一种新的面向顾客的目录分割算法

目录分割问题是基于微观经济观点的数据挖掘在商业中的一个重要的应用.企业希望设计大小为r的k个目录来发送给相应感兴趣的顾客,使得顾客购买的商品数量最多.从顾客角度出发,研究面向顾客的目录分割问题,即在上述目录分割问题中要求顾客对收到的目录至少有兴趣度t. 面向顾客的目录分割问题的效用使用满足最小兴趣度t的顾客数量评估,给出一个有效的算法MaxCover解决该问题,使用新数据结构TFP-Tree来存储顾客数据库,使用树深度遍历方法来选择目录中的产品.通过对实验数据的分析,验证了本算法能够获得更好的促销效果.     

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.     

Mining Customer Value: From Association Rules to Direct Marketing

Direct marketing is a modern business activity with an aim to maximize the profit generated from marketing to a selected group of customers. A key to direct marketing is to select a subset of customers so as to maximize the profit return while minimizing the cost. Achieving this goal is difficult due to the extremely imbalanced data and the inverse correlation between the probability that a customer responds and the dollar amount generated by a response. We present a solution to this problem based on a creative use of association rules. Association rule mining searches for all rules above an interestingness threshold, as opposed to some rules in a heuristic-based search. Promising association rules are then selected based on the observed value of the customers they summarize. Selected association rules are used to build a model for predicting the value of a future customer. On the challenging KDD-CUP-98 dataset, this approach generates 41% more profit than the KDD-CUP winner and 35% more profit than the best result published thereafter, with 57.7% recall on responders and 78.0% recall on non-responders. The average profit per mail is 3.3 times that of the KDD-CUP winner.     

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。     

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.     

Ontology-based data mining approach implemented for sport marketing

Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and marketing. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer 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 the case firm, Taiwan Adidas, for possible product promotion and sport marketing.     

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。     

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.     

组合营销策略中基于约束的关联规则挖掘方法

网络销售是电子商务的一种重要的形式，而组合营销是提升网络销售业绩的一种重要手段。针时目前我国网络销售的基本模式，在已发现的组合营销策略特点的基础上，提出了一种基于约束的关联规则挖掘新算法。     

客户信任网络下病毒式营销核心群体的挖掘

目前,国内外对利用数据挖掘实现智能化制定病毒式营销策略的研究亟待深入.为了挖掘客户网络中的核心群体,定义了一种基于信任关系的客户信任网络CTN(Customer Trust Network),在此基础上创建了产品信息扩散模型CTNBDPI(CTNBased Diffusion of Product Inform ation),提出了核心群体挖掘算法VMCGM(V iralM arketing Core Group Mining)与连续病毒式营销策略的制定方法.CTNBDPI模型引入客户特征与环境因素解决了孤立点的接受与推荐问题,实验证明可以更好地反映病毒式营销中产品信息扩散的规律,与已有研究相比,VMCGM算法具有较低的时间复杂度和较高的准确性.     

决策树在基于消费者外表的服装营销中的应用

服装销售人员常常根据消费者的外表特征来进行快速营销活动,以提高购买率。从数据挖掘技术的角度来探讨基于消费者外表印象的快速营销技术,以帮助营销人员快速寻找外表印象营销规则。介绍了决策树算法原理;其次,讨论了消费者外表印象评价指标体系,并根据该体系由销售人员在服装店铺里进行了消费者的外表及其行为数据采集;应用了计算实例来说明服装消费者的外表营销决策树分类模型;利用工具Clementine中的决策树方法来进行营销规则的挖掘。研究表明了该应用是切实可行的。     

Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm

Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.     

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.     

客户导向目录分割问题的改进算法

客户导向目录分割问题假设顾客至少对目录中一定数量的商品感兴趣,计算目录覆盖的顾客数量,据此评估目录分割结果. 现有的分割算法为了保证目录尽可能多的覆盖顾客,而忽略了目录分割结果的效用. 针对该问题,本文构建一种新的数据存储结构CFP-Tree用于存储顾客交易数据,并提出一种新的算法Effective-Cover解决目录分割问题. 该算法使用树深度遍历法选择目录产品. 实验结果表明,该算法能够获得更好的目录分割结果.     

Bargaining strategy formulation with CRM for an e-commerce agent

The growth of electronic commerce has created the need of automated bargaining agents for improving the efficiency of online transactions. From the perspective of customer relationship marketing (CRM), establishing and maintaining the best possible relationship with valuable customers is a good way to survive in the competitive global market. In order to retain valuable customers, high share customers ought to be treated differently from the low share customers in the bargaining process. In our research, we formulate strategies for a bargaining agent based on the CRM principle. Bargaining tactics are expressed as fuzzy rules that mimic a human bargainer’s knowledge and judgment in making decisions. Actions of the bargaining agent are determined by using approximate reasoning from the set of fuzzy rules. Our bargaining agent and three other bargaining agents found in the literature are employed in an experimental online store. Experimental results indicate that our bargaining agent is more efficient and creates greater customer satisfaction and customer loyalty than do the bargaining agents from the literature.     

Mining demand chain knowledge of life insurance market for new product development

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers’ needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.     

Internet electronic product catalogs: an approach beyond simple keywords and multimedia

Internet-based Electronic Product Catalogs (IEPCs) are one of the most important parts of electronic markets. They are the merchant’s interactive interface towards online customers. Based on the features of their carrier, the interactive and ubiquitous Internet, IEPCs are online, permanently up-to date, and enable customization as well as direct communication between seller and buyer. Even though IEPCs are more sophisticated compared to paper-based catalogs, the search for products on the Internet is still a cumbersome process. Surveys show, that online customers have difficulties navigating through merchants’ sites to find the products they need. In this paper, a comprehensive approach for IEPCs as complex information spaces will be presented. First a detailed requirements analysis for IEPCs will be conducted. Then a concept for organizing information within IEPCs, which goes beyond simple keywords and multimedia, will be presented. Finally, technologies for its implementation will be identified.     

基于频繁模式树的关联规则增量式更新算法

研究了大型事务数据库中关联规则的增量式更新总是，提出了一种基于频繁模式树的关联规则增量式更新算法，以处理最小支持度或事务数据库发生变化后相应关联规则的更新问题，并对其性能进行了分析。     

Customer data mining for lifestyle segmentation

A good relationship between companies and customers is a crucial factor of competitiveness. Market segmentation is a key issue for companies to develop and maintain loyal relationships with customers as well as to promote the increase of company sales. This paper proposes a method for market segmentation in retailing based on customers’ lifestyle, supported by information extracted from a large transactional database. A set of typical shopping baskets are mined from the database, using a variable clustering algorithm, and these are used to infer customers lifestyle. Customers are assigned to a lifestyle segment based on their purchases history. This study is done in collaboration with an European retailing company.     

“徽杭古道”公众账号的旅游产品营销思路分析

旅游产品是一种典型的经验产品,其商品为非标准化,客户只能通过产品体验后才能够评价产品的优劣。对于旅游产品,广告和商品介绍都不能对客户进行有效的劝告。沉没成本问题降低了用户对此类产品的需求。在互联网营销下,微信为旅游产品提供了一种很好的营销方案。借助强社交性的平台,"徽杭古道"平台成功地进行了营销策划。公众平台与朋友圈的组合方案,让旅游产品推广变得更加高效。