、运用关联规则提取交叉规则和运用决策树技术识别目标客户三个步骤来实施。     

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。     

Mining marketing maps for business alliances

A business can strengthen its competitive advantage and increase its market share by forming a strategic alliance. With the help of alliances, businesses can bring to bear significant resources beyond the capabilities of the individual co-operating firms. Thus how to effectively evaluate and select alliance partners is an important task for businesses because a successful corporation partner selection can therefore reduce the possible risk and avoid failure results on business alliance. This paper proposes the Apriori algorithm as a methodology of association rules for data mining, which is implemented for mining marketing map knowledge from customers. Knowledge extraction from marketing maps is illustrated as knowledge patterns and rules in order to propose suggestions for business alliances and possible co-operation solutions. Finally, this study suggests that integration of different research factors, variables, theories, and methods for investigating this research topic of business alliance could improve research results and scope.     

Customer''s time-variant purchase behavior and corresponding marketing strategies: an online retailer''s case

The traditional customer relationship management (CRM) studies are mainly focused on CRM in a specific point of time. The static CRM and derived knowledge of customer behavior could help marketers to redirect marketing resources for profit gain at the given point in time. However, as time goes on, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date.

In this paper, we propose a dynamic CRM model utilizing data mining and a monitoring agent system to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the retailer. Furthermore, we show that longitudinal CRM could be usefully applied to solve several managerial problems, which any retailer may face.     

支持交叉营销的金融产品客户数据挖掘

首先比较了DBSCAN,CLIQUE,CLARANS,K-means和X—means等聚类算法,接着选用X-means聚类算法建立了金融产品客户细分模型,然后结合关联强度分析,设计了支持交叉营销的金融产品客户数据挖掘系统,并给出了一个系统使用示例。     

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。     

A quantitative correlation coefficient mining method for business intelligence in small and medium enterprises of trading business

Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mining method which is capable of uncovering hidden patterns of sales and market. Hence, a prototype business intelligence system (BIS) named correlation coefficient sales data mining system (CCSDMS) has been developed and successfully trial implemented in a selected reference site. A series of experiments have been conducted to evaluate the performance of the proposed system. The results generated by the BIS are compared with a well known market available data mining system. The proposed quantitative correlation coefficient mining method is found to possess higher accuracy, better computational effectiveness and higher predictive power. With the new approach, associations for product relations and customer periodic demands are revealed and this can help to leverage organizational marketing capital to enhance quality and speed of promotions as well as awareness of product relations.     

Ontology-based data mining approach implemented on exploring product and brand spectrum

In physics, a spectrum is, the series of colored bands diffracted and arranged in the order of their respective wave lengths by the passage of white light through a prism or other diffracting medium. Outside of physics, a spectrum is a condition that is not limited to a specific set of values but can vary infinitely within a continuum. In commerce, an effective visualization tool, especially for stakeholders or managers, is a brand spectrum diagram highlighting where the company’s brands and products are situated compared to other competitors. This paper investigates the research issues on product and brand spectrum in the beverage product market of Taiwan, which proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer and product knowledge from the database. Knowledge extracted from data-mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to beverage firms for possible product development, promotion, and marketing.     

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.     

关联规则挖掘在网上书店系统中的应用

数据挖掘技术是在大量的数据中发现未知知识的数据分析技术,利用数据挖掘技术分析客户数据,发现其中的规律,从而为商务决策提供依据.本文研究了关联规则的相关分析并应用于网上书店系统,实现客户订单数据的关联规则挖掘.     

Mining shopping behavior in the Taiwan luxury products market

The rapid growth of Taiwan’s economy has been accompanied by the country’s developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry.     

Mining association rules procedure to support on-line recommendation by customers and products fragmentation

Electronic Commerce (EC) has offered a new channel for instant on-line shopping. However, there are too many various products available from a great number of virtual stores on the Internet for Internet shoppers to select. On-line one-to-one marketing therefore becomes a great assistance to Internet shoppers. One of the most important marketing resources is the prior daily transaction records in the database. The great amount of data not only gives the statistics, but also offers the resource of experiences and knowledge. It is quite natural that marketing managers can perform data mining on the daily transactions and treat the shoppers the way they prefer. However, the data mining on a significant amount of transaction records requires efficient tools. Data mining from automatic or semi-automatic exploration and analysis on a large amount of data items set in a database can discover significant patterns and rules underlying the database. The knowledge can be equipped in the on-line marketing system to promote Internet sales.

The purpose of this paper is to develop a mining association rules procedure from a database to support on-line recommendation. By customers and products fragmentation, product recommendation based on the hidden habits of customers in the database is therefore very meaningful. The proposed data mining procedure consists of two essential modules. One is a clustering module based on a neural network, Self-Organization Map (SOM), which performs affinity grouping tasks on a large amount of database records. The other rule is extraction module employing rough set theory that can extract association rules for each homogeneous cluster of data records and the relationships between different clusters. The implemented system was applied to a sample of sales records from a database for illustration.     

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。     

An efficient data mining approach for discovering interesting knowledge from customer transactions

Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information.

Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements.