數(shù)據(jù)庫(kù)外文文獻(xiàn)翻譯2篇

1、<p><b>　　目 錄</b></p><p>　　1 外文文獻(xiàn)譯文（1）1</p><p>　　1.1建立客戶模型與業(yè)務(wù)數(shù)據(jù):一個(gè)自動(dòng)的方法基于模糊聚類和機(jī)器學(xué)習(xí)1</p><p><b>　　1.2摘要1</b></p><p><b>　　1.3介紹1<

2、;/b></p><p><b>　　1.4提案2</b></p><p>　　2 外文文獻(xiàn)原文（1）3</p><p>　　2.1 title3</p><p>　　2.2 Abstract3</p><p>　　2.3 Introduction3</p><

3、p>　　2.4 Our Proposal5</p><p>　　3 外文文獻(xiàn)譯文（2）6</p><p>　　3.1客戶的知識(shí)關(guān)系管理：整合知識(shí)管理和客戶關(guān)系管理過程6</p><p><b>　　3.2摘要6</b></p><p><b>　　3.3介紹6</b></p&g

4、t;<p><b>　　3.4文獻(xiàn)綜述7</b></p><p>　　3.5提出客戶知識(shí)關(guān)系管理過程的概念模型8</p><p>　　4 外文文獻(xiàn)原文（2）8</p><p>　　4.1 title8</p><p>　　4.2 Abstract8</p><p>　　4.

5、3 Introduction9</p><p>　　4.4 Literature Review10</p><p>　　4.5 Proposed Customer Knowledge Relationship Management Process: A Conceptual Model11</p><p>　　1 外文文獻(xiàn)譯文（1）</p>&l

6、t;p>　　1.1建立客戶模型與業(yè)務(wù)數(shù)據(jù):一個(gè)自動(dòng)的方法基于模糊聚類和機(jī)器學(xué)習(xí)</p><p><b>　　1.2摘要</b></p><p>　　數(shù)據(jù)挖掘(DM)是一門新興學(xué)科,旨在從數(shù)據(jù)中提取知識(shí)使用幾種技術(shù)。DM證明是有用的業(yè)務(wù)數(shù)據(jù)的描述客戶和他們的交易以兆兆字節(jié)。在本文中,我們提出的方法建立客戶模型(也說在文獻(xiàn)資料)與業(yè)務(wù)數(shù)據(jù)。我們的方法是三步。在第一步

7、中,我們使用模糊聚類分類的客戶,即確定客戶群。一個(gè)關(guān)鍵特性是,很多團(tuán)體(或集群)自動(dòng)計(jì)算從數(shù)據(jù)使用劃分熵作為真實(shí)性的標(biāo)準(zhǔn)。在第二步中,我們進(jìn)行降維旨在保持為每組只有客戶的信息最豐富的屬性。為此,我們定義了信息損失量化信息程度的一個(gè)屬性。因此,作為結(jié)果,第二步,我們獲得的消費(fèi)者群每個(gè)描述由一種獨(dú)特的屬性集。在第三個(gè)和最后一步,我們使用摘要神經(jīng)網(wǎng)絡(luò)中獲取有用的知識(shí)從這些組織。真實(shí)世界的數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果揭示了我們的方法的良好性能,應(yīng)該模擬未

8、來的研究。</p><p>　　關(guān)鍵字：數(shù)據(jù)挖掘、客戶利潤(rùn)、客戶關(guān)系、模糊聚類、降維、倒傳遞類神經(jīng)網(wǎng)路、信息原理</p><p><b>　　1.3介紹</b></p><p>　　如果可以探測(cè)和預(yù)測(cè)客戶的行為改變，營(yíng)銷管理能夠和客戶發(fā)展長(zhǎng)期和愉快的關(guān)系。在過去，研究人員通常使用應(yīng)用統(tǒng)計(jì)調(diào)查研究客戶的行為。最近，數(shù)據(jù)挖掘技術(shù)已經(jīng)采用，這些技術(shù)的

9、目標(biāo)通過搜索數(shù)據(jù)庫(kù)以獲取隱式的，未知，和潛在有用信息，包括知識(shí)規(guī)則、約束和規(guī)律。數(shù)據(jù)挖掘，是數(shù)據(jù)庫(kù)的中知識(shí)發(fā)現(xiàn)關(guān)鍵的一步，包括特定算法的模式提取的應(yīng)用，像網(wǎng)絡(luò)、市場(chǎng)、金融和銀行業(yè)務(wù)這些領(lǐng)域已經(jīng)有多方面成功的應(yīng)用。目前，企業(yè)面臨的挑戰(zhàn)是一個(gè)不斷發(fā)展的市場(chǎng)，客戶的需求在不斷地變化。因此，代替對(duì)待所有客戶一樣，企業(yè)可以只選擇那些符合特定的盈利條件的客戶，標(biāo)準(zhǔn)基于他們個(gè)人的需求或消費(fèi)行為。這樣，所發(fā)現(xiàn)的信息可以為市場(chǎng)做更精準(zhǔn)的決策。因此，可以定

