客户关系管理中的数据挖掘技术.doc

毕业论文（设计）外文翻译外文原文Data mining techniques for customerrelationship managementChris Rygielski , Jyun-Cheng Wang , David C. Yen AbstractAdvancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data miningthe extraction of hidden predictive information from large databasesorganizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of todays customer relationship management philosophy.2002 Elsevier Science Ltd. All rights reserved.Keywords: Customer relationship management (CRM); Relationship marketing; Data mining; Neural networks;Chi-square automated interaction detection (CHAID); Privacy rights1. IntroductionA new business culture is developing today. Within it, the economics of customer relationships are changing in fundamental ways, and companies are facing the need to implement new solutions and strategies that address these changes. The concepts of mass production and mass marketing, first created during the Industrial Revolution, are being supplanted by new ideas in which customer relationships are the central business issue. Firms today are concerned with increasing customer value through analysis of the customer lifecycle. The tools and technologies of data warehousing, data mining, and other customer relationship management (CRM) techniques afford new opportunities for businesses to act on the concepts of relationship marketing.The old model of “design-build-sell” (a product-oriented view) is being replaced by “sell-build-redesign” (a customer-oriented view). The traditional process of massmarketing is being challenged by the new approach of one-to-one marketing. In the traditional process, the marketing goal is to reach more customers and expand the customer base. But given the high cost of acquiring new customers, it makes better sense to conduct business with current customers. In so doing, the marketing focus shifts away from the breadth of customer base to the depth of each customers needs.The performance metric changes from market share to so-called “wallet share”. Businesses do not just deal with customers in order to make transactions; they turn the opportunity to sell products into a service experience and endeavor to establish a long-term relationship with each customer.The advent of the Internet has undoubtedly contributed to the shift of marketing focus. As on-line information becomes more accessible and abundant, consumers become more informed and sophisticated. They are aware of all that is being offered, and they demand the best. To cope with this condition, businesses have to distinguish their products or services in a way that avoids the undesired result of becoming mere commodities. One effective way to distinguish themselves is with systems that can interact precisely and consistently with customers. Collecting customer demographics and behavior data makes precision targeting possible. This kind of targeting also helps when devising an effective promotion plan to meet tough competition or identifying prospective customers when new products appear. Interacting with customers consistently means businesses must store transaction records and responses in an online system that is available to knowledgeable staff members who know how to interact with it. The importance of establishing close customer relationships is recognized, and CRM is called for.It may seem that CRM is applicable only for managing relationships between businesses and consumers. A closer examination reveals that it is even more crucial for business customers. In business-to-business (B2B) environments, a tremendous amount of information is exchanged on a regular basis. For example, transactions are more numerous, custom contracts are more diverse, and pricing schemes are more complicated. CRM helps smooth the process when various representatives of seller and buyer companies communicate and collaborate. Customized catalogues,personalized business portals, and targeted product offers can simplify the procurement process and improve efficiencies for both companies. E-mail alerts and new product information tailored to different roles in the buyer company can help