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1、外 文 翻 译毕业设计题目： CRM系统中商业智能模块的设计与开发 原文1：A model of customer relationship management and businessintelligence systems for catalogue and online retailers 译文1：客户关系管理和商业智能系统在商品目录和在线零售商中的应用模型 原文2：Intelligent profitable customers segmentation system based on business intelligence tools 译文2：基于商业智能工具的智能盈利客户细分
2、系统 A model of customer relationship management and business intelligence systems for catalogue and online retailersAs more retailers evolve into customer-centric and segment-based business, business intelligence (BI) and customer relationship management (CRM) systems are playing a key role in achiev
3、ing and maintaining competitive advantage. For the past ten years, the authors have had the rare opportunity of observing and interviewing employees and managers of three different management teams at three separate Fingerhut companies as they experimented with various ITs for their companies. When
4、the first Fingerhut company peaked in 1998, as many as 200 analysts and 40 statisticians mined the database for insights that helped predict consumer shopping patterns and credit behavior. Data mining and BI helped Fingerhut spot shopping patterns, bring product offerings to the right customers, and
5、 nurture customer relationships. By 1998, Fingerhut was the second largest catalogue retailer in the U.S. with revenues nearing $2 billion. However, after Federated acquired Fingerhut in 1999 and made it a subsidiary, Fingerhut Net, it suffered great losses and was eventually liquidated. Finally, a
6、new company, Fingerhut Direct Marketing, was resurrected in 2002 under a new management team, and it once again became successful. What went right? What went wrong? The paper concludes with CRM and BI systems success factors and a discussion of lessons learned.1. IntroductionThe use of IT has create
7、d new ways for firms to exploit vast potentials of customer relationships that have never been exploited before. With growing competition from both traditional and online businesses, keeping customers satisfied, increasing potential sales, and maintaining customer loyalty become strategically import
8、ant to business success. To improve and exploit customer relationships, business intelligence (BI) tools are used to assist CRM systems focus on decision support, market research, target marketing, customer service, and customer collaboration in products and services.Despite numerous CRM studies, ve
9、ry little effort has been made in incorporating consumer preferences for customer satisfaction and relationships. Wang and Head 10 report that most research on consumer behavior addresses the acquisition stage, while research in the retention stage is still in its infancy. This paper deals with this
10、 paucity of research, and presents case studies on the success and failure of customer relationships and business potential sales, and maintaining customer loyalty become strategically important to business success. To improve and exploit customer relationships, business intelligence (BI) tools are
11、used to assist CRM systems focus on decision support, market research, target marketing, intelligence. The paper identies strategies and the successes and failures at Fingerhut Inc, the second largest catalogue mail order company in the U.S. in 1999, and addresses the following questions:1.What are
12、the impacts of price discrimination on customer relationships?2.What are the impacts of CRM and/or BI systems on catalogue and online retailing businesses?3.What are the impacts of high switching costs and/or lock-in strategies on customer relationships?4.What is a successful outcome model for catal
