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(jià)值一直以來(lái)都是企業(yè)進(jìn)行客戶管理以及相關(guān)營(yíng)銷活動(dòng)的重要依據(jù)。通過(guò)對(duì)群體層面的客戶終身價(jià)值和個(gè)體層面的客戶終身價(jià)值進(jìn)行分析,企業(yè)能夠?qū)ΜF(xiàn)存客戶的價(jià)值進(jìn)行識(shí)別,并且不斷改進(jìn)客戶管理方向以實(shí)現(xiàn)客戶價(jià)值最大化。 本文主要從群體層面和個(gè)體層面兩個(gè)維度來(lái)進(jìn)行客戶終身價(jià)值的計(jì)算,分別通過(guò)兩種不同的數(shù)學(xué)模型來(lái)對(duì)客戶的價(jià)值進(jìn)行全方位的分析,以期為企業(yè)對(duì)客戶的管理決策奠定基礎(chǔ)。 在Web2.0時(shí)代越來(lái)越多的消費(fèi)者樂(lè)于在購(gòu)物網(wǎng)站、點(diǎn)評(píng)類網(wǎng)站以及社交類網(wǎng)站上以文字的形式,發(fā)布對(duì)相關(guān)產(chǎn)品、服務(wù)、品牌或企業(yè)的評(píng)價(jià)。眾所周知,對(duì)于企業(yè)而言,用戶每產(chǎn)生一次購(gòu)買行為都會(huì)為企業(yè)帶來(lái)一定的利潤(rùn),會(huì)給企業(yè)帶來(lái)一定的直接價(jià)值,而用戶每產(chǎn)生一次評(píng)論行為是否同樣會(huì)為企業(yè)帶來(lái)一定的影響與價(jià)值。 現(xiàn)階段研究者僅從消費(fèi)者的購(gòu)買行為出發(fā)對(duì)客戶的終身價(jià)值進(jìn)行建模,并未有研究者綜合考慮了客戶的購(gòu)買行為和評(píng)論行為。由于該研究點(diǎn)較為新穎,未出現(xiàn)相關(guān)的研究,因此本文決定將評(píng)論行為引入客戶終身價(jià)值建模領(lǐng)域,來(lái)進(jìn)行相關(guān)的嘗試和探索。 在研究大眾點(diǎn)評(píng)網(wǎng)客戶的過(guò)程中,本文發(fā)現(xiàn)同一消費(fèi)者會(huì)同時(shí)使用其點(diǎn)評(píng)平臺(tái)和團(tuán)購(gòu)平臺(tái),從而使得用戶自身的兩種行為會(huì)產(chǎn)生相互影響,因此針對(duì)這種客戶特征,本文嘗試建立了群體層面和個(gè)體層面兩種改進(jìn)的客戶終身價(jià)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(jià)值模型和個(gè)體層面的客戶終身價(jià)值模型。針對(duì)群體層面的模型研究,本文選取RFM作為基礎(chǔ)模型,將評(píng)論行為引入模型中,提出了綜合考慮兩種行為的改進(jìn)RFM模型；針對(duì)個(gè)體層面的模型研究,本文首先利用Logit回歸模型對(duì)評(píng)論行為對(duì)購(gòu)買行為的影響進(jìn)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(jìn)一步的改進(jìn)了模型對(duì)購(gòu)買金額進(jìn)行預(yù)測(cè);再次向件利用Logit模型和改進(jìn)Pareto/NBD的方法進(jìn)行評(píng)論次數(shù)的預(yù)測(cè)；最后提出針對(duì)個(gè)體層面的客戶終身價(jià)值模型。實(shí)證結(jié)果顯示,預(yù)測(cè)模型都取得了很好的擬合和預(yù)測(cè)效果。 本研究發(fā)現(xiàn)了消費(fèi)者自身評(píng)論行為對(duì)于其購(gòu)買行為的影響,并且通過(guò)實(shí)證分析達(dá)到了較好的預(yù)測(cè)結(jié)果,這對(duì)于企業(yè)今后進(jìn)行真正高價(jià)值用戶的識(shí)別,改進(jìn)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(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F274

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