本文選題：客戶(hù)關(guān)系管理 + 數(shù)據(jù)挖掘�。� 參考：《浙江理工大學(xué)》2015年碩士論文

【摘要】：伴隨著互聯(lián)網(wǎng)技術(shù)的飛速發(fā)展，企業(yè)之間的競(jìng)爭(zhēng)日益激烈，傳統(tǒng)的營(yíng)銷(xiāo)模式受到嚴(yán)重的挑戰(zhàn)，企業(yè)需要并得以借助先進(jìn)的管理思想以及先進(jìn)的技術(shù)手段，去充分了解和掌握客戶(hù)信息，，發(fā)現(xiàn)與挖掘潛在市場(chǎng)機(jī)會(huì)，規(guī)避風(fēng)險(xiǎn)，提高客戶(hù)忠誠(chéng)度與滿(mǎn)意度。企業(yè)對(duì)于客戶(hù)信息的獲取以及如何使這些信息發(fā)揮最大的效用，是企業(yè)在自身發(fā)展中的一個(gè)重要工作，這就必須有一個(gè)專(zhuān)門(mén)的系統(tǒng)來(lái)收集客戶(hù)信息，并加以分析和利用。客戶(hù)關(guān)系管理(CustomerRelationship Management,CRM)系統(tǒng)正是在此環(huán)境下應(yīng)運(yùn)而生，它將利用企業(yè)各種資源，通過(guò)客戶(hù)的管理過(guò)程，分析出企業(yè)優(yōu)劣，提高企業(yè)的競(jìng)爭(zhēng)力，使企業(yè)在現(xiàn)如今激烈的競(jìng)爭(zhēng)環(huán)境中處于不敗之地。同時(shí)，隨著企業(yè)業(yè)務(wù)的不斷擴(kuò)大，企業(yè)里的數(shù)據(jù)信息正以指數(shù)級(jí)的速度在迅速增長(zhǎng)，特別是客戶(hù)信息中不僅包含著有價(jià)值的部分，同時(shí)也包含很多冗余的信息，要想在這海量的信息中準(zhǔn)確的找到企業(yè)需要的資源，從中提取出客戶(hù)關(guān)系圖，也是一個(gè)難點(diǎn)。因此，需要在構(gòu)建CRM系統(tǒng)的同時(shí)應(yīng)用現(xiàn)如今正飛速發(fā)展的數(shù)據(jù)挖掘技術(shù)，將兩者結(jié)合起來(lái)，才能搭建出一個(gè)完整的CRM系統(tǒng)。 本文結(jié)合浙江中煙工業(yè)公司（以下簡(jiǎn)稱(chēng)浙江中煙）CRM系統(tǒng)，分析了企業(yè)中海量客戶(hù)數(shù)據(jù)處理的方法，并對(duì)客戶(hù)進(jìn)行劃分，提取客戶(hù)關(guān)系圖以及檢測(cè)客戶(hù)忠誠(chéng)度等。浙江中煙CRM系統(tǒng)對(duì)企業(yè)的發(fā)展起到了至關(guān)重要的作用。主要研究有以下幾個(gè)方面： 1)以浙江中煙為例，結(jié)合大型集團(tuán)企業(yè)CRM系統(tǒng)的需求分析，了解CRM系統(tǒng)的特性、架構(gòu)和運(yùn)行機(jī)制，分析企業(yè)CRM系統(tǒng)的可行性以及數(shù)據(jù)挖掘技術(shù)如何的應(yīng)用于系統(tǒng)當(dāng)中。 2)構(gòu)建客戶(hù)分類(lèi)決策樹(shù)，提取客戶(hù)的行為記錄，如反饋、來(lái)訪、活動(dòng)交流等，分析客戶(hù)的忠誠(chéng)度狀態(tài)；建立客戶(hù)忠誠(chéng)度預(yù)警模型，根據(jù)CLV曲線分析，得出客戶(hù)在某個(gè)時(shí)間點(diǎn)或時(shí)間范圍內(nèi)的忠誠(chéng)度，并據(jù)此提出提升客戶(hù)忠誠(chéng)度的方案。 3)提出了一種基于改進(jìn)的FP-Growth算法的客戶(hù)關(guān)系圖提取方法，使得企業(yè)能夠很清晰的看出與客戶(hù)之間的關(guān)系，找出存在的不足加以改進(jìn)，使企業(yè)決策者和服務(wù)人員做出相應(yīng)的決策行為，改善客戶(hù)關(guān)系，提高客戶(hù)的忠誠(chéng)度。 4)提出了一種基于TFIDF算法同義替換和相鄰合并的文本挖掘技術(shù)，可以降低服務(wù)器壓力，使服務(wù)人員可以更快更準(zhǔn)確的從知識(shí)庫(kù)中尋找出相關(guān)信息，滿(mǎn)足客戶(hù)的需求。
[Abstract]:With the rapid development of Internet technology, the competition between enterprises is increasingly fierce, the traditional marketing model is seriously challenged, enterprises need and can use advanced management ideas and advanced technical means. To fully understand and master customer information, identify and tap potential market opportunities, avoid risks, and improve customer loyalty and satisfaction. It is an important work for enterprises to obtain customer information and how to make it play a most important role in their own development. So it is necessary to have a special system to collect customer information and analyze and utilize it. The customer relationship Management (CRM) system emerges as the times require in this environment, it will use all kinds of resources of the enterprise, through the customer management process, analyze the advantages and disadvantages of the enterprise, improve the competitiveness of the enterprise. So that enterprises in today's fierce competition environment in an invincible position. At the same time, with the continuous expansion of enterprise business, the data information in the enterprise is growing exponentially, especially the customer information not only contains valuable parts, but also contains a lot of redundant information. It is also a difficult point to find the resources needed by the enterprise and extract the customer relationship diagram from the massive information. Therefore, it is necessary to build a complete CRM system by applying the data mining technology which is developing at full speed in order to build a complete CRM system. Based on the CRM system of Zhejiang Zhongyan Industrial Corporation (hereinafter referred to as "Zhejiang Zhongyan"), this paper analyzes the methods of processing massive customer data in enterprises, and divides customers, extracts customer relationship diagrams and detects customer loyalty. Zhejiang Zhongyan CRM system plays a vital role in the development of enterprises. The main research has the following several aspects: 1) take Zhejiang Zhongyan as an example, combined with the large-scale group enterprise CRM system demand analysis, understands the CRM system characteristic, the structure and the movement mechanism, Analyze the feasibility of CRM system and how to apply data mining technology in the system. 2) construct customer classification decision tree, extract customer behavior record, such as feedback, visit, activity exchange, etc. Based on the analysis of CLV curve, the customer loyalty in a certain time or time range can be obtained. Based on the improved FP-Growth algorithm, a customer relationship graph extraction method is proposed, which enables enterprises to clearly see the relationship with customers. To find out the shortcomings to improve, so that the enterprise decision makers and service personnel to make the corresponding decision-making behavior, improve customer relations, (4) A text mining technology based on synonymous substitution and adjacent merging of TFIDF algorithm is proposed, which can reduce the server pressure and make the service staff find the relevant information from the knowledge base more quickly and accurately. Meet the needs of customers.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張蓉;數(shù)據(jù)挖掘技術(shù)在客戶(hù)關(guān)系管理系統(tǒng)中的應(yīng)用[J];重慶廣播電視大學(xué)學(xué)報(bào);2003年02期