10、義對(duì)客戶概括的數(shù)據(jù)挖掘，是簡(jiǎn)單地作為的允許建立描述一組特定的習(xí)慣、態(tài)度和行為客戶檔案的技術(shù)。數(shù)據(jù)挖掘技術(shù)在客戶分析中面臨一些困難，大量的數(shù)據(jù)可用來創(chuàng)建用戶模型，數(shù)據(jù)是否適當(dāng)，數(shù)據(jù)噪音問題和和捕獲人類不確定性行為的必要性。數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)技術(shù)能夠處理大量的數(shù)據(jù)和不確定性。這些特征使這些技術(shù)實(shí)現(xiàn)客戶模型的自動(dòng)生成，提高決策效率。一些文獻(xiàn)提出人工智能技術(shù)可以解決這個(gè)問題。事實(shí)上，許多工業(yè)應(yīng)用程序?yàn)?lt;/p><p>　

11、　第十七文獻(xiàn)中提出一個(gè)集成的數(shù)據(jù)挖掘模型和行為得分模型管理銀行現(xiàn)有的信用卡客戶。區(qū)分基于還款行為時(shí)效性、頻率、貨幣行為和得分的預(yù)測(cè)因子的客戶群，使用自組織映射方法。同樣將銀行的客戶分為三種主要盈利的客戶群，使用先驗(yàn)的關(guān)聯(lián)規(guī)則挖掘不同客戶群的功能屬性。</p><p>　　其他方法也在零售市場(chǎng)得到應(yīng)用，因?yàn)樵趧?dòng)態(tài)的零售市場(chǎng)觀察客戶行為的變化可以幫助管理者建立有效的宣傳活動(dòng)。第五文獻(xiàn)中的模型，合并了客戶行為變量，人口

12、統(tǒng)計(jì)學(xué)變量和事務(wù)數(shù)據(jù)庫(kù)統(tǒng)計(jì)客戶的行為變化。為了挖掘變化模式，相似性和不可預(yù)測(cè)性兩種擴(kuò)展特征用來分析不同時(shí)期模式的相似度。關(guān)聯(lián)規(guī)則挖掘首次發(fā)現(xiàn)客戶行為模式。自從發(fā)現(xiàn)了關(guān)聯(lián)規(guī)則，通過比較兩組不同時(shí)期的數(shù)據(jù)的關(guān)聯(lián)規(guī)則確定客戶行為的變化?；谙惹暗膶W(xué)習(xí)，客戶行為改變包括出現(xiàn)模式、增加模式、流失模式和不確定模式。</p><p>　　第四十文獻(xiàn)中另一個(gè)值得注意的案例，現(xiàn)在這個(gè)模式使用到其他的服務(wù)，目前提供給移動(dòng)電信用戶。使

13、用要素分析，聚類和定量關(guān)聯(lián)規(guī)則這些方法發(fā)現(xiàn)細(xì)分客戶群采用的服務(wù)模式，從這些分析中，確定了三種類別的用戶。第一類用戶由新一代利用額外服務(wù)收費(fèi)的用戶組成，組要為了休閑和娛樂。年輕的一代比年長(zhǎng)的一代更頻繁地使用手機(jī)，他們趨向展示更高的各種不同的附加服務(wù)使用模式。第二類用戶使用實(shí)際的附加服務(wù)，低價(jià)或免費(fèi)的如“數(shù)據(jù)服務(wù)”和“通話服務(wù)”通過“來電顯示請(qǐng)求服務(wù)”。最后一類用戶沒有明顯的特征。這份研究使用關(guān)聯(lián)規(guī)則發(fā)現(xiàn)在每個(gè)用戶群，為不同用戶組的移動(dòng)服務(wù)

14、市場(chǎng)提供戰(zhàn)略指導(dǎo)。</p><p>　　第29文獻(xiàn)中模型挖掘客戶行為以幫助經(jīng)理們?yōu)楣咎岢龈玫拇黉N活動(dòng)和其他相關(guān)決策。關(guān)聯(lián)規(guī)則的關(guān)系數(shù)據(jù)庫(kù)設(shè)計(jì)實(shí)現(xiàn)了挖掘系統(tǒng)幫助電子目錄設(shè)計(jì)和促銷策略設(shè)計(jì)。關(guān)聯(lián)規(guī)則在相關(guān)的數(shù)據(jù)庫(kù)的應(yīng)用挖掘消費(fèi)行為，以便生成零售業(yè)購(gòu)物中心的交叉銷售的電子目錄設(shè)計(jì)和營(yíng)銷方案。</p><p>　　在本文中，我們提出一種方法從業(yè)務(wù)數(shù)據(jù)中開發(fā)自動(dòng)客戶分析(模型)。它涉及到三個(gè)步驟