13、ogue and online businesses?6. Lessons learned, insights, and success factorsAs e-commerce evolves, new visions and paradigms emerge. How do conventional management strategies and processes compare with the experience gained from the successes and failures at Fingerhut? And how do conventional manage
14、ment strategies and processes compare with the experience gained from the CRM successes and failures at Fingerhut? The lessons learned were:1. The use of BI and CRM at Fingerhut reduces the threats of price and cost transparency and disintermediation. Early e-commerce visions predicted that price an
15、d cost transparency would cause customers to move to retailers who offered lowest prices, and that direct sales would eliminate intermediaries. With innovation in OLAP and DM, Fingerhut was able to lock-in customers in the sub-prime market, predict buyer patterns, and maintain customers trust and lo
16、yalty. DM also allowed Fingerhut to focus its efforts on nurturing buyer behaviors. This was a winwin strategy which resulted in customer satisfaction and trust while bringing more profits to Fingerhut.2. High switching costs do not hurt customer satisfaction. Because Fingerhut tailored its products
17、 and credit services to its customers, customer satisfaction level and loyalty was high. It was only after Federated extended credit beyond customers ability to pay that customers became dissatisfied. As we have seen in the credit market recently, sub-prime mortgage lenders who offer credit beyond c
18、ustomers ability to pay also suffer failure.3. Price discrimination among sales channels hurt customer relationships when lock-in and high switching costs are removed. After Federated gave former Fingerhut customers credit cards, they shopped elsewhere for lower prices.4. Success in the catalogue ma
19、il order business does not guarantee success in online e-commerce. Pundits predicted that it would be easy for catalogue mail order companies to move into online retailing because they operated without physical stores. However, having past experience in order fulfillment and in running businesses wi
20、thout physical stores does not automatically translate into success in online business.作者：Dien D. Phan，Doug Vogel国籍：USA出处：Information & Management，2009，9：1-9.客户关系管理和商业智能系统在商品目录和在线零售商中的应用模型摘要：随着越来越多的零售商发展以客户为中心和以细分为基础的商业，商业智能（BI）和客户关系管理（CRM）系统在实现和保持竞争优势中正发挥着关键作用。在过去的十年中，作者曾难得的机会观察和采访了三个不同在线家居购物公司的员工和
21、三个不同的管理团队经理，因为他们为自己的公司试验着各种智能交通系统。当第一个芬格公司在1998年达到高峰时，有多达200名分析师和40统计人员采集数据库的数据，这样有助于预测消费者行为模式和信贷行为。数据挖掘和商业智能帮助芬格发现购物模式，使产品到正确的客户手里，培养客户关系。到1998年，在线家居购物已经是美国第二大商品目录零售商，收入接近20亿美元。然而，联邦于1999年收购在线家居购物并使其成为一个子公司，名叫在线家居购物网。但在线家居购物遭受了重大损失，并最终被清算。最后，一家名为在线家居购物直销的新公司在新的管理团队带领下诞生于2002年，并再次取得了成功。哪里是对的？哪里出了问题？
22、本文总结了客户关系管理和商业智能系统的成功因素并吸取了经验教训。1、引言企业利用信息技术创造了新的途经来利用拥有巨大潜力的客户关系，这在以前是从来没有被利用过的。随着传统业务和在线业务竞争的越来越激烈，让顾客满意，增加潜在的销售，维护客户忠诚度成为商业成功中重要的战略意义。在改善和利用客户关系中，商业智能（BI）工具主要在决策支持，市场调研，目标市场营销，客户服务，产品和服务上的客户合作上面帮助客户关系管理系统。尽管有关客户关系管理的研究有很多，但很少有把消费者的喜好包含进客户满意度和客户关系中。王和海德的报告主要是研究当保留期的研究仍处于初级阶段时，用消费者行为解决大部分收购阶段。本文解决了
23、这方面的不足，并提出了客户关系和商业智能中成功和失败的例子。本文论述了在1999年时作为美国第二大商业目录邮购公司的在线家居购物公司的战略决策、成功和失败，并解决以下问题： 1、客户关系中价格歧视会产生什么影响？ 2、商业目录和网上零售业务中应用客户关系管理和/或商业智能会产生什么影响？ 3、客户关系中应用高转换成本和/或固定的战略决策会产生什么影响？4、商业目录和在线业务的一个成功的最终模型是什么？6 经验教训，见解和成功的因素随着电子商务的发展，新的视野和范例出现了。如何将传统的管理战略和流程与在线家居购物所获取的成功和失败的经验相比较？以及如何将传统的管理战略和流程与在线家居购物从客户关
24、系管理中获得的成功和失败的经验相比较？以下是所吸取的教训：1商业智能和客户关系管理的应用，使在线家居购物减少了来自于价格、成本透明度和中介的威胁。早期的电子商务梦想通过增加价格和成本的透明度，使消费者转向提供最低价格的零售商，而且直销将消除经销商这一环节。随着联机分析处理和快讯商品广告的发展，在线家居购物能够抓住次级市场的顾客，预测买方格局，以及维护客户的信任和忠诚。快讯商品广告也促使在线家居购物把努力集中在重点培育买方行为。这是一个双赢的战略，因为在获得客户满意度和信任的同时，给在线家居购物带来更多的利润。2高转换成本不会伤害客户满意度。由于在线家居购物向顾客提供产品定制和信贷服务，所以它的
25、客户满意度和忠诚度都很高。只是后来联邦扩大信贷业务，超出了客户的支付能力，从而导致客户的不满。正如我们现在看到的信贷市场，那些次级按揭贷款的提供信贷也遭受了损失，因为他们提供的信贷业务超出了客户的支付能力。3当锁定和高转换成本被取消时，销售渠道之间的价格歧视会伤害客户关系。所以在联邦把信用卡发放给在线家居购物以前的顾客后，他们却去了其他能提供更低价格的地方购物。4.商业目录邮购业务的成功并不能保证在线电子商务的成功。专家预言，商业目录邮购公司进军网上零售是很容易的，因为他们没有经营实体商店。不过，没有实体商店而拥有履行订单和企业经营经验的企业并不会自动转化为成功的网上业务。作者：Dien D.
