客户关系管理中的数据挖掘技术外文翻译.doc

1、客户关系管理中的数据挖掘技术外文翻译 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 campaig

2、n management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining?the extraction of hidden predictive information from large databases?organizations can identify valuable customers, predict future behaviors, and

3、 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

4、 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

5、 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

6、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 chang

7、ing 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 t

8、he 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

9、 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,

10、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 custom

11、ers 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

12、.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,

13、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 dat

14、a 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 respo

15、nses 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 consu

16、mers. 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 ar

17、e 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.

18、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

19、among the benefits of CRMCap 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

20、 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 an

21、d 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 approac

22、hes 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 b

23、asis 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 mini

24、ng; 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 t

25、echniques 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 dis

26、covery” 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 au

27、tomatically 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 fir

28、st 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 p

29、erspective 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 a

30、verage total revenue over a period of time. Specific application programs were created for collecting data and calculationsThe 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 informati

31、on 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

32、 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 fe

33、edback 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 an

34、d 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

35、 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 decisions?and data mining is particularly suited to this purpose. With the application of advanced algorithms, data mining uncov

36、ers 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 questi

37、ons 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

38、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 a

39、nd relationships present in the data. From a process orientation, data mining activities fall into three general categoriessee Fig. 1:Discovery?the process of looking in a database to find hidden patterns without a predetermined idea or hypothesis about what the patterns may be.Predictive Modeling?t

40、he process of taking patterns discovered from the database and using them to predict the future.Forensic Analysis?the 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: classif

41、ication, 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 descripti

42、on.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 predicti

43、ve 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 d

44、escribed 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 5.2.3.1. RetailThrough the use of store-branded credit cards and point-of-sale systems, retailers can keep detailed records o

45、f every shopping transaction. This enables them to better understand their various customer segments. Some retail applications include 5:Performing basket analysis?Also known as affinity analysis, basket analysis reveals which items customers tend to purchase together. This knowledge can improve sto

46、cking, store layout strategies, and promotions. Sales forecasting?Examining 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 marketing?Retailers can develop profiles of customers with ce

47、rtain behaviors, for example, those who purchase designer labels clothing or those who attend sales. This information can be used to focus cost?effective promotions.Merchandise planning and allocation?When retailers add new stores, they can improve merchandise planning and allocation by examining pa

48、tterns 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 marketing?By identifying customer segments, card issuers and

49、 acquirers can improve profitability with more effective acquisition and retention programs, targeted product development, and customized pricing.Cardholder pricing and profitability?Card 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 pricing.Fraud detection?Fraud is enormously costly. By analyzing past transactions that were later determined to be fraudulent, bank