本文選題：電商企業(yè) + 客戶價值�。� 參考：《北京交通大學》2017年碩士論文

【摘要】：在網(wǎng)絡經濟迅猛發(fā)展、市場競爭日益加劇的背景下,客戶在企業(yè)中所扮演的角色和所處的地位都發(fā)生了翻天覆地的變化,客戶對于企業(yè)未來的生存發(fā)展都起著決定性的作用。企業(yè)成功的關鍵是確定企業(yè)的客戶,并成功獲取客戶,對客戶進行合理的客戶分類是企業(yè)與客戶之間建立良好的關系、并和諧發(fā)展的前提條件,也是非常重要的條件。電商企業(yè)近年來發(fā)展迅速,越來越多的電商企業(yè)破土而出,進入人們的視野,A電商企業(yè)面臨的市場競爭越來越大。A電商企業(yè)面臨著以產品為中心向客戶數(shù)據(jù)為中心的模式轉變,對客戶進行合理的細分成為了這一巨大轉變的前提和基礎。在擁有大量客戶行為數(shù)據(jù)的情況下,如何對客戶進行合理的細分,并為不同類型的客戶提供符合其特點的服務,從而更好地維系與客戶之間的關系,并為企業(yè)帶來更多的利潤,已成為了 A電商企業(yè)眼下急需解決的問題。本文通過分析當前A電商企業(yè)客戶關系管理中存在的問題,發(fā)現(xiàn)對該客戶關系管理系統(tǒng)優(yōu)化的必要性,并進一步選取合適的指標,進行對A電商企業(yè)客戶生命周期價值模型的構建,在分析K-means聚類分析算法不足的基礎上對其改進,利用改進后的K-means算法對A電商企業(yè)的客戶進行客戶細分,然后對當前客戶關系管理系統(tǒng)在客戶細分方面存在的缺陷進行優(yōu)化。主要工作如下:構建客戶價值的量化模型。通過選取更加契合A電商企業(yè)的指標,以客戶關系管理理論為基礎,構建A電商企業(yè)的客戶價值模型,為后續(xù)的研究奠定基礎。基于客戶價值模型進行客戶細分。剖析聚類分析經典算法K-means算法,闡述其基本的思想和流程,從而分析其存在的不足之處,提出改進算法,并進一步對A電商企業(yè)的客戶進行細分�？蛻絷P系管理系統(tǒng)的優(yōu)化。利用細分后的結果,對客戶關系管理系統(tǒng)在客戶細分方面存在的不足進行優(yōu)化,從而提高企業(yè)客戶滿意度和留存率,實現(xiàn)最佳的客戶關系管理。
[Abstract]:Under the background of rapid development of network economy and increasing market competition, the role and position of customers in enterprises have changed dramatically, and customers play a decisive role in the future survival and development of enterprises. The key to the success of an enterprise is to determine the customer of the enterprise and obtain the customer successfully. The reasonable classification of the customer is the prerequisite for the establishment of a good relationship and the harmonious development between the enterprise and the customer, and is also a very important condition. With the rapid development of e-commerce enterprises in recent years, more and more e-commerce enterprises have stepped out of the ground and entered the field of vision. The market competition faced by e-commerce enterprises is increasing. A e-commerce enterprises are facing a transformation from product-centered to customer-data-centric. A reasonable breakdown of the customer has become the premise and basis of this huge change. In the case of having a large number of customer behavior data, how to segment the customer reasonably and provide different types of customer with the service according to their characteristics, so as to better maintain the relationship with the customer, and bring more profits for the enterprise. It has become a problem urgently needed to be solved at present by A e-commerce enterprises. Based on the analysis of the problems existing in customer relationship management (CRM) in E-business enterprises, this paper finds out the necessity of optimizing the CRM system, and further selects appropriate indicators. On the basis of analyzing the insufficiency of K-means clustering analysis algorithm, the customer life cycle value model of A ecommerce enterprise is constructed, and the improved K-means algorithm is used to segment the customers of A e-commerce enterprise. Then the defects of current customer relationship management system in customer segmentation are optimized. The main work is as follows: build the quantitative model of customer value. Based on the theory of customer relationship management, the customer value model of E-Commerce A enterprise is constructed by selecting more suitable indexes for E-business enterprise, which will lay a foundation for further research. Customer segmentation based on customer value model. This paper analyzes the classical clustering analysis algorithm K-means algorithm, expounds its basic idea and flow, analyzes its shortcomings, proposes an improved algorithm, and further subdivides the customers of A e-commerce enterprise. Customer relationship management system optimization. By using the result of subdivision, the shortcomings of customer relationship management system in customer segmentation are optimized, so as to improve customer satisfaction and retention rate, and realize the best customer relationship management.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F274;F724.6

【參考文獻】

相關期刊論文 前10條

1 蔡尚霖;;中國電子商務與傳統(tǒng)商務的碰撞[J];企業(yè)改革與管理;2015年20期

2 李洪霞;;財產保險公司通過客戶細分提供更好的服務[J];赤子(上中旬);2015年09期

3 孫卓;;k-均值聚類算法及其應用[J];農業(yè)網(wǎng)絡信息;2013年07期

4 蔡淑琴;馬玉濤;王瑞;;在線口碑傳播的意見領袖識別方法研究[J];中國管理科學;2013年02期

5 徐翔斌;王佳強;涂歡;穆明;;基于改進RFM模型的電子商務客戶細分[J];計算機應用;2012年05期

6 仝雪姣;孟凡榮;王志曉;;對k-means初始聚類中心的優(yōu)化[J];計算機工程與設計;2011年08期

7 孫可;劉杰;王學穎;;K均值聚類算法初始質心選擇的改進[J];沈陽師范大學學報(自然科學版);2009年04期

8 汪中;劉貴全;陳恩紅;;一種優(yōu)化初始中心點的K-means算法[J];模式識別與人工智能;2009年02期

9 王玲;薄列峰;焦李成;;密度敏感的譜聚類[J];電子學報;2007年08期

10 王莉,張朋柱;網(wǎng)絡環(huán)境下產品開發(fā)中企業(yè)和客戶交互的形式和內容[J];上海管理科學;2004年05期

相關碩士學位論文 前7條

1 崔丹丹;K-Means聚類算法的研究與改進[D];安徽大學;2012年

2 喬中杰;B2C電子商務網(wǎng)站顧客價值及其應用研究[D];北京化工大學;2011年

3 計海斌;基于改進RFM模型的應用研究[D];吉林大學;2010年

4 梅立群;語文周報社客戶管理策略研究[D];河北科技大學;2010年

5 孫江坤;移動行業(yè)集團客戶價值評估模型的構建及應用[D];同濟大學;2008年

6 周涓;基于最大最小距離法的多中心聚類算法研究[D];重慶大學;2006年

7 許葉軍;基于BP神經網(wǎng)絡的交互式賦權法及應用研究[D];東南大學;2005年

，

本文編號：1854291