26、 Phan，Doug Vogel国籍：USA出处：Information & Management，2009，9：1-9.Intelligent profitable customers segmentation system based on business intelligence toolsAbstractFor the success of CRM, it is important to target the most profitable customers of a company. Many CRM researches have been performed to calcu
27、late customer profitability and develop a comprehensive model of it. Most of them, however, had some limitations and accordingly the customer segmentation based on the customer profitability model is still underutilized. This paper aims at providing an easy, efficient and more practical alternative
28、approach based on the customer satisfaction survey for the profitable customers segmentation. We present a multiagent-based system, called the survey-based profitable customers segmentation system that executes the customer satisfaction survey and conducts the mining of customer satisfaction survey,
29、 socio-demographic and accounting database through the integrated uses of business intelligence tools such as DEA (Data Envelopment Analysis), Self-Organizing Map (SOM) neural network and C4.5 for the profitable customers segmentation. A case study on a Motor companys profitable customer segmentatio
30、n is illustrated.2. Profitable customer segmentation and customer satisfaction surveyTraditional customer segmentation models were based on demographic, attitudinal, and psychographic attributes of a customer (Griffin, 2003). They gave too simple results and poor accuracy for todays complicated busi
31、ness environment. Recently, the customer segmentation based on customer transactional and behavioral data (e.g. purchases type, volume and history, call center complaints, claims, web activity data,etc.) collected by various information systems is commonly used. However, the customer segmentation ba
32、sed on his/her profitability to a company is still underutilized.Customer profitability is a customer-level measure that refers to the revenues less the costs which one particular customer generates over a given period of time and has been studied the name of Customer value, Customer Lifetime Value,
33、 LTV and Customer Equity. Many customer profitability researches focused on the future cash flow derived from the past profit contribution and did not considered the change of profit contribution resulted from the customer defection (Berger & Nasr, 1998; Gupta & Lehmann, 2003).Hwang, Jung, and Suh (
34、2004) suggested a new customer profitability model considering past profit contribution, potential benefit indicated cross-selling and up-selling opportunity, and defection probability of a customer measured customer loyalty and segmented customers based on their model. However, they said that it ha
35、d some limitations such as not considering the reactivation possibility of customers, attracting/servicing cost and causes of customer defection. It is difficult and complicated to develop an effective and exact customer profitability model and segment profitable customers based on that model. In th
36、is study, we provide an easy, efficient and more practical alternative approach through the customer satisfaction survey for the profitable customers segmentation instead of using that model. The typical customer satisfaction survey collects data on the causal context of satisfaction, i.e. anteceden
37、ts (e.g. perceived performance of various product attributes/service) and consequences (e.g. overall satisfaction level, repurchase intentions and word-of-mouth intentions). According to the Satisfaction-Profit Chain principle (Anderson & Mittal, 2000), improving product and service attributes cause
