本文關(guān)鍵詞： 精確營銷 聚類分析 交叉營銷分析 經(jīng)營周期營銷分析　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：從上世紀(jì)70年代開始,全球進(jìn)行信息化建設(shè)。各種的應(yīng)用信息系統(tǒng)遍布各個行業(yè)的角落。在長期的使用過程中,生產(chǎn)了大量的數(shù)據(jù),形成了數(shù)據(jù)的初始化積累。在本世紀(jì)初期,世界的IT建設(shè)發(fā)生了重大變化,從含有大量操作界面的人機(jī)交互的信息系統(tǒng),轉(zhuǎn)變?yōu)閹缀鯖]有界面的自動化數(shù)據(jù)挖掘和服務(wù)發(fā)布型系統(tǒng),典型模式為web2.0、云服務(wù)、數(shù)據(jù)挖掘等。本文論述了對銀行數(shù)據(jù)密集型行業(yè)采用了新興自動化數(shù)據(jù)挖掘技術(shù),對銀行歷史數(shù)據(jù)進(jìn)行分析,加工出精確營銷數(shù)據(jù)的系統(tǒng)實現(xiàn)方式。銀行從傳統(tǒng)的借貸業(yè)務(wù)產(chǎn)生到存款利率差的主要盈利模式進(jìn)行轉(zhuǎn)變,對中間業(yè)務(wù)的收入更加重視;客戶也將由大型公司客戶,逐步轉(zhuǎn)變?yōu)楦又匾闹行∑髽I(yè)客戶和零售客戶。在這個情況下,傳統(tǒng)的粗放式銷售方式,在面對龐大的客戶群體和復(fù)雜的銷售產(chǎn)品時就顯得有些力不從心了。本文核心工作包含設(shè)計和實現(xiàn)兩部分。設(shè)計部分,首先對國內(nèi)外的銀行業(yè)務(wù)發(fā)展進(jìn)行討論和分析,發(fā)現(xiàn)其業(yè)務(wù)模式已經(jīng)有了很大的轉(zhuǎn)變。通過對業(yè)務(wù)模式轉(zhuǎn)變的深入分析,發(fā)現(xiàn)在當(dāng)前的業(yè)務(wù)模式下,需要更加精確的營銷方式。再通過對銀行銷售業(yè)務(wù)的調(diào)研,確定精確營銷需求內(nèi)容,包括:數(shù)據(jù)清洗、客戶整合、客戶評級、客戶生命周期管理、客戶精確營銷數(shù)據(jù)語言等。同時對銀行信息建設(shè)的現(xiàn)狀進(jìn)行調(diào)研和分析,設(shè)計出以數(shù)據(jù)提取、轉(zhuǎn)換和加載(Extraction-Transformation-Loading,ETL)為主要技術(shù)架構(gòu),以數(shù)據(jù)挖掘為手段的技術(shù)體系。并根據(jù)業(yè)務(wù)需求,設(shè)計出聚類分析的方式,對數(shù)據(jù)進(jìn)行處理。在確定技術(shù)架構(gòu)后,進(jìn)入實現(xiàn)部分。首先具體設(shè)計出ETL處理的流程和數(shù)據(jù)模型;制定出ETL分層處理結(jié)構(gòu),到達(dá)系統(tǒng)的松耦合性。采用KETTEL工具實現(xiàn)了ETL處理過程,完成對銀行龐大的客戶群體的識別、定位、細(xì)分;并在此基礎(chǔ)上,使用交叉營銷分析、經(jīng)營周期營銷分析兩種分析方法實現(xiàn)對客戶精確營銷的預(yù)言。在分析出客戶精確營銷數(shù)據(jù)后,再次通過ETL方式將該數(shù)據(jù)推送到web頁面進(jìn)入商機(jī)池。在商機(jī)池中,客戶經(jīng)理進(jìn)行商機(jī)認(rèn)領(lǐng),實現(xiàn)精確營銷。完成整個精確營銷系統(tǒng)的實現(xiàn)。本文最后會對課題工作進(jìn)行總結(jié)。得出基于數(shù)據(jù)挖掘的精確營銷能夠大大的提高銀行的營銷效率結(jié)論。對于銀行這類數(shù)據(jù)富集行業(yè),有效數(shù)據(jù)挖掘不僅僅只是提高銀行營銷效率,還能進(jìn)行廣泛的應(yīng)用,所以應(yīng)該加大對數(shù)據(jù)富集行業(yè)的數(shù)據(jù)挖掘,通過數(shù)據(jù)分析創(chuàng)造價值。
[Abstract]:Since -30s, information construction has been carried out all over the world. A variety of application information systems have spread all over the corners of various industries. In the long-term use process, a large number of data have been produced. In the beginning of this century, the IT construction of the world has changed greatly, from the human-computer interactive information system with a large number of operating interfaces. An automated data mining and service publishing system with almost no interface, typical for web 2.0, cloud services. Data mining. This paper discusses the use of emerging automated data mining technology in data-intensive banking industries to analyze the historical data of banks. The bank changes from the traditional lending business to the main profit mode of deposit interest rate difference, and pays more attention to the income of intermediate business. Customers will also be transformed from large corporate customers to more important SME and retail customers. In this case, the traditional extensive sales mode. In the face of a large number of customers and complex sales of products appears to be a little inadequate. The core work of this paper includes design and implementation of two parts. The design part. First of all, the development of domestic and foreign banking business has been discussed and analyzed, and found that its business model has changed a lot. Through the in-depth analysis of the transformation of the business model, found in the current business model. Need more accurate marketing methods. Then through the bank sales research, determine the content of precise marketing requirements, including: data cleaning, customer integration, customer rating, customer life cycle management. Customer accurate marketing data language and so on. At the same time, the status of bank information construction is investigated and analyzed, and the data extraction is designed. Transformation and loading Extraction-Transformation-Loading ETL is the main technical architecture. Data mining as the means of the technical system. And according to the business requirements, design a cluster analysis method to deal with the data. After determining the technical framework. First, the process and data model of ETL processing are designed. The ETL hierarchical processing structure is worked out to achieve the loose coupling of the system. The ETL processing process is realized by using KETTEL tools, which can identify, locate and subdivide the huge customer group of the bank. And on this basis, the use of cross-marketing analysis, business cycle marketing analysis of the two analysis methods to achieve accurate customer marketing prediction. After analyzing the customer accurate marketing data. Again, the data is pushed to the web page through ETL to enter the pool of business opportunities. In the pool of business opportunities, the account manager claims the business opportunities. In the end, the thesis will sum up the work of the subject, and draw the conclusion that the accurate marketing based on data mining can greatly improve the marketing efficiency of the bank. This type of data enrichment industry. Effective data mining is not only to improve the efficiency of bank marketing, but also can be widely used, so we should increase the data mining of data enrichment industry, through data analysis to create value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP311.52

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