15、，在第一步中，用模糊聚類方法分類的客戶。模糊聚類算法關(guān)鍵的一步是決定劃分聚類的組數(shù)，自動(dòng)從數(shù)據(jù)使用分區(qū)熵作為一個(gè)有效措施。在第二步中，維(或?qū)傩詳?shù)量)對(duì)于每個(gè)集群(或一組客戶)減少選擇只有信息最豐富的屬性。選擇是基于屬性信息的損失；量計(jì)算運(yùn)用信息熵的屬性。因此，第二步的結(jié)果，我們獲取幾組的消費(fèi)者群，他們每個(gè)人都被一個(gè)不同的組，每組的屬性被認(rèn)定為信息最豐富的。在第三步，最后一個(gè)步驟中，每個(gè)聚類減少訓(xùn)練，從反饋網(wǎng)絡(luò)中提取有用的知識(shí)。因此，連

16、接客戶分析(或模型)我們獲取一組反饋網(wǎng)絡(luò)編碼和分類未知潛在的客戶。下文組織如下，第二章中我們?cè)敿?xì)描述我們的模型。第三章中將進(jìn)行實(shí)驗(yàn)分析了，而最后一章是論文總結(jié)和對(duì)未來研究的發(fā)展方向。</p><p><b>　　1.4提案</b></p><p>　　我們的方法是三步，總結(jié)了圖1。首先，我們使用模糊聚類算法細(xì)分客戶群。該方法的一個(gè)重要特性是使用劃分熵作為一個(gè)有效性度量

17、自動(dòng)計(jì)算聚類的數(shù)量。通過這種方式，最優(yōu)的聚類數(shù)量是能夠產(chǎn)生最低劃分熵。第一步的結(jié)果，從消費(fèi)習(xí)慣，,收入等相似性方面細(xì)分獲取幾組描述客戶類別。第二步，繼續(xù)降維保持聚類的相關(guān)屬性。事實(shí)上，聚類中的屬性不是所有都是相關(guān)的，有一些屬性應(yīng)該刪除。重復(fù)步驟，利用決策樹方法來決定一個(gè)屬性是否重要，關(guān)于給定一組客戶中使用定義信息丟失的閾值。第三，每個(gè)聚類被導(dǎo)入倒傳遞網(wǎng)絡(luò)提取有用的信息。整個(gè)算法的結(jié)果，獲取客戶細(xì)分結(jié)果。這樣，新一輪分類后以前未知的客戶就

18、變成非常容易的任務(wù)。接下來的部分，將詳細(xì)描述方法中每一步。</p><p>　　2.2 第二步，屬性的選擇</p><p>　　自然，在相同的集群(同一群顧客)中，并不是所有的初始屬性是有效，其中有些應(yīng)該被丟棄。衡量一個(gè)屬性傳達(dá)的信息，在一個(gè)給定的聚類的值，使用基于該屬性值的頻率的熵。我們將使用圖2中相同的聚類方法計(jì)算這個(gè)頻率，但是只適用于一個(gè)數(shù)據(jù)集組成的屬性值。在給定一組客戶中一個(gè)屬性可

19、能值的數(shù)量無法準(zhǔn)確計(jì)算，特別是可能會(huì)非常接近的數(shù)值屬性值，但不是完全相同的。給定客戶群的屬性運(yùn)用圖2的聚類方法，獲取一組嵌入屬性值非常類似的聚類；因此相應(yīng)的集群多次相似的值被視為一個(gè)單一值。在進(jìn)一步深入學(xué)習(xí)之前，現(xiàn)在我們需要以下的注釋。</p><p>　　2 外文文獻(xiàn)原文（1）</p><p><b>　　2.1 title</b></p><p

20、>　　Building customer models from business data: an automatic approach based on fuzzy clustering maching learning</p><p>　　2.2 Abstract</p><p>　　Data mining (DM) is a new emerging discipline t

21、hat aims to extract knowledge from data using several techniques. DM turned out to be useful in business where the data describing the customers and their transactions is in the order of terabytes. In this paper, we prop

22、ose an approach for building customer models (said also pro?les in the literature) from business data. Our approach is three-step. In the ?rst step, we use fuzzy clustering to categorize customers, i.e., determine groups