2 劉淳猛;何利力;李鋮瀚;;數(shù)據(jù)挖掘技術(shù)在CRM系統(tǒng)中的應(yīng)用[J];工業(yè)控制計(jì)算機(jī);2014年10期

3 何葉榮;;客戶(hù)關(guān)系管理與企業(yè)核心競(jìng)爭(zhēng)力[J];淮南師范學(xué)院學(xué)報(bào);2006年04期

4 張偉;劉勇國(guó);彭軍;廖曉峰;吳中福;;數(shù)據(jù)挖掘發(fā)展研究[J];計(jì)算機(jī)科學(xué);2001年07期

5 韓客松,王永成,陳桂林;漢語(yǔ)語(yǔ)言的無(wú)詞典分詞模型系統(tǒng)[J];計(jì)算機(jī)應(yīng)用研究;1999年10期

6 徐莉,徐楠;以構(gòu)建CRM為契機(jī)提升企業(yè)的核心競(jìng)爭(zhēng)力[J];技術(shù)經(jīng)濟(jì)與管理研究;2004年05期

7 柴巧葉;;企業(yè)管理決策中數(shù)據(jù)倉(cāng)庫(kù)的設(shè)計(jì)與實(shí)現(xiàn)[J];計(jì)算機(jī)教育;2007年24期

8 徐樂(lè)群,蔣明青;客戶(hù)關(guān)系管理在煙草行業(yè)中的應(yīng)用[J];科技創(chuàng)業(yè)月刊;2004年08期

9 蔣晨琛;常國(guó)鋒;;數(shù)據(jù)挖掘技術(shù)在CRM中的應(yīng)用研究[J];科技信息;2010年21期

10 李偉萁;李光明;;庫(kù)馬爾客戶(hù)終身價(jià)值模型及其參數(shù)的簡(jiǎn)化[J];科技視界;2012年28期

本文編號(hào)：2052884