發(fā)布時間：2018-05-08 15:43

  本文選題：電話營銷 + 數(shù)據(jù)挖掘�。� 參考：《蘭州大學》2017年碩士論文

【摘要】：隨著金融的全球化和自由化進程的加快,銀行業(yè)的競爭越來越激烈,依靠存貸差的傳統(tǒng)盈利模式已經(jīng)很難再持續(xù)發(fā)展下去。在營銷領域,傳統(tǒng)的粗放式的客戶營銷策略轉(zhuǎn)向精細化的客戶營銷策略,展開以客戶為中心的精準營銷活動已是大勢所趨。所有銀行營銷活動都將依賴于其龐大的數(shù)據(jù)集,單純地利用人工來分析這些數(shù)據(jù)是不可能的,數(shù)據(jù)挖掘模型有助于進行這些數(shù)據(jù)集的分析。本文以預測銀行電話營銷結果為目標,首先對研究問題的背景、意義、國內(nèi)外研究現(xiàn)狀以及研究方法與思路進行介紹。其次介紹了本研究中所涉及的數(shù)據(jù)挖掘技術,包括分類回歸樹、邏輯回歸、隨機森林、梯度迭代決策樹等算法,在此基礎上,介紹了xgboost集成學習框架。并介紹了處理不均衡數(shù)據(jù)集的邊界合成少數(shù)類過抽樣算法(BorderlineSMOTE),并將該算法與以上五種數(shù)據(jù)挖掘算法相結合,建立了銀行電話營銷分類模型。通過ROC曲線、AUC值、敏感度、特異度等指標發(fā)現(xiàn),Borderline-SMOTE算法結合xgboost所得到的模型預測效果最佳,AUC值達到0.97。其次,xgboost模型不管是在預測效果還是運算效率上,都要優(yōu)于本文構建的其它模型。本文還將兩種信息提取方法(變量重要性分析和CART規(guī)則提取)用于提取數(shù)據(jù)集的關鍵信息,并揭示了幾個關鍵屬性(例如,Euribor3m、持續(xù)時間、年齡等)。這樣的信息提取證實了所獲得的模型對于電話營銷活動管理者是可信的和有價值的。
[Abstract]:With the acceleration of financial globalization and liberalization, the competition of banking is becoming more and more fierce. It is difficult to continue to develop the traditional profit model which depends on the difference between deposit and loan. In the field of marketing, the traditional extensive customer marketing strategy turns to the refined customer marketing strategy, and it is the trend of the times to launch the client-centered precision marketing activities. All marketing activities of banks will depend on their huge data sets. It is impossible to analyze these data simply by using manpower. The data mining model is helpful for the analysis of these data sets. This paper aims at predicting the results of bank telephone marketing. Firstly, it introduces the background, significance, current research situation, research methods and ideas of the research. Secondly, this paper introduces the data mining techniques involved in this study, including classified regression tree, logical regression, stochastic forest, gradient iterative decision tree and so on. On this basis, the integrated learning framework of xgboost is introduced. This paper also introduces the borderline SMOTET algorithm of edge synthesis for dealing with unbalanced data sets, and establishes a bank telephone marketing classification model by combining this algorithm with the above five data mining algorithms. Through the ROC curve, sensitivity, specificity and other indicators, we found that Borderline-SMOTE algorithm combined with xgboost has the best prediction effect of 0.97. Secondly, the xgboost model is superior to the other models in terms of prediction effect and operational efficiency. In this paper, two information extraction methods (variable importance analysis and CART rule extraction) are used to extract the key information of the data set, and several key attributes (such as Euribor3m, duration, age, etc.) are revealed. Such information extraction confirms that the obtained model is credible and valuable to telephone marketing managers.
【學位授予單位】：蘭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F274;F831.2

【參考文獻】

相關期刊論文 前2條

1 王建榮;;企業(yè)有效開展電話營銷的策略[J];中國市場;2012年48期

2 杜玉新;;保險電話營銷問題研究[J];金融教學與研究;2010年06期

相關碩士學位論文 前2條

1 廖琳;決策樹在學生信息管理系統(tǒng)中的應用研究[D];廣西大學;2014年

2 柳楊亮;基于客戶交易行為的事件式精準營銷在交通銀行零售板塊中的應用研究[D];華東理工大學;2013年

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本文編號：1861992