23、 of customers</p><p>　　Keywords: Data mining; customer pro?ling; customer relationships management (CRM); fuzzy clustering; dimensionality reduction; backpropagation networks; information theory.</p>

24、<p>　　2.3 Introduction</p><p>　　Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in their behavior. In the past, researchers gene

25、rally used to apply statistical surveys to study customer behavior. Recently, data mining techniques have been adopted. These techniques aim to search through a database to obtain implicit, previously unknown, and potent

26、ially useful information including knowledge rules, constraints and regularities. Data mining, a key step in K</p><p>　　As a result, the discovered information can be ascertained to support better decisionma

27、king in marketing. Consequently, one can de?ne data mining in customer pro?ling simply as being the technology that allows building customer pro?les each describing the speci?c habits, attitudes and behavior of a group o

28、f customers. Some of the di?culties faced by data mining techniques for customer pro?ling are the amount of data available to create user models, the adequacy of the data, the noise within that</p><p>　　r th

29、e interested reader to theThe model in Ref. 17 proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map was used to identify groups of cust

30、omers based on repayment behavior and recency, frequency, monetary behavioral and scoring predictors. It also classi?ed bank customers into three major pro?table groups of customers. The resulting groups of customers wer

31、e then pro?led by customer’s feature attributes determin</p><p>　　Other works are also developed in retail marketing because understanding changes in customer behavior in the dynamic retail market can help m

32、anagers to establish e?ective promotion campaigns. The model in Ref. 5 integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavio

33、r. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance bet</p><p>　　Another work worthy of notice is that proposed in Ref. 40

34、which presents the patterns of use for additional services that are currently provided to mobile telecommunication subscribers. Factor analysis, clustering and quantitative association rules are used to ?nd the service a

35、doption patterns of segmented groups. From the analysis, three categories of users are identi?ed. The ?rst group consists of a new generation of customers who utilize chargeable additional services using the “direct butt

36、on</p><p>　　The model in Ref. 29 mines customer behavior to assist managers in developing better promotion and other relevant policies for a ?rm. The association rules of the relational database design are i

37、mplemented in the mining system which provides electronic catalog designs and promotional policies. The association rules from relational database design are utilized to mine consumer behavior in order to generate cross-

38、selling proposals for an electronic catalog design and marketing for a retailing mall</p><p>　　In this paper, we propose an approach to develop automatically customer pro?les (said also models) from business

39、 data. It involves three steps. In the ?rst step, we use fuzzy clustering to categorize customers. A key feature of this fuzzy clustering model is that the number of groups is determined automatically from data using the

40、 partition entropy as a validity measure. In the second step, the dimension (or number of attributes) for each cluster (or group of customers) is reduced by selecting o</p><p>　　2.4 Our Proposal</p>

41、<p>　　Our approach is three-step and is summarized in Fig. 1. First, we use fuzzy clustering to identify groups of customers. An important characteristic of our approach is that the number of clusters is computed au

42、tomatically using the partition entropy as a validity measure. In this way, the optimal number of clusters is that producing the lowest partition entropy. As a result to this ?rst step, we obtain a set of groups each des

43、cribing a customer category whose consuming habits, incomes, etc., are </p><p>　　2.2. Second step: Attribute selection</p><p>　　Naturally, within the same cluster (the same group of customers),

44、not all original attributes are “informative” and some of them should be discarded. As a measure for the “information” conveyed by an attribute, we will use the entropy based on the frequency of the values taken by that

45、attribute within a given cluster. In order to compute this frequency, we will use the same clustering approach in Fig. 2 but applied to a data set composed of the values taken by that attribute. This is motivated b</p

46、><p>　　3 外文文獻(xiàn)譯文（2）</p><p>　　3.1客戶的知識(shí)關(guān)系管理：整合知識(shí)管理和客戶關(guān)系管理過程</p><p><b>　　3.2摘要</b></p><p>　　市場(chǎng)的激烈的競(jìng)爭(zhēng)和業(yè)務(wù)環(huán)境的快速變化，信息利用已經(jīng)成為企業(yè)增強(qiáng)競(jìng)爭(zhēng)優(yōu)勢(shì)的關(guān)鍵，知識(shí)管理(KM)和客戶關(guān)系管理(CRM)過程是一個(gè)嶄新的研究領(lǐng)域