increase the effectiveness of the sales pitch. Trust and authority are enhanced if targeted academic reports or industry news are delivered to the relevant individuals. All of these can be considered among the benefits of CRM. Cap Gemini conducted a study to gauge company awareness and preparation of a CRM strategy 1. Of the firms surveyed, 65% were aware of CRM technology and methods; 28% had CRM projects under study or in the implementation phase; 12% were in the operational phase. In 45% of the companies surveyed, implementation and monitoring of the CRM project had been initiated and controlled by top management. Thus, it is apparent that this is a new and emerging concept that is seen as a key strategic initiative.This article examines the concepts of customer relationship management and one of its components, data mining. It begins with an overview of the concepts of data mining and CRM, followed by a discussion of evolution, characteristics, techniques, and applications of both concepts. Next, it integrates the two concepts and illustrates the relationship, benefits, and approaches to implementation, and the limitations of the technologies. Through two studies, we offer a closer look at two data mining techniques: Chi-square Automatic Interaction Detection (CHAID) and Neural Networks. Based on those case studies, CHAID and neural networks are compared and contrasted on the basis of their strengths and weaknesses. Finally, we draw conclusions based on the discussion.2.1. Definition“Data mining” is defined as a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data 2. The term is an analogy to gold or coal mining; data mining finds and extracts knowledge (“data nuggets”) buried in corporate data warehouses, or information that visitors have dropped on a website, most of which can lead to improvements in the understanding and use of the data. The data mining approach is complementary to other data analysis techniques such as statistics, on-line analytical processing (OLAP), spreadsheets, and basic data access. In simple terms, data mining is another way to find meaning in data.Data mining discovers patterns and relationships hidden in data 3, and is actually part of a larger process called “knowledge discovery” which describes the steps that must be taken to ensure meaningful results. Data mining software does not, however, eliminate the need to know the business, understand the data, or be aware of general statistical methods. Data mining does not find patterns and knowledge that can be trusted automatically without verification. Data mining helps business analysts to generate hypotheses, but it does not validate the hypotheses.2.2. The evolution of data miningData mining techniques are the result of a long research and product development process. The origin of data mining lies with the first storage of data on computers, continues with improvements in data access, until today technology allows users to navigate through data in real time. In the evolution from business data to useful information, each step is built on the previous ones. Table 1 shows the evolutionary stages from the perspective of the user.In the first stage, Data Collection, individual sites collected data used to make simple calculations such as summations or averages. Information generated at this step answered business questions related to figures derived from data collection sites,such as total revenue or average total revenue over a period of time. Specific application programs were created for collecting data and calculations. The second step, Data Access, used databases to store data in a structured format. At this stage, company-wide policies for data collection and reporting of management information were established. Because every business unit conformed to specific requirements or formats, businesses could query the information system regarding branch sales during any specified time period.Once individual figures were known, questions that probed the performance of aggregated sites could be asked. For example, regional sales for a specified period could be calculated. Thanks to