本文選題：電信運營企業(yè) + 客戶流失��； 參考：《哈爾濱工程大學(xué)》2013年博士論文

【摘要】：電信運營企業(yè)客戶流失是一個受多因素影響的復(fù)雜問題，尤其是2008年以后我國電信業(yè)針對3G牌照的發(fā)放又進(jìn)行了新一輪的電信重組，全業(yè)務(wù)運營下的三大運營企業(yè)從此展開了激烈的客戶市場競爭。由于我國移動客戶群體龐大，中低端客戶在不同運營企業(yè)間流動性強，因此，針對客戶流失的成因分析和建立客戶流失預(yù)測模型具有重要的理論價值和現(xiàn)實意義。 本文詳細(xì)分析了國內(nèi)外學(xué)者在客戶流失領(lǐng)域的研究成果，探討了客戶流失的影響因素和客戶流失預(yù)測的方法。通過對3G時代電信運營環(huán)境的分析，總結(jié)了國內(nèi)外電信運營企業(yè)客戶流失的現(xiàn)狀，并從電信運營環(huán)境角度、運營企業(yè)流失客戶數(shù)據(jù)統(tǒng)計分析角度深入研究了電信運營企業(yè)客戶流失的成因，歸納得到客戶流失成因的8種類型。據(jù)此，基于數(shù)據(jù)挖掘和客戶價值的理論和方法，研究了BP神經(jīng)網(wǎng)絡(luò)算法、支持向量機算法、C5.0決策樹算法在客戶流失預(yù)測上的應(yīng)用，為了獲得更好的預(yù)測效果，構(gòu)建了Lagrange組合預(yù)測模型和基于客戶價值的預(yù)測模型。重點就以下問題進(jìn)行了研究： 在廣泛研究和借鑒國內(nèi)外相關(guān)數(shù)據(jù)挖掘理論及成果的基礎(chǔ)上，探討了電信運營企業(yè)的客戶構(gòu)成，，深入分析了客戶流失與流失客戶的概念、以及客戶流失的現(xiàn)象與特征，從而梳理給出三戶關(guān)系模型。 對構(gòu)建模型的客戶屬性進(jìn)行了分類，即原始屬性與衍生屬性。以往對電信客戶流失預(yù)測的研究都是采用客戶消費行為、個人信息、繳費信息等原始屬性數(shù)據(jù)，這些原始屬性數(shù)據(jù)很難真實地反映客戶流失的行為；加入了衍生屬性，如：月租標(biāo)志、呼轉(zhuǎn)標(biāo)志、賬戶余額標(biāo)志、充值行為標(biāo)志等，其數(shù)據(jù)集能更好的預(yù)測客戶流失，使得預(yù)測的命中率更高，計算的客戶價值更具研究意義。 通過分析客戶協(xié)議數(shù)據(jù)、消費行為數(shù)據(jù)和賬單數(shù)據(jù)得出與客戶流失密切相關(guān)的屬性集，根據(jù)獲取運營企業(yè)數(shù)據(jù)的難易程度，建立了客戶流失預(yù)測指標(biāo)體系，并基于數(shù)據(jù)挖掘算法建立了Lagrange組合預(yù)測模型。針對客戶流失預(yù)測問題的研究，選擇了數(shù)據(jù)挖掘的三種經(jīng)典算法（BP、SVM、C5.0）構(gòu)建了單一客戶流失預(yù)測模型，并通過對模型的評估顯示，任意單一模型都沒有最優(yōu)。據(jù)此借助Lagrange函數(shù)求極值的思想構(gòu)建了客戶流失的組合預(yù)測模型，其預(yù)測效果比單一模型更理想。 提出二維度預(yù)防客戶流失的方法，即基于Lagrange的客戶流失組合預(yù)測與基于客戶價值的流失客戶評價。根據(jù)組合預(yù)測模型預(yù)測得到的客戶流失名單是否有挽留的價值，或者說是否有對這樣的客戶有再投入成本挽留的必要，取決于該客戶對運營企業(yè)是否是有價值客戶，并依據(jù)這兩種途徑的預(yù)測結(jié)果，再分析客戶流失的根本原因。 最后，通過對客戶流失成因的分析，以及對客戶流失預(yù)測模型的評估，提出電信運營企業(yè)降低客戶流失的措施與建議。
[Abstract]:Customer churn is a complex problem affected by many factors, especially after 2008, the telecom industry of our country has carried out a new round of telecom reorganization aiming at the issue of 3G license. All-business operation under the three major operating enterprises from then on launched a fierce customer market competition. Because of the huge mobile customer group and the strong liquidity among the middle and low end customers in different operating enterprises, it is of great theoretical value and practical significance to analyze the causes of customer turnover and to establish a customer churn prediction model. In this paper, the research results of domestic and foreign scholars in the field of customer churn are analyzed in detail, and the influencing factors of customer churn and the method of customer churn prediction are discussed. Based on the analysis of telecom operating environment in 3G era, this paper summarizes the current situation of customer churn in telecom operation enterprises at home and abroad, and from the point of view of telecommunication operation environment, In this paper, the causes of customer churn in telecom operation enterprises are studied from the point of view of statistical analysis of customer churn data, and eight types of customer churn are concluded. Based on the theory and method of data mining and customer value, this paper studies the application of BP neural network algorithm, support vector machine algorithm and C5.0 decision tree algorithm in customer churn prediction. Lagrange combination forecasting model and customer value based forecasting model are constructed. Research focused on the following issues: On the basis of extensive research and reference of relevant data mining theories and achievements at home and abroad, this paper probes into the customer structure of telecom operation enterprises, deeply analyzes the concepts of customer churn and customer churn, as well as the phenomena and characteristics of customer churn. In order to sort out the three-family relationship model. The customer attributes of the building model are classified, that is, the original attributes and the derived attributes. In the past, the research of telecom customer churn prediction used the original attribute data, such as customer consumption behavior, personal information, payment information and so on. These original attribute data can hardly truly reflect the behavior of customer churn. Such as: monthly rent sign, call mark, account balance mark, recharge behavior mark, etc., its data set can better predict customer churn, make forecast hit ratio higher, calculate customer value more research significance. Through the analysis of customer agreement data, consumer behavior data and billing data, the attribute set which is closely related to customer churn is obtained. According to the degree of difficulty in obtaining operation enterprise data, a customer churn prediction index system is established. Based on the data mining algorithm, the combined prediction model of Lagrange is established. In order to solve the problem of customer churn prediction, three classical algorithms of data mining are selected to construct a single customer churn prediction model. The evaluation of the model shows that there is no optimal model for any single model. Based on the idea of calculating extreme value of Lagrange function, the combined forecasting model of customer churn is constructed, and its prediction effect is more ideal than that of single model. This paper presents a two-dimensional method to prevent customer churn, that is, the combination prediction of customer churn based on Lagrange and the evaluation of customer churn based on customer value. Whether the customer churn list based on the combination forecasting model has the value of retention, or whether it is necessary to retain such a customer at a cost, depends on whether the customer is a valuable customer to the operating enterprise. And according to the forecast results of these two ways, the root cause of customer turnover is analyzed again. Finally, through the analysis of the causes of customer churn and the evaluation of customer churn prediction model, the paper puts forward the measures and suggestions to reduce customer churn in telecom operation enterprises.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：F274;F626

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本文編號：1917621