數(shù)據(jù)庫外文文獻翻譯2篇

1、<p><b>　　目 錄</b></p><p>　　1 外文文獻譯文（1）1</p><p>　　1.1建立客戶模型與業(yè)務數(shù)據(jù):一個自動的方法基于模糊聚類和機器學習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 外文文獻譯文（2）6</p><p>　　3.1客戶的知識關系管理：整合知識管理和客戶關系管理過程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文獻綜述7</b></p><p>　　3.5提出客戶知識關系管理過程的概念模型8</p><p>　　4 外文文獻原文（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 外文文獻譯文（1）</p>&l

6、t;p>　　1.1建立客戶模型與業(yè)務數(shù)據(jù):一個自動的方法基于模糊聚類和機器學習</p><p><b>　　1.2摘要</b></p><p>　　數(shù)據(jù)挖掘(DM)是一門新興學科,旨在從數(shù)據(jù)中提取知識使用幾種技術。DM證明是有用的業(yè)務數(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(也說在文獻資料)與業(yè)務數(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。一個關鍵特性是,很多團體(或集群)自動計算從數(shù)據(jù)使用劃分熵作為真實性的標準。在第二步中,我們進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屬性。因此,作為結果,第二步,我們獲得的消費者群每個描述由一種獨特的屬性集。在第三個和最后一步,我們使用摘要神經(jīng)網(wǎng)絡中獲取有用的知識從這些組織。真實世界的數(shù)據(jù)集上的實驗結果揭示了我們的方法的良好性能,應該模擬未

8、來的研究。</p><p>　　關鍵字：數(shù)據(jù)挖掘、客戶利潤、客戶關系、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>　　如果可以探測和預測客戶的行為改變，營銷管理能夠和客戶發(fā)展長期和愉快的關系。在過去，研究人員通常使用應用統(tǒng)計調查研究客戶的行為。最近，數(shù)據(jù)挖掘技術已經(jīng)采用，這些技術的

9、目標通過搜索數(shù)據(jù)庫以獲取隱式的，未知，和潛在有用信息，包括知識規(guī)則、約束和規(guī)律。數(shù)據(jù)挖掘，是數(shù)據(jù)庫的中知識發(fā)現(xiàn)關鍵的一步，包括特定算法的模式提取的應用，像網(wǎng)絡、市場、金融和銀行業(yè)務這些領域已經(jīng)有多方面成功的應用。目前，企業(yè)面臨的挑戰(zhàn)是一個不斷發(fā)展的市場，客戶的需求在不斷地變化。因此，代替對待所有客戶一樣，企業(yè)可以只選擇那些符合特定的盈利條件的客戶，標準基于他們個人的需求或消費行為。這樣，所發(fā)現(xiàn)的信息可以為市場做更精準的決策。因此，可以定

10、義對客戶概括的數(shù)據(jù)挖掘，是簡單地作為的允許建立描述一組特定的習慣、態(tài)度和行為客戶檔案的技術。數(shù)據(jù)挖掘技術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，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)挖掘和機器學習技術能夠處理大量的數(shù)據(jù)和不確定性。這些特征使這些技術實現(xiàn)客戶模型的自動生成，提高決策效率。一些文獻提出人工智能技術可以解決這個問題。事實上，許多工業(yè)應用程序為</p><p>　

11、　第十七文獻中提出一個集成的數(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)分基于還款行為時效性、頻率、貨幣行為和得分的預測因子的客戶群，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，使用先驗的關聯(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得到應用，因為在動態(tài)的零售市場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。第五文獻中的模型，合并了客戶行為變量，人口

12、統(tǒng)計學變量和事務數(shù)據(jù)庫統(tǒng)計客戶的行為變化。為了挖掘變化模式，相似性和不可預測性兩種擴展特征用來分析不同時期模式的相似度。關聯(lián)規(guī)則挖掘首次發(fā)現(xiàn)客戶行為模式。自從發(fā)現(xiàn)了關聯(lián)規(guī)則，通過比較兩組不同時期的數(shù)據(jù)的關聯(lián)規(guī)則確定客戶行為的變化?；谙惹暗膶W習，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>　　第四十文獻中另一個值得注意的案例，現(xiàn)在這個模式使用到其他的服務，目前提供給移動電信用戶。使