47、，但是，圍繞它科學(xué)研究和相關(guān)文獻(xiàn)仍然有限，另外，客戶獲取、保持、擴(kuò)張過程中知識(shí)管理的作用提高客戶滿意仍然處于研究和報(bào)告水平。本論文的目的根據(jù)知識(shí)管理和客戶關(guān)系管理不同的模型，結(jié)合知識(shí)管理和客戶關(guān)系管理提供一個(gè)概念性的框架，這個(gè)框架稱為客戶知識(shí)關(guān)系管理。主要強(qiáng)調(diào)了概念為基礎(chǔ)的客戶知識(shí)的概念(了解客戶，客戶信息，客戶知識(shí))。因此，本文研究知識(shí)管理過程的發(fā)展（客戶知識(shí)發(fā)現(xiàn)、處理、運(yùn)用）。本文分析研究喬丹公司如何利用知識(shí)管理過程提高客戶關(guān)系管理

48、過程。根據(jù)該公司的數(shù)據(jù)采集，結(jié)果分析表明知識(shí)管理過程對(duì)客戶關(guān)系管理過程產(chǎn)生了積極的影響。</p><p>　　關(guān)鍵詞：知識(shí)獲取、知識(shí)創(chuàng)造、知識(shí)應(yīng)用；客戶的獲取、客戶保留、客戶挖掘。</p><p><b>　　3.3介紹</b></p><p>　　由于客戶知識(shí)革命的快速發(fā)展，建立同客戶高效且有效的的關(guān)系非常需要知識(shí)管理過程。另外，客戶關(guān)系管理

49、的本質(zhì)是知識(shí)管理，因?yàn)榭蛻絷P(guān)系管理幫助企業(yè)加強(qiáng)服務(wù)，快速響應(yīng)客戶需求。企業(yè)需要加強(qiáng)與客戶的互動(dòng)，確定知識(shí)管理的相關(guān)活動(dòng)領(lǐng)域，改善流程。</p><p>　　此外，知識(shí)管理是一個(gè)捕獲、創(chuàng)建和應(yīng)用知識(shí)使客戶關(guān)系管理過程成功的方法。更進(jìn)一步，Gebert提出客戶關(guān)系管理和知識(shí)管理已經(jīng)在商業(yè)市場(chǎng)中引起廣泛的興趣。這兩種方法關(guān)注分配資源，支持業(yè)務(wù)活動(dòng)，以獲得競(jìng)爭(zhēng)優(yōu)勢(shì)，盡管兩個(gè)概念實(shí)際上作為分開的研究的領(lǐng)域。Lin認(rèn)為客戶關(guān)

50、系管理和知識(shí)管理對(duì)每個(gè)市場(chǎng)決策者和信息技術(shù)專業(yè)人員有重要意義。</p><p>　　客戶關(guān)系管理的知識(shí)管理是獲得客戶滿意重要方法。知識(shí)管理過程和客戶關(guān)系管理過程的結(jié)合一個(gè)新的研究領(lǐng)域，因此圍繞它科學(xué)研究和相關(guān)文獻(xiàn)仍然有限，知識(shí)管理過程對(duì)客戶關(guān)系管理過程中的影響利用仍然處于研究和報(bào)告水平。</p><p>　　本論文的目的在于提出客戶知識(shí)關(guān)系管理概念模型，整合知識(shí)管理過程和客戶關(guān)系管理過程提

51、提提高客戶滿意度。將會(huì)實(shí)現(xiàn)下面的目的：</p><p>　　確定目前企業(yè)如何處理客戶，通過分析企業(yè)的使命完成。</p><p>　　提高對(duì)客戶知識(shí)的獲取以獲得新客戶，通過利用客戶知識(shí)保持現(xiàn)有客戶，拓展客戶知識(shí)擴(kuò)大與客戶的關(guān)系?！?lt;/p><p>　　描述未來企業(yè)如何處理客戶關(guān)系，識(shí)別企業(yè)的愿景實(shí)現(xiàn)目標(biāo)。</p><p>　　接下來部分，查閱相

52、關(guān)文獻(xiàn)，第三章提出客戶知識(shí)關(guān)系管理過程模型，第四章提出研究方法。</p><p><b>　　3.4文獻(xiàn)綜述</b></p><p>　　本節(jié)概述不同文獻(xiàn)關(guān)于知識(shí)管理過程，提供了CRM過程的描述。最后，描述相關(guān)文獻(xiàn)對(duì)知識(shí)管理過程和客戶關(guān)系管理過程的關(guān)系。</p><p>　　2.1 知識(shí)管理過程</p><p>　　知識(shí)

