網(wǎng)購(gòu)客戶流失的實(shí)證分析
發(fā)布時(shí)間：2018-08-28 09:41
【摘要】：近年來(lái),隨著生產(chǎn)力的不斷提高,信息技術(shù)的大力發(fā)展,互聯(lián)網(wǎng)成為了當(dāng)今社會(huì)的重要戰(zhàn)略資源。伴隨著互聯(lián)網(wǎng)時(shí)代的到來(lái),企業(yè)的商務(wù)環(huán)境也發(fā)生了翻天覆地的變化,電子商務(wù)平臺(tái)得以構(gòu)建。電子商務(wù)模式的簡(jiǎn)單、快捷和方便性吸引了大量客戶的目光。許多客戶紛紛轉(zhuǎn)向了這個(gè)新興的市場(chǎng),加入了網(wǎng)購(gòu)的大軍。 在電子商務(wù)平臺(tái)上,企業(yè)僅僅通過(guò)吸引新客戶,提高市場(chǎng)份額來(lái)贏得這場(chǎng)商戰(zhàn)的勝利是遠(yuǎn)遠(yuǎn)不夠的。電商企業(yè)還必須做好客戶流失的防御工作,解決好客戶“進(jìn)”和“出”的問(wèn)題,才能達(dá)到電商企業(yè)對(duì)客戶的有效管理的目的。目前關(guān)于客戶流失的研究更多的還是關(guān)注傳統(tǒng)企業(yè),而較少去涉及B2C平臺(tái)或C2C平臺(tái)這種新型商務(wù)環(huán)境中的企業(yè)。在這個(gè)網(wǎng)購(gòu)已經(jīng)成為一種生活方式的時(shí)代,傳統(tǒng)研究還很難在電子商務(wù)領(lǐng)域得到運(yùn)用。因此,本文將傳統(tǒng)的客戶流失預(yù)測(cè)模型與電子商務(wù)模式相結(jié)合進(jìn)行研究,以達(dá)到電商企業(yè)的最新要求。 電商企業(yè)每天都會(huì)產(chǎn)生海量的客戶購(gòu)買數(shù)據(jù),通過(guò)分析客戶購(gòu)買行為來(lái)預(yù)測(cè)客戶流失對(duì)于企業(yè)來(lái)說(shuō)至關(guān)重要,數(shù)據(jù)挖掘技術(shù)在商業(yè)上的應(yīng)用也由此而生。利用數(shù)據(jù)挖掘技術(shù)對(duì)電子商務(wù)網(wǎng)站的海量客戶數(shù)據(jù)進(jìn)行分析并研究,可以得出網(wǎng)購(gòu)客戶的流失預(yù)測(cè)模型,從而為電子商務(wù)服務(wù)商提供有價(jià)值的信息。 數(shù)據(jù)挖掘技術(shù)是一種過(guò)程,這個(gè)過(guò)程整合了數(shù)學(xué)、統(tǒng)計(jì)、人工智能和機(jī)器學(xué)習(xí)的技術(shù),從而可以在大型的數(shù)據(jù)庫(kù)中提取和識(shí)別出對(duì)企業(yè)有用的信息。數(shù)據(jù)挖掘技術(shù)在客戶關(guān)系管理上的應(yīng)用已經(jīng)成為了全球經(jīng)濟(jì)化時(shí)代的一個(gè)必然的趨勢(shì)。數(shù)據(jù)挖掘技術(shù)是一種分析客戶關(guān)系管理的有效工具,這種技術(shù)工具能夠幫助企業(yè)儲(chǔ)存和整合企業(yè)和客戶之間的海量數(shù)據(jù),分析出隱藏在這些海量數(shù)據(jù)下的大量信息,并幫助企業(yè)分析現(xiàn)有的客戶,找出企業(yè)的潛在客戶以及對(duì)企業(yè)有價(jià)值的高價(jià)值客戶和浪費(fèi)企業(yè)資源、卻對(duì)企業(yè)沒(méi)有任何盈利的負(fù)價(jià)值客戶。這些信息的整合能夠使得企業(yè)在高效的全球化進(jìn)程中占據(jù)信息優(yōu)勢(shì),更好地幫助企業(yè)提高企業(yè)資源的利用效率,提高企業(yè)營(yíng)銷政策的效果。 本文基于數(shù)據(jù)挖掘技術(shù)構(gòu)建客戶流失預(yù)測(cè)模型,引入了客戶關(guān)系管理理論中對(duì)客戶歷史購(gòu)買行為進(jìn)行描述的RFM理論,并結(jié)合當(dāng)當(dāng)網(wǎng)客戶的實(shí)際情況,對(duì)網(wǎng)購(gòu)客戶的流失預(yù)測(cè)模型做出了修正,從而可以運(yùn)用少數(shù)關(guān)鍵性指標(biāo)對(duì)客戶流失進(jìn)行預(yù)測(cè)。 本文的主要內(nèi)容包括：(1)對(duì)已有的客戶流失理論和技術(shù)進(jìn)行總結(jié);(2)對(duì)當(dāng)當(dāng)網(wǎng)客戶購(gòu)買數(shù)據(jù)進(jìn)行探索性分析；(3)基于數(shù)據(jù)挖掘技術(shù)分析當(dāng)當(dāng)網(wǎng)購(gòu)客戶,構(gòu)建網(wǎng)購(gòu)客戶流失預(yù)測(cè)模型。各章節(jié)主要內(nèi)容如下： 第一章是緒論部分。主要說(shuō)明本文的研究背景、研究問(wèn)題、研究?jī)?nèi)容以及研究的成果。 第二章是理論基礎(chǔ)部分。主要對(duì)客戶關(guān)系管理理論、客戶流失預(yù)測(cè)理論和客戶細(xì)分理論進(jìn)行了概括性地論述。 第三章主要研究網(wǎng)購(gòu)客戶流失預(yù)測(cè)模型的構(gòu)建,重點(diǎn)描述了在客戶流失預(yù)測(cè)中應(yīng)用廣泛的邏輯斯蒂回歸、決策樹、神經(jīng)網(wǎng)絡(luò)三種技術(shù)的理論。 第四章對(duì)當(dāng)當(dāng)網(wǎng)客戶購(gòu)買數(shù)據(jù)的實(shí)證分析。本文基于RFM理論提取出客戶的購(gòu)買行為數(shù)據(jù),并對(duì)客戶的行為做出了分析。 第五章是模型構(gòu)建部分。本文主要是在現(xiàn)有的研究基礎(chǔ)上,針對(duì)B2C平臺(tái)的客戶提出了結(jié)合RFM理論和數(shù)據(jù)挖掘技術(shù)的網(wǎng)購(gòu)客戶流失預(yù)測(cè)模型,并對(duì)模型的結(jié)果進(jìn)行了評(píng)估。 第六章對(duì)論文的研究工作進(jìn)行了總結(jié),并提出了研究?jī)?nèi)容的局限性,指出今后的研究方向。