13、用要素分析，聚類和定量關聯(lián)規(guī)則這些方法發(fā)現(xiàn)細分客戶群采用的服務模式，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收費的用戶組成，組要為了休閑和娛樂。年輕的一代比年長的一代更頻繁地使用手機，他們趨向展示更高的各種不同的附加服務使用模式。第二類用戶使用實際的附加服務，低價或免費的如“數(shù)據(jù)服務”和“通話服務”通過“來電顯示請求服務”。最后一類用戶沒有明顯的特征。這份研究使用關聯(lián)規(guī)則發(fā)現(xiàn)在每個用戶群，為不同用戶組的移動服務

14、市場提供戰(zhàn)略指導。</p><p>　　第29文獻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活動和其他相關決策。關聯(lián)規(guī)則的關系數(shù)據(jù)庫設計實現(xiàn)了挖掘系統(tǒng)幫助電子目錄設計和促銷策略設計。關聯(lián)規(guī)則在相關的數(shù)據(jù)庫的應用挖掘消費行為，以便生成零售業(yè)購物中心的交叉銷售的電子目錄設計和營銷方案。</p><p>　　在本文中，我們提出一種方法從業(yè)務數(shù)據(jù)中開發(fā)自動客戶分析(模型)。它涉及到三個步驟

15、，在第一步中，用模糊聚類方法分類的客戶。模糊聚類算法關鍵的一步是決定劃分聚類的組數(shù)，自動從數(shù)據(jù)使用分區(qū)熵作為一個有效措施。在第二步中，維(或屬性數(shù)量)對于每個集群(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；量計算運用信息熵的屬性。因此，第二步的結果，我們獲取幾組的消費者群，他們每個人都被一個不同的組，每組的屬性被認定為信息最豐富的。在第三步，最后一個步驟中，每個聚類減少訓練，從反饋網(wǎng)絡中提取有用的知識。因此，連

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

17、自動計算聚類的數(shù)量。通過這種方式，最優(yōu)的聚類數(shù)量是能夠產生最低劃分熵。第一步的結果，從消費習慣，,收入等相似性方面細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屬性。事實上，聚類中的屬性不是所有都是相關的，有一些屬性應該刪除。重復步驟，利用決策樹方法來決定一個屬性是否重要，關于給定一組客戶中使用定義信息丟失的閾值。第三，每個聚類被導入倒傳遞網(wǎng)絡提取有用的信息。整個算法的結果，獲取客戶細分結果。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。接下來的部分，將詳細描述方法中每一步。</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，其中有些應該被丟棄。衡量一個屬性傳達的信息，在一個給定的聚類的值，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算這個頻率，但是只適用于一個數(shù)據(jù)集組成的屬性值。在給定一組客戶中一個屬性可

19、能值的數(shù)量無法準確計算，特別是可能會非常接近的數(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；因此相應的集群多次相似的值被視為一個單一值。在進一步深入學習之前，現(xiàn)在我們需要以下的注釋。</p><p>　　2 外文文獻原文（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 外文文獻譯文（2）</p><p>　　3.1客戶的知識關系管理：整合知識管理和客戶關系管理過程</p><p><b>　　3.2摘要</b></p><p>　　市場的激烈的競爭和業(yè)務環(huán)境的快速變化，信息利用已經(jīng)成為企業(yè)增強競爭優(yōu)勢的關鍵，知識管理(KM)和客戶關系管理(CRM)過程是一個嶄新的研究領域

47、，但是，圍繞它科學研究和相關文獻仍然有限，另外，客戶獲取、保持、擴張過程中知識管理的作用提高客戶滿意仍然處于研究和報告水平。本論文的目的根據(jù)知識管理和客戶關系管理不同的模型，結合知識管理和客戶關系管理提供一個概念性的框架，這個框架稱為客戶知識關系管理。主要強調了概念為基礎的客戶知識的概念(了解客戶，客戶信息，客戶知識)。因此，本文研究知識管理過程的發(fā)展（客戶知識發(fā)現(xiàn)、處理、運用）。本文分析研究喬丹公司如何利用知識管理過程提高客戶關系管理

48、過程。根據(jù)該公司的數(shù)據(jù)采集，結果分析表明知識管理過程對客戶關系管理過程產生了積極的影響。</p><p>　　關鍵詞：知識獲取、知識創(chuàng)造、知識應用；客戶的獲取、客戶保留、客戶挖掘。</p><p><b>　　3.3介紹</b></p><p>　　由于客戶知識革命的快速發(fā)展，建立同客戶高效且有效的的關系非常需要知識管理過程。另外，客戶關系管理

