外文翻译--客户关系管理中的数据挖掘技术.doc

外文翻译-客户关系管理中的数据挖掘技术 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 philosophy2002 Elsevier Science Ltd All rights reservedKeywords Customer relationship management CRM Relationship marketing Data mining Neural networksChi-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 marketingThe 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 needsThe 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 customerThe 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 forIt 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 cataloguespersonalized 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 initiativeThis 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 discussion21 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 dataData 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 hypotheses22 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 userIn 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 sitessuch 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 periodOnce 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 basisInformation 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 4The 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 systems23 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 bePredictive Modelingthe process of taking patterns discovered from the database and using them to predict the futureForensic Analysisthe process of applying the extracted patterns to find anomalous or unusual data elementsData 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 descriptionCompanies in various industries can gain a competitive edge by mining their expanding databases for valuable detailed transaction information Examples of such uses are provided belowEach 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 5com 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 5Performing 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 itemDatabase 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 promotionsMerchandise 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 storecom BankingBanks can utilize knowledge discovery for various applications including 5Card marketingBy identifying customer segments card issuers and acquirers can improve profitability with more effective acquisition and retention programs targeted product development and customized pricingCardholder pricing and profitabilityCard issuers can take advantage of data mining technology to price their products so as to imize profit and minimize loss of customers Includes risk-based pricingFraud detectionFraud is enormously costly By analyzing past transactions that were later determined to be fraudulent banks can identify patternsPredictive 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关系营销一个现实如数据仓库数据挖掘和系列管理软件已经取得了客户关系管理的新领域公司可以赢得竞争优势特别是通过数据挖掘从大型数据库隐藏的预测信息提取企业可以识别有价值的客户预测未来的行为并使企业积极进取知识驱动的决策数据挖掘移动可能超越过去的事件的分析自动化未来分析通常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在过去太费时追求业务问题然而这些问题使客户关系管理成为可能各种技术在数据挖掘软件不同类型的应用程序自身的优势和挑战在神经网络卡方自动交互检测CHAID一个特殊的二分法虽然不同的方法大量的境界数据挖掘一些数据挖掘类型用要完成的各项目标的使的客户关系管理理念 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 私隐权简介 一个新的商业文化发展客户关系经济学根本途径不断变化企业都面临处理这些变化实施新的解决方案和战略需要大规模生产和大规模营销的概念在工业革命创造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技术关系营销的概念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传统大规模营销一对一营销的新方法被质疑在传统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进行与现有客户的业务在这样做时营销重点客户群的度转移到每个客户的深度需求该性能指标从市场份额所谓的钱包份额变化企业不只是为了进行交易应付客户他们把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机会互联网的出现无疑有助于市场重点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所有正在提供的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商品令人失望的结果一个有效的方法来区分始终与客户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标识坚持与客户交互 意味着企业必须在一个网上系统存储交易记录和反应可用知道如何交互知识的工作人员建立密切的客户关系的重要性是公认的和CRM呼吁这可能CRM是适用于企业和消费者之间管理关系仔细观察发现这是商业客户更加重要在企业对企业B2B的环境中巨大的信息量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有针对性的学术报告或行业消息被传递到有关人士这些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准备在接受调查的公司65CRM技术和方法28有或在实施阶段 12是在实际运作阶段 45的受访公司CRM项目实施和已开展由最高控制管理监测因此很明显这是一个看作是一个关键的战略举措新兴概念本文探讨了客户关系管理理念和其组成部分数据挖掘它始于一个数据客户关系管理的概念的概述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和神经网络的长处和短处的基础进行了比较和对比最后我们根据讨论得出的结论数据挖掘概述 21定义 数据挖掘被