multi-dimensional databases, a business could obtain either a global view or drill down to a particular site for comparisons with its peers (Data Navigation). Finally, on-line analytic tools provided real-time feedback and information exchange with collaborating business units (Data Mining). This capability is useful when sales representatives or customer service persons need to retrieve customer information on-line and respond to questions on a real-time basis.Information systems can query past data up to and including the current level of business. Often businesses need to make strategic decisions or implement new policies that better serve their customers. For example, grocery stores redesign their layout to promote more impulse purchasing. Telephone companies establish new price structures to entice customers into placing more calls. Both tasks require an understanding of past customer consumption behavior data in order to identify patterns for making those strategic decisionsand data mining is particularly suited to this purpose. With the application of advanced algorithms, data mining uncovers knowledge in a vast amount of data and points out possible relationships among the data. Data mining help businesses address questions such as, “What is likely to happen to Boston unit sales next month, and why?” Each of the four stages were revolutionary because they allowed new business questions to be answered accurately and quickly 4.The core components of data mining technology have been developing for decades in research areas such as statistics, artificial intelligence, and machine learning. Today, these technologies are mature, and when coupled with relational database systems and a culture of data integration, they create a business environment that can capitalize on knowledge formerly buried within the systems.2.3. Applications of data miningData mining tools take data and construct a representation of reality in the form of a model. The resulting model describes patterns and relationships present in the data. From a process orientation, data mining activities fall into three general categories(see Fig. 1):Discoverythe process of looking in a database to find hidden patterns without a predetermined idea or hypothesis about what the patterns may be.Predictive Modelingthe process of taking patterns discovered from the database and using them to predict the future.Forensic Analysisthe process of applying the extracted patterns to find anomalous or unusual data elements.Data mining is used to construct six types of models aimed at solving business problems: classification, regression, time series, clustering, association analysis, and sequence discovery 3. The first two, classification and regression, are used to make predictions, while association and sequence discovery are used to describe behavior. Clustering can be used for either forecasting or description.Companies in various industries can gain a competitive edge by mining their expanding databases for valuable, detailed transaction information. Examples of such uses are provided below.Each of the four applications below makes use of the first two activities of data mining: discovery and predictive modeling. The discovery process, while not mentioned explicitly in the examples (except in the retail description), is used to identify customer segments. This is done through conditional logic, analysis of affinities and associations, and trends and variations. Each of the application categories described below describes some sort of predictive modeling. Each business is interested in predicting the behavior of its customers through the knowledge gained in data mining . RetailThrough the use of store-branded credit cards and point-of-sale systems, retailers can keep detailed records of every shopping transaction. This enables them to better understand their various customer segments. Some retail applications include 5:Performing basket analysisAlso known as affinity analysis, basket analysis reveals which items customers tend to purchase together. This knowledge can improve stocking, store layout strategies, and promotions. Sales