38、s increased customer satisfaction, increased customer satisfaction leads to greater customer retention and improving customer retention greater profitability.Empirical Researches have shown that increasing overall satisfaction leads to greater repurchase intentions, as well as to actual repurchase b
39、ehavior and companies with high customer satisfaction and retention can expect higher profits (Reichheld & Frederick, 1996).In this study, we use the customers overall satisfaction level, repurchase intentions, word-of-mouth intentions obtained from the customer satisfaction survey and his/her profi
40、t/loss to a company derived from the accounting database of it for the first step of profitable customers segmentation.3. Profitable customers segmentation based on customer satisfaction surveyWe propose a survey-based profitable customers segmentation system (SPCSS) based on data mining and agent t
41、echnology that designs, executes (on-line, e-mail, etc.) customer satisfaction survey and conducts predefined mining processes for the profitable customers segmentation. SPCSS has a multi-agent based architecture and the integration of predefined mining processes into decision support system framewo
42、rk . There are three types of intelligent agents within the SPCSS architecture: Survey management (SM) agent with survey knowledge base that provides system co-ordination, facilitates (mined) knowledge communication, and takes the charge of design and execution of customer satisfaction survey, profi
43、table customers segmentation (PCS) agent that segments profitable customers among all the surveyed customers through the mining of integrated data from the customer satisfaction survey and accounting database and decides the priority order for each non-profitable customer according to the size of po
44、ssibility that he/she is converted to profitable one through the mining of integrated data from the customer satisfaction survey and customer database, and user assistant agent that acts as the intelligent interface agent between the user (e.g. the engineer of customer satisfaction center) and the S
45、PCSS.作者：Jang Hee Lee, Sang Chan Park国籍：South Korea出处：Expert Systems with Applications ，2005，29 ：145152.基于商业智能工具的智能盈利客户细分系统摘要客户关系管理的成功，很重要的就是为公司寻找盈利客户。许多客户关系管理的研究已经进行客户盈利能力的计算，并为它开发了一个全面的模型。尽管如此，他们大多有一定的局限性，基于客户盈利能力模型的相应客户细分仍未得到充分利用。本文旨在提供一种简单，有效和更务实的替代方法，它是基于盈利客户细分后的客户满意度调查。我们提出了一个基于多方代理的系统，名为基于调查后的
46、盈利客户细分系统，用来实施客户满意度调查，管理客户满意度调查的收集，社会人口统计，通过商业智能工具的综合使用核算数据库，其中商业智能工具如DEA（数据包络分析法），自组织映射（SOM）的神经式网络和进行有利润客户细分的C4.5。下面用一个关于汽车公司盈利客户细分的研究案例来进行说明。2 盈利客户细分和客户满意度调查 传统的客户细分模型是基于人口，态度，以及客户心理属性（格里芬，2003）。他们给的结果过于简单，对于今天的复杂的商业环境来说太不准确了。最近，基于各种信息系统收集的客户交易和行为数据（如购买的类型，数量和历史，呼叫中心的投诉，索赔，网络活动的数据等）的客户细分频繁被使用。然而，基于
47、他/她盈利能力的客户细分对一个公司来说仍然是不足的。客户盈利能力是客户等级的衡量，是指收入减去某一特定客户产生超过给定时间，并已研究了客户价值的名称，客户终身价值，按揭成数及客户权益的成本。许多客户盈利能力的研究着眼于从过去的利润贡献来预计未来的现金流，而没有考虑盈利贡献变化造成的客户流失（伯杰和纳斯尔，1998；古普塔和莱曼，2003）。黄，郑，和苏（2004年）提出了新客户的盈利能力模型，这个模型考虑到了过去的盈利贡献，交叉销售的潜在利益和向上销售的机会，测量客户忠诚度来判定顾客流失的可能性和客户细分。但是，他们表示这个模型仍然还有一些局限性，比如没有考虑到客户回流的可能性，吸引/服务成本
48、和客户流失的原因。建立一个有效而准确的客户盈利能力模型和根据该模型细分盈利客户是非常困难和复杂的。在这项研究中，我们提供一种简单，有效和更务实的替代方法，这种方法是用盈利客户细分进行客户满意度调查来替代使用的模型。典型的客户满意度调查是收集跟满意度方面有关的数据，即先例数据（如感知各种产品属性/服务的表现）和后果（如整体满意程度，回购意图和口碑意图）。根据满意度利益链的原理（安德森与米塔尔，2000年），提高产品和服务属性导致增加客户满意度，增加客户满意度带来更大的客户忠诚度，提高客户忠诚度带来更大的利润。实证研究的结果显示，越来越高的整体满意度会带来更多的回购意图和实际的回购行为，公司有高客户满意度和忠诚度就能期望获得更高的利润（赖克尔德及冯检基，1996）。在这项研究中，我们使用了从客户满意度调查中获得的客户的整体满意度，回购意图，口碑意图，并把从核算数据库中获得的他/她的盈利/亏损作为公司进行盈利客户细分的第一步。3 基于客户满意度调查的盈利客户细分我们提供了一项以调查为基础的盈利客户细分系统（SPCSS），它基于数据挖掘和代理技术，并为盈利客户细分预定义挖掘过程，其中代理
