本文選題：數(shù)據(jù)挖掘 + 客戶分類�。� 參考：《沈陽工業(yè)大學》2016年碩士論文

【摘要】：隨著電子商務的迅猛發(fā)展和信息處理技術(shù)能力的增強,數(shù)據(jù)挖掘技術(shù)在商務領(lǐng)域得到了廣泛的應用,這一應用能夠提升商家對客戶的識別、分析和需求滿足能力。但是由于不同行業(yè)的數(shù)據(jù)采集點、數(shù)據(jù)處理要求及結(jié)果體現(xiàn)形式的不同,需要針對不同的行業(yè)甚至是企業(yè)構(gòu)建適合自身的模型和運算方法。本文總體上在分析B2C客戶消費特點的基礎(chǔ)上,以電子商務相關(guān)理論為指導,綜合運用幾種數(shù)據(jù)挖掘技術(shù)于企業(yè)客戶關(guān)系管理中,以期望技術(shù)的運用提升企業(yè)的客戶關(guān)系管理水平。本文在介紹相關(guān)概念和技術(shù)手段的基礎(chǔ)上,首先分析了B2C下客戶消費的特點,并著重分析了客戶分析的流程,分別分析了客戶分析的總體流程和RFM理論下的客戶數(shù)據(jù)分析流程,并基于RFM視角下客戶價值和基于客戶屬性、消費心理特征和網(wǎng)絡(luò)影響等非價值指標對客戶進行了分類,并構(gòu)建了基于二維聚類的客戶分類模型。其次,基于不同分類,針對于客戶維護重點是要利用關(guān)聯(lián)規(guī)則對客戶的潛在需求進行挖掘和分析,并試探性的從掃描次數(shù)減少的角度對關(guān)聯(lián)規(guī)則的Apriori算法進行了改進；針對新客戶主要是根據(jù)其基本注冊信息和瀏覽記錄利用個性化推薦技術(shù)進行推薦,并考慮數(shù)據(jù)量的影響引入規(guī)模因子改進了相關(guān)系數(shù)的計算,以提升預測的精度。再次,利用分類結(jié)果和關(guān)聯(lián)規(guī)則構(gòu)建了退出類、重點類、普通類、潛力類及黃金類五類客戶關(guān)系管理的策略,并提出了實施的保障。最后通過案例分析證明了本文提出方法的有效性。本文試圖采用雙聚類組合的方式對客戶進行分類,并探索改進關(guān)聯(lián)規(guī)則和推薦系統(tǒng)的算法,為B2C企業(yè)的客戶關(guān)系管理水平提升做出一定的支撐。
[Abstract]:With the rapid development of electronic commerce and the enhancement of information processing technology, data mining technology has been widely used in the field of commerce. This application can improve the ability of merchants to identify, analyze and meet the needs of customers. However, due to the difference of data collection points, data processing requirements and the results of different industries, it is necessary to build suitable models and operation methods for different industries or even enterprises. Based on the analysis of the characteristics of B2C customer consumption, and guided by the related theory of electronic commerce, this paper synthetically applies several kinds of data mining techniques to enterprise customer relationship management. Improve the level of customer relationship management with the application of expected technology. Based on the introduction of related concepts and technical means, this paper first analyzes the characteristics of customer consumption under B2C, and focuses on the process of customer analysis, and analyzes the overall flow of customer analysis and the flow of customer data analysis based on RFM theory, respectively. Customers are classified based on customer value and non-value indicators such as customer attributes, consumer psychological characteristics and network influence from the perspective of RFM, and a customer classification model based on two-dimensional clustering is constructed. Secondly, based on different classification, the focus of customer maintenance is to mine and analyze the potential needs of customers by using association rules, and tentatively improve the Apriori algorithm of association rules from the angle of reducing scanning times. According to the basic registration information and browsing record, the new customers are recommended by personalized recommendation technology, and the scale factor is introduced to improve the calculation of correlation coefficient to improve the accuracy of prediction. Thirdly, by using classification results and association rules, the strategies of customer relationship management of exit class, key class, common class, potential class and gold class are constructed, and the security of implementation is put forward. Finally, the effectiveness of the proposed method is proved by a case study. In this paper, we try to classify customers by using double clustering and combination, and explore the algorithm of improving association rules and recommendation system to support the improvement of customer relationship management level in B2C enterprises.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP311.13;F274

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