[Abstract]:In recent years, with the continuous improvement of productivity and the rapid development of information technology, the Internet has become an important strategic resource in today's society. With the advent of the Internet era, the business environment of enterprises has undergone tremendous changes, e-commerce platform can be constructed. The simplicity, speediness and convenience of e-commerce model has attracted great attention. A lot of customers have turned to this new market and joined the army of online shopping.
On the platform of E-commerce, it is far from enough for an enterprise to win the business battle only by attracting new customers and increasing market share. E-commerce enterprises must also do a good job in preventing the loss of customers, and solve the problems of "entering" and "exiting" customers, so as to achieve the goal of effective customer management for E-commerce enterprises. Customer churn research is more concerned about traditional enterprises, but less about the B2C platform or C2C platform in this new business environment. In this era of online shopping has become a way of life, traditional research is difficult to be used in the field of e-commerce. Therefore, this paper will traditional customer churn prediction model and electronics. The business mode is combined to study to meet the latest requirements of e-commerce enterprises.
E-commerce enterprises produce a large amount of customer purchase data every day. It is very important for enterprises to predict customer churn by analyzing customer purchase behavior, and the application of data mining technology in commerce comes into being. Purchase customer churn prediction model, so as to provide valuable information for e-commerce service providers.
Data mining technology is a process, which integrates mathematics, statistics, artificial intelligence and machine learning technology, so as to extract and identify useful information from large databases. The application of data mining technology in customer relationship management has become an inevitable trend in the era of global economy. Data mining technology is an effective tool to analyze customer relationship management. This technology tool can help enterprises store and integrate massive data between enterprises and customers, analyze a large amount of information hidden in these massive data, and help enterprises analyze existing customers, identify potential customers of enterprises and value to enterprises. The integration of these information can make the enterprise occupy the information superiority in the highly efficient globalization process, and help the enterprise to improve the utilization efficiency of enterprise resources and improve the effect of enterprise marketing policy.
Based on the data mining technology, this paper constructs the customer churn prediction model, introduces the RFM theory which describes the customer's historical purchasing behavior in the customer relationship management theory, and amends the customer churn prediction model according to the actual situation of Dangdang customers, so that a few key indicators can be used to predict the customer churn in. Forecast.