49、的本質是知識管理，因為客戶關系管理幫助企業(yè)加強服務，快速響應客戶需求。企業(yè)需要加強與客戶的互動，確定知識管理的相關活動領域，改善流程。</p><p>　　此外，知識管理是一個捕獲、創(chuàng)建和應用知識使客戶關系管理過程成功的方法。更進一步，Gebert提出客戶關系管理和知識管理已經(jīng)在商業(yè)市場中引起廣泛的興趣。這兩種方法關注分配資源，支持業(yè)務活動，以獲得競爭優(yōu)勢，盡管兩個概念實際上作為分開的研究的領域。Lin認為客戶關

50、系管理和知識管理對每個市場決策者和信息技術專業(yè)人員有重要意義。</p><p>　　客戶關系管理的知識管理是獲得客戶滿意重要方法。知識管理過程和客戶關系管理過程的結合一個新的研究領域，因此圍繞它科學研究和相關文獻仍然有限，知識管理過程對客戶關系管理過程中的影響利用仍然處于研究和報告水平。</p><p>　　本論文的目的在于提出客戶知識關系管理概念模型，整合知識管理過程和客戶關系管理過程提

51、提提高客戶滿意度。將會實現(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>　　提高對客戶知識的獲取以獲得新客戶，通過利用客戶知識保持現(xiàn)有客戶，拓展客戶知識擴大與客戶的關系?！?lt;/p><p>　　描述未來企業(yè)如何處理客戶關系，識別企業(yè)的愿景實現(xiàn)目標。</p><p>　　接下來部分，查閱相

52、關文獻，第三章提出客戶知識關系管理過程模型，第四章提出研究方法。</p><p><b>　　3.4文獻綜述</b></p><p>　　本節(jié)概述不同文獻關于知識管理過程，提供了CRM過程的描述。最后，描述相關文獻對知識管理過程和客戶關系管理過程的關系。</p><p>　　2.1 知識管理過程</p><p>　　知識

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

54、lt;p>　　2.2客戶關系管理過程</p><p>　　客戶關系管理是近年來相關領域中出現(xiàn)的最熱門的課題，因為企業(yè)客戶關系管理的價值。更進一步，客戶關系管理成為所有企業(yè)的重要的業(yè)務流程和任務。</p><p>　　作者提出對客戶關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，流程擴大與客戶的關系。</p><p&

55、gt;　　2.3知識管理和客戶關系管理流程之間的關系</p><p>　　客戶關系管理中的知識管理很重要，因為這幫助企業(yè)做更好的服務，加強產品的質量，減少費用和迅速響應客戶。然而，企業(yè)管理知識最突出的挑戰(zhàn)是在所有不同部門成員之間計算和整合知識。因此，知識管理是成功的客戶關系管理戰(zhàn)略關鍵因素之一，提高服務質量，降低服務成本，發(fā)展新產品和服務客戶。只有少數(shù)的企業(yè)實現(xiàn)把信息轉成客戶知識。</p><

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

57、的知識價值。意識到以客戶為中心的知識是公司客戶數(shù)據(jù)和知識的整合。另外，Geib對客戶關系管理的定義客戶滿意管理通過向客戶提供高質量的服務和互動提供高價值客戶的滿意度。為了得到更好的服務質量和提供流程和問題的解決方案，這些目標往往由知識管理系統(tǒng)實現(xiàn)。</p><p>　　根據(jù)以上研究，構建一個高效的和有效的客戶關系，知識管理過程是關鍵?？蛻絷P系管理中的知識管理應用對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，客戶關系管理過程整合知識管理過程仍需要更進一步的研究。因此，企業(yè)意識到客戶關系管理的成功知識管理起重要的作用，企業(yè)需要整合他們的知識管理流程和客戶關系管理流程。</p><p>　　3.5提出客戶知識關系管理過程的概念模型</p><p>　　本節(jié)對知識管理和客戶關系管理流程整合的客戶知識關系管理提出一個概念模型，目的是提高客戶滿意度。根據(jù)Alryalat提出的知識管

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

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

61、認為創(chuàng)建企業(yè)需要的知識要選擇企業(yè)內部和外部的知識。同樣，區(qū)別知識的要充分了解需要知識的特點，挑選目前相關的知識和分配要獲取和創(chuàng)建的知識資源。此外，通過理解和選擇現(xiàn)有的存儲庫中有用的知識，知識選擇有助于識別知識的需求。這一過程能更容易搜索和發(fā)現(xiàn)知識。知識的識別包括識別和確定需要知識，在知識可以創(chuàng)建或共享前，對知識的需求已經(jīng)確認。</p><p>　　4 外文文獻原文（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