本文選題：客戶識別 + 客戶開發(fā)��； 參考：《河南工業(yè)大學(xué)》2013年碩士論文

【摘要】：近年來，中國保險(xiǎn)市場快速擴(kuò)張的同時(shí)，客戶的識別成本和開發(fā)成本也在大幅提升。從節(jié)約成本的角度出發(fā)，如何準(zhǔn)確地識別目標(biāo)客戶和最大限度的開發(fā)現(xiàn)有客戶的潛在價(jià)值已成為保險(xiǎn)企業(yè)的難題，而數(shù)據(jù)挖掘技術(shù)的出現(xiàn)為這一問題的解決提供了更好的途徑。 在這樣的背景下，本文對保險(xiǎn)行業(yè)在客戶識別和客戶開發(fā)活動(dòng)中應(yīng)用數(shù)據(jù)挖掘技術(shù)的相關(guān)理論和實(shí)踐進(jìn)行探討。首先，闡述了CRM中的客戶識別與客戶開發(fā)理論，逐一對數(shù)據(jù)挖掘的概念、分類、主要算法和流程進(jìn)行了介紹，設(shè)計(jì)了保險(xiǎn)業(yè)的數(shù)據(jù)挖掘主題，實(shí)現(xiàn)了數(shù)據(jù)挖掘與CRM的結(jié)合。接著，結(jié)合XX人壽保險(xiǎn)公司存在的問題，從客戶和產(chǎn)品兩個(gè)角度出發(fā)，運(yùn)用數(shù)據(jù)挖掘軟件Clementine對提取的保險(xiǎn)公司客戶購買信息數(shù)據(jù)進(jìn)行以下三方面的實(shí)證分析，完成了客戶識別和客戶開發(fā)的任務(wù)： 第一，構(gòu)建了基于C5.0算法的目標(biāo)客戶分析模型，歸納出了購買和不購買意外保險(xiǎn)的客戶特征，利用這些客戶特征預(yù)測潛在客戶購買和不購買意外保險(xiǎn)的概率，以此來完成潛在客戶識別任務(wù)。 第二，構(gòu)建了基于Apriori算法的市場購物籃分析模型，挖掘出哪些險(xiǎn)種會(huì)被客戶同時(shí)購買，為企業(yè)制定合理的險(xiǎn)種組合策略提供借鑒，用于支持客戶開發(fā)工作。 第三，提出了基于K-means細(xì)分的交叉銷售模型，該模型的生成分為兩步：首先，根據(jù)年繳保費(fèi)和年收入這兩個(gè)維度預(yù)設(shè)將總體客戶劃分為Ⅰ類客戶、Ⅱ類客戶、Ⅲ類客戶和Ⅳ類客戶，然后分別對這四類客戶進(jìn)行K-means聚類，實(shí)現(xiàn)更加具體的客戶細(xì)分，并對運(yùn)行出來的聚類結(jié)果進(jìn)行了分析；然后，利用客戶細(xì)分模型中的聚類結(jié)果，找出各聚類組的特征險(xiǎn)種，旨在尋找向現(xiàn)有客戶銷售新險(xiǎn)種或服務(wù)的機(jī)會(huì)，實(shí)現(xiàn)了客戶識別和客戶開發(fā)兩項(xiàng)任務(wù)的結(jié)合。 文章最后，結(jié)合本文得出的主要結(jié)論和在研究過程中遇到的問題，指出了在保險(xiǎn)業(yè)中實(shí)施數(shù)據(jù)挖掘技術(shù)的未來研究方向和不足之處。
[Abstract]:In recent years, the rapid expansion of the insurance market in China, customer identification costs and development costs are also rising significantly. From the perspective of cost saving, how to accurately identify the target customers and maximize the potential value of existing customers has become a difficult problem for insurance enterprises, and the emergence of data mining technology provides a better way to solve this problem. In this context, this paper discusses the theory and practice of applying data mining technology to customer identification and customer development in insurance industry. Firstly, this paper introduces the theory of customer identification and customer development in CRM, introduces the concept, classification, algorithm and flow of data mining one by one, designs the topic of data mining in insurance industry, and realizes the combination of data mining and CRM. Then, combined with the problems of XX life insurance company, from the customer and product point of view, using the data mining software Clementine to extract the insurance company customer purchase information data from the following three aspects of empirical analysis. Completed the tasks of customer identification and customer development: First, the target customer analysis model based on C5.0 algorithm is constructed, and the customer characteristics of buying and not buying accident insurance are summed up, and the probability of potential customers buying and not buying accident insurance is predicted by these customer characteristics. In order to complete the potential customer identification task. Secondly, the market shopping basket analysis model based on Apriori algorithm is constructed to find out which kinds of insurance will be purchased by customers at the same time. Thirdly, a cross-selling model based on K-means subdivision is proposed. The model is divided into two steps: first, according to the two dimensions of annual premium and annual income, the total customers are divided into class I customers, class II customers. The third class customer and the 鈪，

本文編號：1802756