The main contents of this paper include: (1) summarizing the existing customer churn theory and technology; (2) exploratory analysis of Dangdang customer purchasing data; (3) building a customer churn prediction model based on data mining technology.
The first chapter is the introduction. It mainly explains the research background, research problems, research contents and research results.
The second chapter is the theoretical basis. It mainly discusses the customer relationship management theory, customer churn prediction theory and customer segmentation theory.
Chapter 3 mainly studies the construction of customer churn prediction model for online shopping. It mainly describes the theory of Logistic Regression, Decision Tree and Neural Network which are widely used in customer churn prediction.
The fourth chapter is the empirical analysis of Dangdang's customer purchase data. Based on RFM theory, this paper extracts the customer purchase behavior data and analyzes the customer behavior.
The fifth chapter is the construction of the model. Based on the existing research, this paper proposes a customer churn prediction model for online shopping combined with RFM theory and data mining technology for B2C platform customers, and evaluates the results of the model.
Chapter 6 summarizes the research work of the paper, puts forward the limitations of the research content, and points out the future research direction.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F713.36
本文編號(hào)：2209013
[Abstract]:In recent years, with the continuous improvement of productivity and the rapid development of information technology, the Internet has become an important strategic resource in today's society. With the advent of the Internet era, the business environment of enterprises has undergone tremendous changes, e-commerce platform can be constructed. The simplicity, speediness and convenience of e-commerce model has attracted great attention. A lot of customers have turned to this new market and joined the army of online shopping.
On the platform of E-commerce, it is far from enough for an enterprise to win the business battle only by attracting new customers and increasing market share. E-commerce enterprises must also do a good job in preventing the loss of customers, and solve the problems of "entering" and "exiting" customers, so as to achieve the goal of effective customer management for E-commerce enterprises. Customer churn research is more concerned about traditional enterprises, but less about the B2C platform or C2C platform in this new business environment. In this era of online shopping has become a way of life, traditional research is difficult to be used in the field of e-commerce. Therefore, this paper will traditional customer churn prediction model and electronics. The business mode is combined to study to meet the latest requirements of e-commerce enterprises.
E-commerce enterprises produce a large amount of customer purchase data every day. It is very important for enterprises to predict customer churn by analyzing customer purchase behavior, and the application of data mining technology in commerce comes into being. Purchase customer churn prediction model, so as to provide valuable information for e-commerce service providers.
Data mining technology is a process, which integrates mathematics, statistics, artificial intelligence and machine learning technology, so as to extract and identify useful information from large databases. The application of data mining technology in customer relationship management has become an inevitable trend in the era of global economy. Data mining technology is an effective tool to analyze customer relationship management. This technology tool can help enterprises store and integrate massive data between enterprises and customers, analyze a large amount of information hidden in these massive data, and help enterprises analyze existing customers, identify potential customers of enterprises and value to enterprises. The integration of these information can make the enterprise occupy the information superiority in the highly efficient globalization process, and help the enterprise to improve the utilization efficiency of enterprise resources and improve the effect of enterprise marketing policy.
Based on the data mining technology, this paper constructs the customer churn prediction model, introduces the RFM theory which describes the customer's historical purchasing behavior in the customer relationship management theory, and amends the customer churn prediction model according to the actual situation of Dangdang customers, so that a few key indicators can be used to predict the customer churn in. Forecast.
The main contents of this paper include: (1) summarizing the existing customer churn theory and technology; (2) exploratory analysis of Dangdang customer purchasing data; (3) building a customer churn prediction model based on data mining technology.
The first chapter is the introduction. It mainly explains the research background, research problems, research contents and research results.
The second chapter is the theoretical basis. It mainly discusses the customer relationship management theory, customer churn prediction theory and customer segmentation theory.
Chapter 3 mainly studies the construction of customer churn prediction model for online shopping. It mainly describes the theory of Logistic Regression, Decision Tree and Neural Network which are widely used in customer churn prediction.
The fourth chapter is the empirical analysis of Dangdang's customer purchase data. Based on RFM theory, this paper extracts the customer purchase behavior data and analyzes the customer behavior.
The fifth chapter is the construction of the model. Based on the existing research, this paper proposes a customer churn prediction model for online shopping combined with RFM theory and data mining technology for B2C platform customers, and evaluates the results of the model.
Chapter 6 summarizes the research work of the paper, puts forward the limitations of the research content, and points out the future research direction.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F713.36
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