發(fā)布時間：2018-06-15 03:51

  本文選題：電信業(yè) + 特征分析��； 參考：《浙江工商大學(xué)》2017年碩士論文

【摘要】：當前,電信業(yè)的競爭變得越來越激烈,而且隨著智能手機和互聯(lián)網(wǎng)應(yīng)用的普及,一些網(wǎng)絡(luò)聊天工具像微信、QQ這類通訊軟件對電信業(yè)的傳統(tǒng)業(yè)務(wù)如短信業(yè)務(wù)和語音業(yè)務(wù)帶來了一定的影響,因此,進行客戶細分,識別有價值的潛在客戶變得尤為重要。本文拓寬了以單一指標如客戶價值來進行客戶細分的指標體系,結(jié)合互聯(lián)網(wǎng)的深入應(yīng)用給企業(yè)和客戶帶來的變化,最終確立了一套貼合實際的指標體系。在確立客戶細分指標體系時,本文將數(shù)據(jù)挖掘的思想融入其中。首先基于可獲得的大量原始數(shù)據(jù),對其進行數(shù)據(jù)預(yù)處理,再結(jié)合電信業(yè)的行業(yè)特征確立了上網(wǎng)客戶和非上網(wǎng)客戶的細分指標體系。對于非上網(wǎng)客戶的指標體系主要包括通話、消費等客戶的傳統(tǒng)特征,而對于上網(wǎng)客戶,則主要將客戶的上網(wǎng)行為如流量的使用、對APP的瀏覽行為納入指標體系。此外,本文從多個角度比較全面的對電信業(yè)的客戶特征進行了分析。在分析過程中,將上網(wǎng)客戶和非上網(wǎng)客戶分開,分別進行特征分析。不僅包括傳統(tǒng)的統(tǒng)計特征,如年齡、性別、套餐、終端、通話行為等,還創(chuàng)新性的從多個方面,對上網(wǎng)客戶的APP使用行為進行了分析,并將結(jié)果進行了可視化的展示。在模型改進方面,為了克服Kohonen SOM算法和K-Means算法的缺點,本文將KohonenSOM算法和K-Means算法進行了結(jié)合,創(chuàng)建了 Kohonen SOM+K-Means聚類分析模型。Kohonen SOM首先進行一次初始聚類,確定K值的個數(shù),將其結(jié)果作為K-Means聚類的初始輸入,最終將上網(wǎng)客戶細分成了"普通人"、"社交王"、"閱讀迷"、"生活控'"和"購物狂" 5類,將非上網(wǎng)客戶細分成了 6類,分別是:"不活躍客戶群"、"長途夜間活躍客戶群"、"低端主動客戶群"、"高語音親情網(wǎng)內(nèi)客戶群"、"較高消費本地客戶群"和"高消費漫游客戶群'"。根據(jù)不同客戶群的客戶特征,提出了針對該電信運營公司精準營銷的策略及建議。本文還基于聚類結(jié)果篩選出的高價值客戶對客戶的APP使用利用關(guān)聯(lián)挖掘進行了拓展研究。通過關(guān)聯(lián)挖掘并結(jié)合電信業(yè)業(yè)務(wù)規(guī)則篩選了 101條關(guān)聯(lián)規(guī)則,對客戶的APP使用進行了關(guān)聯(lián)推薦。文章最后,對全文的主要工作進行了總結(jié),并結(jié)合本文存在的不足,對后續(xù)的研究進行了展望。
[Abstract]:At present, the competition in the telecommunications industry is becoming more and more fierce, and with the popularity of smart phones and Internet applications, Some network chat tools such as WeChat QQ and other communication software have a certain impact on the traditional business of telecommunications such as SMS and voice services so it is particularly important to segment customers and identify potential customers of value. This paper broadens the index system of customer segmentation with a single index such as customer value, and finally establishes a set of index system suitable to the actual situation by combining the changes brought to enterprises and customers by the deep application of the Internet. In establishing the index system of customer segmentation, this paper integrates the idea of data mining into it. Firstly, based on a large number of raw data available, the paper preprocesses the data, and then establishes the subdivision index system of Internet customers and non-online customers according to the industry characteristics of telecommunications industry. For non-online customers, the index system mainly includes the traditional characteristics of customers, such as telephone, consumption and so on, while for online customers, it mainly includes the usage of customers' online behavior such as traffic, and the browsing behavior of app into the index system. In addition, this paper analyzes the customer characteristics of telecom industry from a variety of angles. In the process of analysis, the online customer and the non-online customer are separated, and the characteristics are analyzed separately. It not only includes the traditional statistical characteristics, such as age, gender, package, terminal, telephone behavior, etc., but also analyzes the application behavior of Internet customers from many aspects, and visualizes the results. In order to overcome the shortcomings of Kohonen SOM algorithm and K-Means algorithm, this paper combines Kohonen SOM algorithm with K-Means algorithm, and establishes Kohonen SOM K-Means clustering analysis model. Kohonen SOM clustering model. Using the results as the initial input of K-Means clustering, the online customers were subdivided into five categories: "ordinary person", "Social King", "Reading fan", "Life Control" and "shopaholic". They are: "inactive customer group", "long distance nocturnal active customer group", "low end active customer group", "high voice affinity network customer group", "higher consumption local customer group" and "high consumption roaming customer group". According to the customer characteristics of different customer groups, the strategies and suggestions for precision marketing of the telecom operation company are put forward. In addition, based on the clustering results, the application mining of high value customers is extended. Through association mining and combining with telecom business rules, 101 association rules are screened, and the application of application is recommended. Finally, the main work of this paper is summarized, and the future research is prospected.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F626;F274