53、管理的目的不是處理所有知識(shí)，對(duì)企業(yè)來說管理知識(shí)是最重要的。它涉及應(yīng) 用收集的知識(shí)和全部人力物力實(shí)現(xiàn)企業(yè)特定的目標(biāo)，合適的人利用合適的信息在合適的時(shí)間幫助人們收集和分析知識(shí)提高企業(yè)效益。</p><p>　　作者提出并發(fā)展了一個(gè)概念和知識(shí)管理清晰模型?；谥R(shí)管理文獻(xiàn)中多種模型的調(diào)查，從知識(shí)的過程中捕獲知識(shí)，知識(shí)處理中產(chǎn)生所需的知識(shí)，知識(shí)提取中應(yīng)用知識(shí)。根據(jù)下面表1中知識(shí)管理過程的分類法。</p>&

54、lt;p>　　2.2客戶關(guān)系管理過程</p><p>　　客戶關(guān)系管理是近年來相關(guān)領(lǐng)域中出現(xiàn)的最熱門的課題，因?yàn)槠髽I(yè)客戶關(guān)系管理的價(jià)值。更進(jìn)一步，客戶關(guān)系管理成為所有企業(yè)的重要的業(yè)務(wù)流程和任務(wù)。</p><p>　　作者提出對(duì)客戶關(guān)系管理的清晰模型概念化，如下圖表2依靠CRM流程的分類法，客戶流程獲取新客戶，客戶流程保持客戶，流程擴(kuò)大與客戶的關(guān)系。</p><p&

55、gt;　　2.3知識(shí)管理和客戶關(guān)系管理流程之間的關(guān)系</p><p>　　客戶關(guān)系管理中的知識(shí)管理很重要，因?yàn)檫@幫助企業(yè)做更好的服務(wù)，加強(qiáng)產(chǎn)品的質(zhì)量，減少費(fèi)用和迅速響應(yīng)客戶。然而，企業(yè)管理知識(shí)最突出的挑戰(zhàn)是在所有不同部門成員之間計(jì)算和整合知識(shí)。因此，知識(shí)管理是成功的客戶關(guān)系管理戰(zhàn)略關(guān)鍵因素之一，提高服務(wù)質(zhì)量，降低服務(wù)成本，發(fā)展新產(chǎn)品和服務(wù)客戶。只有少數(shù)的企業(yè)實(shí)現(xiàn)把信息轉(zhuǎn)成客戶知識(shí)。</p><

56、p>　　另外，Salomann區(qū)分三種知識(shí)流動(dòng)在企業(yè)和客戶交互中的起到至關(guān)重要的作用：客戶知識(shí)在他們的購(gòu)買周期間支持客戶；一個(gè)連續(xù)的知識(shí)流直接從公司向它的客戶。來自客戶的知識(shí)的必須合并公司產(chǎn)品和服務(wù)的創(chuàng)新和發(fā)展。關(guān)于客戶知識(shí)的收集通過客戶關(guān)系管理服務(wù)，支持流程和客戶關(guān)系管理分析過程。</p><p>　　Ocker 和 Mudambi指出這些企業(yè)需要探索和改進(jìn)客戶關(guān)系管理和知識(shí)管理的方法為企業(yè)和客戶增加額外

57、的知識(shí)價(jià)值。意識(shí)到以客戶為中心的知識(shí)是公司客戶數(shù)據(jù)和知識(shí)的整合。另外，Geib對(duì)客戶關(guān)系管理的定義客戶滿意管理通過向客戶提供高質(zhì)量的服務(wù)和互動(dòng)提供高價(jià)值客戶的滿意度。為了得到更好的服務(wù)質(zhì)量和提供流程和問題的解決方案，這些目標(biāo)往往由知識(shí)管理系統(tǒng)實(shí)現(xiàn)。</p><p>　　根據(jù)以上研究，構(gòu)建一個(gè)高效的和有效的客戶關(guān)系，知識(shí)管理過程是關(guān)鍵?？蛻絷P(guān)系管理中的知識(shí)管理應(yīng)用對(duì)提高客戶滿意度起重要的作用。深入探索所有可用資源中

58、所有可用的研究，建議，客戶關(guān)系管理過程整合知識(shí)管理過程仍需要更進(jìn)一步的研究。因此，企業(yè)意識(shí)到客戶關(guān)系管理的成功知識(shí)管理起重要的作用，企業(yè)需要整合他們的知識(shí)管理流程和客戶關(guān)系管理流程。</p><p>　　3.5提出客戶知識(shí)關(guān)系管理過程的概念模型</p><p>　　本節(jié)對(duì)知識(shí)管理和客戶關(guān)系管理流程整合的客戶知識(shí)關(guān)系管理提出一個(gè)概念模型，目的是提高客戶滿意度。根據(jù)Alryalat提出的知識(shí)管

