網購客戶流失的實證分析
發(fā)布時間：2018-08-28 09:41
【摘要】：近年來,隨著生產力的不斷提高,信息技術的大力發(fā)展,互聯網成為了當今社會的重要戰(zhàn)略資源。伴隨著互聯網時代的到來,企業(yè)的商務環(huán)境也發(fā)生了翻天覆地的變化,電子商務平臺得以構建。電子商務模式的簡單、快捷和方便性吸引了大量客戶的目光。許多客戶紛紛轉向了這個新興的市場,加入了網購的大軍。 在電子商務平臺上,企業(yè)僅僅通過吸引新客戶,提高市場份額來贏得這場商戰(zhàn)的勝利是遠遠不夠的。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”和“出”的問題,才能達到電商企業(yè)對客戶的有效管理的目的。目前關于客戶流失的研究更多的還是關注傳統企業(yè),而較少去涉及B2C平臺或C2C平臺這種新型商務環(huán)境中的企業(yè)。在這個網購已經成為一種生活方式的時代,傳統研究還很難在電子商務領域得到運用。因此,本文將傳統的客戶流失預測模型與電子商務模式相結合進行研究,以達到電商企業(yè)的最新要求。 電商企業(yè)每天都會產生海量的客戶購買數據,通過分析客戶購買行為來預測客戶流失對于企業(yè)來說至關重要,數據挖掘技術在商業(yè)上的應用也由此而生。利用數據挖掘技術對電子商務網站的海量客戶數據進行分析并研究,可以得出網購客戶的流失預測模型,從而為電子商務服務商提供有價值的信息。 數據挖掘技術是一種過程,這個過程整合了數學、統計、人工智能和機器學習的技術,從而可以在大型的數據庫中提取和識別出對企業(yè)有用的信息。數據挖掘技術在客戶關系管理上的應用已經成為了全球經濟化時代的一個必然的趨勢。數據挖掘技術是一種分析客戶關系管理的有效工具,這種技術工具能夠幫助企業(yè)儲存和整合企業(yè)和客戶之間的海量數據,分析出隱藏在這些海量數據下的大量信息,并幫助企業(yè)分析現有的客戶,找出企業(yè)的潛在客戶以及對企業(yè)有價值的高價值客戶和浪費企業(yè)資源、卻對企業(yè)沒有任何盈利的負價值客戶。這些信息的整合能夠使得企業(yè)在高效的全球化進程中占據信息優(yōu)勢,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)營銷政策的效果。 本文基于數據挖掘技術構建客戶流失預測模型,引入了客戶關系管理理論中對客戶歷史購買行為進行描述的RFM理論,并結合當當網客戶的實際情況,對網購客戶的流失預測模型做出了修正,從而可以運用少數關鍵性指標對客戶流失進行預測。 本文的主要內容包括：(1)對已有的客戶流失理論和技術進行總結;(2)對當當網客戶購買數據進行探索性分析；(3)基于數據挖掘技術分析當當網購客戶,構建網購客戶流失預測模型。各章節(jié)主要內容如下： 第一章是緒論部分。主要說明本文的研究背景、研究問題、研究內容以及研究的成果。 第二章是理論基礎部分。主要對客戶關系管理理論、客戶流失預測理論和客戶細分理論進行了概括性地論述。 第三章主要研究網購客戶流失預測模型的構建,重點描述了在客戶流失預測中應用廣泛的邏輯斯蒂回歸、決策樹、神經網絡三種技術的理論。 第四章對當當網客戶購買數據的實證分析。本文基于RFM理論提取出客戶的購買行為數據,并對客戶的行為做出了分析。 第五章是模型構建部分。本文主要是在現有的研究基礎上,針對B2C平臺的客戶提出了結合RFM理論和數據挖掘技術的網購客戶流失預測模型,并對模型的結果進行了評估。 第六章對論文的研究工作進行了總結,并提出了研究內容的局限性,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.
【學位授予單位】：西南財經大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F713.36
本文編號：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.
【學位授予單位】：西南財經大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F713.36
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