本文關(guān)鍵詞：基于信息熵視角的在線零售企業(yè)顧客細(xì)分研究　出處：《中國地質(zhì)大學(xué)(北京)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 顧客細(xì)分 RFM 信息熵 聚類分析 K-means

【摘要】：隨著電子商務(wù)的繁榮發(fā)展,以及“互聯(lián)網(wǎng)+”戰(zhàn)略的推進(jìn),B2C這種商業(yè)模式越來越被廣大企業(yè)和消費(fèi)者所重視。與傳統(tǒng)零售企業(yè)相比,在線零售企業(yè)的固定資產(chǎn)占比小、潛在顧客量大、產(chǎn)品服務(wù)不受時(shí)空限制等,使得顧客成為在線零售企業(yè)最重要的資產(chǎn)。CRM的有效實(shí)施為在線零售企業(yè)帶來了全新的顧客關(guān)系管理方式,而對(duì)顧客進(jìn)行科學(xué)細(xì)分是在線零售企業(yè)正確實(shí)施CRM的關(guān)鍵。并且,科學(xué)合理的顧客細(xì)分方法為在線零售企業(yè)實(shí)施差別化營銷、精準(zhǔn)營銷等提供了基礎(chǔ),可有效增強(qiáng)企業(yè)競(jìng)爭(zhēng)優(yōu)勢(shì)。因此,科學(xué)合理的顧客細(xì)分理論與方法研究具有重要的理論與現(xiàn)實(shí)意義。在線零售企業(yè)在顧客細(xì)分研究與實(shí)踐中,在細(xì)分指標(biāo)體系和模型兩方面存在不足。在對(duì)顧客細(xì)分研究文獻(xiàn)梳理的基礎(chǔ)上,認(rèn)為顧客細(xì)分的本質(zhì)上是基于顧客屬性和行為特征所包含的信息量展開的�；诖�,將信息熵引入到顧客細(xì)分理論與方法研究中,構(gòu)建基于RFM模型的顧客細(xì)分指標(biāo)體系和基于信息熵和聚類分析方法相結(jié)合的顧客細(xì)分模型,最后對(duì)某在線零售企業(yè)一年的交易數(shù)據(jù)進(jìn)行實(shí)證研究,基于顧客消費(fèi)行為特征實(shí)現(xiàn)了顧客細(xì)分的目標(biāo),為在線零售企業(yè)的顧客細(xì)分實(shí)踐提供理論與方法支持。具體而言,本文研究的主要成果包括:(1)將基于最大熵準(zhǔn)則的客觀賦權(quán)方法—熵權(quán)法應(yīng)用到顧客細(xì)分的RFM模型的指標(biāo)權(quán)重確定中,避免了指標(biāo)權(quán)重主觀設(shè)定的不足。(2)將叉熵用于對(duì)K-means聚類的改進(jìn)中,通過熵值計(jì)算,基于樣本之間的叉熵距離確定聚類數(shù)目和聚類中心,解決了經(jīng)典K-means聚類方法中需要預(yù)先確定聚類數(shù)目和聚類中心的不足。(3)根據(jù)在線零售企業(yè)的特點(diǎn),將基于叉熵的K-means聚類方法與改進(jìn)的RFM模型相結(jié)合,構(gòu)建了在線零售企業(yè)的顧客細(xì)分模型。并進(jìn)行實(shí)證研究,分別從消費(fèi)行為和顧客價(jià)值兩個(gè)角度實(shí)現(xiàn)顧客的細(xì)分,通過更為細(xì)致的細(xì)分結(jié)果指導(dǎo)企業(yè)的經(jīng)營決策,為企業(yè)實(shí)現(xiàn)精準(zhǔn)營銷提供決策支持。論文研究拓展了信息熵的應(yīng)用領(lǐng)域,豐富了顧客細(xì)分的理論與方法,為在線零售企業(yè)的顧客細(xì)分提供了新的工具與方法,對(duì)于其他類似企業(yè)的顧客細(xì)分具有一定借鑒意義。
[Abstract]:With the development of electronic commerce, and to promote Internet plus "strategy, B2C business model is increasingly valued by the majority of enterprises and consumers. Compared with the traditional retail enterprise, fixed assets online retail enterprises accounted for a small amount of potential customers, products and services is not limited by time and space, so that customers become effective implementation online retail enterprises the most important asset of.CRM brings a new way of customer relationship management for online retail business, the customer segmentation is the key scientific and correct implementation of CRM online retail enterprises. And the scientific and reasonable method of customer segmentation for online retail enterprises to implement differentiated marketing, provides a basis for precision marketing, can effectively enhance the enterprise competitive advantage. Therefore, it has important theoretical and practical significance to research the customer segmentation theory and method of scientific and reasonable. The online retail enterprises in customer segmentation Research and practice, the problems existing in the two aspects of subdivision index system and model. Based on the customer segmentation research on the literature review, the essence of customer segmentation is the amount of information based on customer attributes and behaviors features expanded. Based on this, the research to the customer segmentation theory and method of information entropy is introduced. The construction of RFM model of customer segmentation index system and information entropy and cluster analysis method based on customer segmentation model based on the empirical research on a year of online retail transaction data, customer consumption behavior realizes customer segmentation based on the target of providing theory and method support for the practice of online customer segmentation of retail enterprises specifically, the main results of this study include: (1) the RFM model based on maximum entropy criterion of objective weighting methods - entropy method is applied to the customer segmentation. To determine the index weight, avoid the shortcoming of subjective index weight setting. (2) the cross entropy for the improvement of K-means clustering, through entropy calculation, determine the cluster number and center distance between the samples based on the cross entropy, solves the predetermined lack of cluster center and the number of the classic K-means clustering method. (3) according to the characteristics of online retail enterprises, the RFM model and the improved K-means clustering method based on the cross entropy combination, constructs the model of customer segmentation of online retail enterprises. And empirical research, separately from the two aspects of consumer behavior and customer value realization of customer segmentation, through a more detailed breakdown of the results to guide business decisions, provide decision support for enterprise to achieve precision marketing. This paper expands the application field of information entropy, enriches the theory and method of customer segmentation, for online retail enterprises The customer segmentation of the industry provides new tools and methods, which can be used for reference for the customer segmentation of other similar enterprises.

【學(xué)位授予單位】：中國地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F274;F724.2

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