59、理流程和客戶關(guān)系管理流程，模型1解釋了各種形式的客戶知識(shí)的流向（關(guān)于客戶的知識(shí)，獲取客戶知識(shí)，挖掘客戶知識(shí)）和知識(shí)管理過程（ 客戶知識(shí)管理過程的說明，客戶知識(shí)管理過程的目的，客戶知識(shí)管理過程的利用）。</p><p>　　客戶知識(shí)關(guān)系管理模型中有12階段。第一個(gè)階段確定企業(yè)的目標(biāo)，明確目前企業(yè)和客戶的狀態(tài)。這個(gè)階段的目的是闡明發(fā)展客戶關(guān)系的優(yōu)缺點(diǎn)，分析企業(yè)與客戶目前的關(guān)聯(lián)，分析企業(yè)為客戶提供的服務(wù)。這階段的目的是

60、觀察目前的客戶關(guān)系是否符合企業(yè)的目標(biāo)。</p><p>　　第二階段是關(guān)于客戶知識(shí)的流程，這個(gè)階段對(duì)獲取客戶非常重要、必不可少。這個(gè)階段的主要目標(biāo)理解如何捕獲所需知識(shí)。關(guān)于客戶的知識(shí)流程需要每個(gè)階段的序列。</p><p>　　第一個(gè)階段是需要客戶知識(shí)。需要許多人力物力在任何地方和任何時(shí)間去收集信息，知識(shí)的利用對(duì)提高工作效率節(jié)約時(shí)間和成本顯示巨大優(yōu)勢(shì)。另外，Sunassee 和 Sewry

61、認(rèn)為創(chuàng)建企業(yè)需要的知識(shí)要選擇企業(yè)內(nèi)部和外部的知識(shí)。同樣，區(qū)別知識(shí)的要充分了解需要知識(shí)的特點(diǎn)，挑選目前相關(guān)的知識(shí)和分配要獲取和創(chuàng)建的知識(shí)資源。此外，通過理解和選擇現(xiàn)有的存儲(chǔ)庫(kù)中有用的知識(shí)，知識(shí)選擇有助于識(shí)別知識(shí)的需求。這一過程能更容易搜索和發(fā)現(xiàn)知識(shí)。知識(shí)的識(shí)別包括識(shí)別和確定需要知識(shí)，在知識(shí)可以創(chuàng)建或共享前，對(duì)知識(shí)的需求已經(jīng)確認(rèn)。</p><p>　　4 外文文獻(xiàn)原文（2）</p><p>&

62、lt;b>　　4.1 title</b></p><p>　　Towards Customer Knowledge Relationship Management:Integrating Knowledge Management and Customer Relationship Management Process</p><p>　　4.2 Abstract</

63、p><p>　　Due to the strong competition that exists among organisations and the rapid change in the business environment, knowledge has turned out to become a key source for organisations to enhance the competiti

64、ve advantage. Integrating Knowledge Management (KM) and Customer Relationship Management (CRM) process is a new research area, therefore, scienti?c research and literature around it remain limited. In addition, the impac

65、t of KM process on customer acquisition, retention, and expansion to improve c</p><p>　　Keywords: Knowledge capture; knowledge creation; knowledge application; customer acquisitions; customer retention; cust

66、omer expansion.</p><p>　　4.3 Introduction</p><p>　　Due to the rapid growth of the customer knowledge revolution, the KM process has become very necessary for building an e?cient and e?ective rel

67、ationship with customers. In addition, KM is essential for CRM because it can help the organisations enhance their services, and respond rapidly to their customers’ need (Alryalat et al.2007). Organisations need to enhan

68、ce the processes with customers to identify relevant activity ?elds for KM to improve these processes.</p><p>　　Additionally, KM is an approach that is used to capture, create, and apply knowledge to make th

69、e CRM process successful (Alryalat et al., 2007). Furthermore, Gebert et al. (2002) maintains that CRM and KM have been recently gaining wide interest in business environment. Both approaches focus on allocating resource

70、s to support business activities in order to gain competitive advantages despite the fact that both concepts are currently treated mostly as separate research areas. Moreover, Lin et al</p><p>　　The role of

71、KM in CRM is important for achieving customer satisfaction. The act of integration of KM process and CRM process is a new research area and, therefore; scienti?c research and literature around it remain limited. Yet the

72、impact of KM process on CRM process remains under exploration and report.</p><p>　　The aim of this paper is to propose a conceptual model of CKRM that describes the integration of KM and CRM process to impro

73、ve customer satisfaction. The following objectives will be gained fromthis aim:</p><p>　　1. To identify how the organisation deals with customers currently. This will be accomplished by identifying the missi

74、on of the organisation.</p><p>　　2. To acquire new customers by enhancing customer knowledge acquisition, retain existing customers through improved customer knowledge retention, andexpand the relationship w