forecastingExamining time-based patterns helps retailers make stocking decisions. If a customer purchases an item today, when are they likely to purchase a complementary item?Database marketingRetailers can develop profiles of customers with certain behaviors, for example, those who purchase designer labels clothing or those who attend sales. This information can be used to focus costeffective promotions.Merchandise planning and allocationWhen retailers add new stores, they can improve merchandise planning and allocation by examining patterns in stores with similar demographic characteristics. Retailers can also use data mining to determine the ideal layout for a specific store.2.3.2. BankingBanks can utilize knowledge discovery for various applications, including 5:Card marketingBy identifying customer segments, card issuers and acquirers can improve profitability with more effective acquisition and retention programs, targeted product development, and customized pricing.Cardholder pricing and profitabilityCard issuers can take advantage of data mining technology to price their products so as to maximize profit and minimize loss of customers. Includes risk-based pricing.Fraud detectionFraud is enormously costly. By analyzing past transactions that were later determined to be fraudulent, banks can identify patterns.Predictive life-cycle managementData mining helps banks predict each customers lifetime value and to service each segment appropriately (for example, offering special deals and discounts).译文：客户关系管理中的数据挖掘技术Chris Rygielski , Jyun-Cheng Wang , David C. Yen 摘要近年来，技术的进步让关系营销成为一个现实。如数据仓库，数据挖掘和一系列管理软件等技术已经取得了客户关系管理的新领域，在那里公司可以赢得竞争优势。特别是通过数据挖掘中从大型数据库隐藏的预测信息的提取，企业可以识别有价值的客户，预测客户未来的行为，并使企业积极进取，做出知识驱动的决策。通过数据挖掘移动可能超越过去的事件的分析，自动化是适应于未来的分析，通常用历史为导向的工具，提供了诸如决策支持系统。数据挖掘工具回答了在过去太费时追求的业务问题。然而，这些问题的答案使客户关系管理成为可能。各种技术在数据挖掘软件存在，不同类型的应用程序都拥有自身的优势和挑战。在神经网络和卡方自动交互检测（CHAID）中存在一个特殊的二分法。虽然不同的方法于大量的境界数据挖掘，一些数据挖掘类型用于要完成的各项目标的使用，对当今的客户关系管理理念来说是很必要的。 2002 Elsevier科学有限公司保留所有权利。关键词：客户关系管理（CRM） 关系营销 数据挖掘 神经网络 卡方自动交互检测（CHAID） 私隐权1.简介 今天，一个新的商业文化正在发展。因此，客户关系经济学在根本途径中不断变化，与此同时企业都面临处理这些变化要实施新的解决方案和战略的需要。大规模生产和大规模营销的概念，最先是在工业革命时被创造，现在正在被新的观念所取代，其中客户关系是中央企业的问题。今天的企业越来越通过对客户生命周期分析关注客户价值。这些工具和数据仓库技术，数据挖掘和其他客户关系管理（CRM）技术在关系营销的概念上提供了新的商业机遇。“卖出建造重新设计”（以客为本的观点）正在取代由“设计建造销售”（以产品为导向的观点）的旧模式。传统大规模营销的过程在一对一营销的新方法上被质疑。在传统的过程中，营销的目标是吸引更多客户，扩大客户群。不过，考虑到获取新客户的成本，它可以更好地进行与现有客户的业务。在这样做时，营销重点从客户群的宽度转移到每个客户的深度需求。该性能指标从市场份额到所谓的“钱包份额”变化。企业不只是为了进行交易而应付客户，他们把握了运用服务体验销售产品，并努力与每一位客户建立长期合作关系的机会。互联网的出现，无疑有助于市场重点的转变。由于网上信息变得更方便和丰富，消费者变得更加明智和成熟。他们从所有正在提供的信息中知道，他们要求最好的。为了应付这种情况，企业必须分清他们的产品或服务的方式，避免成为单纯的商品这令人失望的结果。一个有效的方法来区分使用恰当始终的与客户进行互动的系统。收集客户的人口统计和行为的数据，使精确定位成为可能。这定位有助于在什么时候制定一个有效的推广计划，以满足激烈的竞争或当新产品出现时识别潜在客户标识。坚持与客户交互 ，意味着企业必须在一个网上系统中存储交易记录和反应，这样可用使有知道如何交互知识的工作人员利用。建立密切的客户关系的重要性是公认的和被客户关系管理（CRM）呼吁的。这可能好像CRM是只适用于企业和消费者之间的管理关系。仔细观察发现，这是对商业客户更加重要的。在企业对企业（B2B）的环境中，巨大的信息量在定期交换。例如，交易是多不胜数，合同定制更加多样化，定价计划是更为复杂。 CRM可帮助各代表卖方和买方公司沟通和协作的过程顺利。定制目录，个性化的商业门户网站，并提供针对性的产品可以简化采购进程，提高两家公司的效率。电子邮件警报和新产品信息在买方量身定做不同的角色可以帮助公司增加推销的有效性。信任和权威是否有针对性的学术报告或行业消息被传递到有关人士的加强。这些全可被视为在客户关系管理的好处。凯捷安进行了一项研究，以了解公司认识和一个CRM战略1的准备。在接受调查的公司中，65意识到CRM技术和方法，28有对CRM项目有所研究或在实施阶段; 12是在实际运作阶段。 45的受访公司，对CRM项目的实施和已开展由最高控制管理层的监测。因此，很明显，这是一个看作是一个关键的战略举措的新兴概念。本文探讨了客户关系管理理念和其一组成部分，数据挖掘。它始于一个数据挖掘和客户关系管理的概念的概述，接着讨论演化、特点、技术中的应用,并阐述了两个概念。接下来，它集成了两个概念，并说明的关系，利益，以及实现方法，以及局限性的技术。经过两年的研究，我们提供了两个数据挖掘细看技巧：卡方自动交互检测（CHAID）和神经网络。在这些案例研究的基础上，CHAID和神经网络在各自的长处和短处的基础上进行了比较和对比。最后，我们根据讨论得出了的结论。2.数据挖掘：概述 2.1.定义 “数据挖掘”被定义为一个使用统计算法来发现数据模式和相关性2复杂的数据搜索功能。这个词是一个黄金和煤炭开采类比;数据挖掘发现和提取埋葬在企业数据仓库里的知识（“数据掘金“），或游客从网站上下载的信息，其中大部分可能导致在理解和使用的数据上有所改进。数据挖掘方法与其他数据分析是相辅相成的，如统计技术，在线分析处理（OLAP），电子表格，和基础数据的访问。简单来说，数据挖掘是另一种方式在数据中找到意义。数据挖掘发现数据中隐藏的模式和关系3，其实是一部分所谓的“知识发现”的更大过程，用来描述一个必须采取确保有意义的结果的步骤。数据挖掘软件也不具备，但是，消除了解业务，理解数据，或者是普遍知道统计方法的需要。数据挖掘没有找到模式和可信任的情况下自动验证的知识。数据挖掘帮助业务分析师产生假设，但它不验证这假说。2.2.数据挖掘的演化 数据挖掘技术是一个长期的研究和产品发展过程的结果。数据挖掘根源在于第一个存储数据的计算机上，继续与数据访问的改进，直到今天的技术允许用户浏览实时数据。在从业务数据到有用的信息进化中，每一步都是建立在之前的每一步。表1从用户的角度上显示了进化的阶段。在第一阶段，数据收集，个别网站采用数据收集用来如求和或平均值的简单计算。在此阶段产生的信息回答了有关数据来源于数据采集地点的商业问题，如总收入或在一段时间内总收入的平均值。具体应用方案是用来收集数据和计算的建立。第二步，数据访问，使用数据库在一个结构化的格式上存储数据。在这个阶段，全公司的数据收集政策和管理信息报告建立了。因为每一个业务单位遵照特定的要求或格式，企业可以在任何特定时间内查询信息系统对于分