【參考文獻】

相關(guān)期刊論文 前10條

1 魏瑾;;基于客戶細分的電信聚類市場營銷策略研究[J];中國市場;2016年31期

2 連漪;楊碩;;基于忠誠度的客戶價值細分模型構(gòu)建及其應(yīng)用[J];商業(yè)經(jīng)濟研究;2016年14期

3 林勤;薛云;楊柏高;;基于變異系數(shù)的雙聚類算法及其在電信客戶細分的應(yīng)用研究[J];計算機應(yīng)用與軟件;2016年02期

4 王虹;孫紅;;基于混合聚類算法的客戶細分策略研究[J];電子科技;2016年01期

5 胡曉雪;趙嵩正;吳楠;;面向分類屬性數(shù)據(jù)的一種改進相異性度量及其在客戶細分中的應(yīng)用[J];計算機應(yīng)用研究;2016年02期

6 趙萌;齊佳音;;基于購買行為RFM及評論行為RFMP模型的客戶終身價值研究[J];統(tǒng)計與信息論壇;2014年09期

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

8 鄧曉懿;金淳;j口良之;韓慶平;;移動商務(wù)中面向客戶細分的KSP混合聚類算法[J];管理科學(xué);2011年04期

9 瞿小寧;;K均值聚類算法在商業(yè)銀行客戶分類中的應(yīng)用[J];計算機仿真;2011年06期

10 蔣國瑞;劉沛;黃梯云;;一種基于AHP方法的客戶價值細分研究[J];計算機工程與應(yīng)用;2007年08期

相關(guān)碩士學(xué)位論文 前5條

1 盧海明;電力客戶細分及增值服務(wù)系統(tǒng)研究[D];廣東工業(yè)大學(xué);2016年

2 張平;電子商務(wù)環(huán)境下客戶分類應(yīng)用研究[D];蘭州交通大學(xué);2012年

3 朱幸燕;基于消費行為認知的電信企業(yè)客戶細分方法研究[D];華南理工大學(xué);2011年

4 計海斌;基于改進RFM模型的應(yīng)用研究[D];吉林大學(xué);2010年

5 鄧曉梅;基于數(shù)據(jù)挖掘的電信客戶細分模型研究[D];大連理工大學(xué);2006年

，

本文編號：2020498