本文選題：RFM模型 + 客戶流失行為��； 參考：《北京郵電大學(xué)》2015年碩士論文

【摘要】：客戶終身價值一直以來都是企業(yè)進行客戶管理以及相關(guān)營銷活動的重要依據(jù)。通過對群體層面的客戶終身價值和個體層面的客戶終身價值進行分析,企業(yè)能夠?qū)ΜF(xiàn)存客戶的價值進行識別,并且不斷改進客戶管理方向以實現(xiàn)客戶價值最大化。 本文主要從群體層面和個體層面兩個維度來進行客戶終身價值的計算,分別通過兩種不同的數(shù)學(xué)模型來對客戶的價值進行全方位的分析,以期為企業(yè)對客戶的管理決策奠定基礎(chǔ)。 在Web2.0時代越來越多的消費者樂于在購物網(wǎng)站、點評類網(wǎng)站以及社交類網(wǎng)站上以文字的形式,發(fā)布對相關(guān)產(chǎn)品、服務(wù)、品牌或企業(yè)的評價。眾所周知,對于企業(yè)而言,用戶每產(chǎn)生一次購買行為都會為企業(yè)帶來一定的利潤,會給企業(yè)帶來一定的直接價值,而用戶每產(chǎn)生一次評論行為是否同樣會為企業(yè)帶來一定的影響與價值。 現(xiàn)階段研究者僅從消費者的購買行為出發(fā)對客戶的終身價值進行建模,并未有研究者綜合考慮了客戶的購買行為和評論行為。由于該研究點較為新穎,未出現(xiàn)相關(guān)的研究,因此本文決定將評論行為引入客戶終身價值建模領(lǐng)域,來進行相關(guān)的嘗試和探索。 在研究大眾點評網(wǎng)客戶的過程中,本文發(fā)現(xiàn)同一消費者會同時使用其點評平臺和團購平臺,從而使得用戶自身的兩種行為會產(chǎn)生相互影響,因此針對這種客戶特征,本文嘗試建立了群體層面和個體層面兩種改進的客戶終身價值模型。 本文的模型主要由兩部分組分：群體層面的客戶終身價值模型和個體層面的客戶終身價值模型。針對群體層面的模型研究,本文選取RFM作為基礎(chǔ)模型,將評論行為引入模型中,提出了綜合考慮兩種行為的改進RFM模型；針對個體層面的模型研究,本文首先利用Logit回歸模型對評論行為對購買行為的影響進行研究,提取出關(guān)鍵變量,并將這些變量作為協(xié)變量引入Pareto/NBD模型中,進一步的改進了模型對購買金額進行預(yù)測;再次向件利用Logit模型和改進Pareto/NBD的方法進行評論次數(shù)的預(yù)測；最后提出針對個體層面的客戶終身價值模型。實證結(jié)果顯示,預(yù)測模型都取得了很好的擬合和預(yù)測效果。 本研究發(fā)現(xiàn)了消費者自身評論行為對于其購買行為的影響,并且通過實證分析達(dá)到了較好的預(yù)測結(jié)果,這對于企業(yè)今后進行真正高價值用戶的識別,改進相關(guān)管理措施提供了重要依據(jù)。
[Abstract]:Customer lifetime value has always been an important basis for enterprise customer management and related marketing activities. By analyzing the customer lifetime value at the group level and the customer lifetime value at the individual level, the enterprise can identify the existing customer value and continuously improve the direction of customer management to maximize customer value. This article mainly from the group level and the individual level two dimensions carries on the customer lifetime value computation, respectively through two kinds of different mathematics models carries on the omni-directional analysis to the customer value. In order to establish the foundation for the enterprise to the customer management decision. In the Web 2.0 era, more and more consumers are willing to publish reviews of related products, services, brands or enterprises in the form of text on shopping sites, comment sites and social networks. As we all know, as far as enterprises are concerned, every time a user produces a purchase behavior, it will bring a certain profit to the enterprise, and will bring a certain direct value to the enterprise. And each time the user produces a comment behavior will also bring certain impact and value for the enterprise. At present, the researchers only model the customer's lifetime value based on the consumer's purchase behavior, and no researchers have considered the customer's purchase behavior and comment behavior synthetically. Because the research point is relatively novel and there is no related research, this paper decides to introduce the comment behavior into the field of customer lifetime value modeling to try and explore it. In the process of studying Dianping customers, this paper finds that the same consumer will use both the comment platform and the group purchase platform, so that the two behaviors of the users themselves will have mutual influence, so this paper aims at the characteristics of this kind of customers. This paper attempts to establish two improved customer lifetime value models: group level and individual level. The model is divided into two parts: customer lifetime value model at group level and customer lifetime value model at individual level. In this paper, we select RFM as the basic model, introduce the comment behavior into the model, put forward the improved RFM model which considers two kinds of behavior synthetically, and study the model of individual level. In this paper, logit regression model is first used to study the effect of comment behavior on purchase behavior, and key variables are extracted, and these variables are introduced into Pareto / NBD model as covariables to further improve the model to predict the purchase amount. At last, the author uses logit model and improved Pareto / NBD method to predict the number of reviews. Finally, a customer lifetime value model for individual level is proposed. The empirical results show that the prediction models have achieved good fitting and prediction results. This study finds out the influence of consumers' own comment behavior on their purchasing behavior, and achieves a good prediction result through empirical analysis, which is useful for enterprises to identify real high-value users in the future. To improve the relevant management measures to provide an important basis.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F274

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