75、ith customers by growth customer knowledge expansion.</p><p>　　3. To describe how the organisation deals with the customers in the future. This will be carried out by identifying the vision of the organisati

76、on.</p><p>　　In the next section, we review relevant literature. Section 3 proposes the CKRM process model and Sec. 4 presents a research methodology.</p><p>　　4.4 Literature Review</p>&

77、lt;p>　　This section gives the reader an overview of di?erent contributions in literature associated with the KM process. It also presents description of CRM process. Finally, it describes the relationship between KM a

78、nd CRM processes related in the literature.</p><p>　　2.1. Knowledge management process</p><p>　　The purpose of KM is not to manage all knowledge, but to manage the knowledge that is most signi?c

79、ant to the organisations. It involves applying the collective knowledge and ability of the entire workforce to achieve speci?c organisation objectives which, in return, can lead to getting the right information to right

80、people at right time and help people generate and share knowledge to enhance organisational performance. </p><p>　　The authors have proposed and developed a conceptual and coherent Model of KM process. Start

81、ing with the Process about Knowledge to capture knowledge, Process for Knowledge to create Knowledge need, and Process from Knowledge to apply knowledge based on a thorough investigation of various models presented in KM

82、 literature (Alryalat et al., 2008a) depending on the taxonomy of the KM process as shown in Table 1.</p><p>　　2.2. Customer relationship management process</p><p>　　CRM has emerged as one of th

83、e most demanding issues in business because of the value expected from carrying out the CRM in organisations. Moreover, CRM is becoming an important business process and is turning out to be animportant assessment tool f

84、or all organisations.</p><p>　　The authors have proposed and developed a conceptual and coherent Model of CRM. Starting with the Process about Customer to acquire new customers, Process for Customer to retai

85、n existing customers, and Process from Customer to expand the relationship with customers (Alryalat et al., 2008b) relying on taxonomy of CRM process maintained in Table 2.</p><p>　　2.3. Relationship between

86、 knowledge management and customer relationship management processes</p><p>　　KM is important for CRM because it can help the organisations make better services, enhance quality of product; reduce cost and r

87、espond to their customers more promptly. However, the most prominent challenge of managing knowledge in the organisations is capturing and integrating knowledge to share among all organizational members. Therefore, KM is

88、 one of the critical factors for the success of CRM strategy with the aim of increasing service quality, decreasing service costs, and o?ering new prod</p><p>　　Additionally, Salomann et al. (2005) distingui

89、sh between three kinds of knowledge ?ows that play a vital role in the interaction between an organisation and its customers: knowledge for, from and about customers. Knowledge for customers to support customers in their

90、 buying cycle; a continuous knowledge ?ow directed from the company to its customers. Knowledge from customers has to be incorporated by the company for products and services innovation and development. Knowledge about c

91、ustomers is co</p><p>　　As will, Ocker and Mudambi (2002) point out that the Organisations require exploring and improving CRM and KM methods to get added knowledge value for themselves and their customers.

92、Realising this value in a customer centric context needs the integration of customer data and knowledge during an organisation. Additionally, Geib et al. (2005) give a description of CRM as Customer Satisfaction manageme

93、nt aims at high customer satisfaction by offering customers a high quality of service and proximi</p><p>　　Based on the above discussion, the KM process has become pivotal for building an excient and effecti

94、ve relationship with customers. The role of KM in CRM is important for achieving customer satisfaction. After conducting a careful examination of all available studies from all available sources, it is recommended that t

95、he CRM process, together with the KM process, still deserves further study. As a result, organisations need to integrate their KM and CRM processes because they realise that KM pla</p><p>　　4.5 Proposed Cust

96、omer Knowledge Relationship Management Process: A Conceptual Model</p><p>　　This section proposes a conceptual model of CKRM that describes the integration of KM and CRM process to improve customer satisfact

97、ion (Alryalat et al., 2007). Model 1 explains the links of the various forms of customer knowledge (knowledge about customer, knowledge for customer, and knowledge from customer) and KM process (Knowledge Process about C

98、ustomer, Knowledge Process for Customer, and Knowledge Process from Customer), based on the KM process proposed by Alryalat et al. (2008a) and the CR</p><p>　　There are 12 phases in a CKRM model. The ?rst ph

99、ase determines organisation mission to determine how an organisation deals with customers at the present time. The objective of this phase is to shed light on the strengths and weaknesses in dealing with customers. The m

100、ission delineates the current association between the company and its customers. It portrays the existing services offered by the company to their customers. The goal is to scrutinise the relationship to determine whethe

101、r it is up t</p><p>　　The second phase is Knowledge Process about customers. This phase is important to the efforts of getting customer acquisition and it plays a signi?cant role in acquiring customers. The