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(dāng)前,電信業(yè)的競(jìng)爭(zhēng)變得越來(lái)越激烈,而且隨著智能手機(jī)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(duì)電信業(yè)的傳統(tǒng)業(yè)務(wù)如短信業(yè)務(wù)和語(yǔ)音業(yè)務(wù)帶來(lái)了一定的影響,因此,進(jìn)行客戶細(xì)分,識(shí)別有價(jià)值的潛在客戶變得尤為重要。本文拓寬了以單一指標(biāo)如客戶價(jià)值來(lái)進(jìn)行客戶細(xì)分的指標(biāo)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(lái)的變化,最終確立了一套貼合實(shí)際的指標(biāo)體系。在確立客戶細(xì)分指標(biāo)體系時(shí),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(duì)其進(jìn)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(xì)分指標(biāo)體系。對(duì)于非上網(wǎng)客戶的指標(biāo)體系主要包括通話、消費(fèi)等客戶的傳統(tǒng)特征,而對(duì)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(duì)APP的瀏覽行為納入指標(biāo)體系。此外,本文從多個(gè)角度比較全面的對(duì)電信業(yè)的客戶特征進(jìn)行了分析。在分析過(guò)程中,將上網(wǎng)客戶和非上網(wǎng)客戶分開,分別進(jìn)行特征分析。不僅包括傳統(tǒng)的統(tǒng)計(jì)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(gè)方面,對(duì)上網(wǎng)客戶的APP使用行為進(jìn)行了分析,并將結(jié)果進(jìn)行了可視化的展示。在模型改進(jìn)方面,為了克服Kohonen SOM算法和K-Means算法的缺點(diǎn),本文將KohonenSOM算法和K-Means算法進(jìn)行了結(jié)合,創(chuàng)建了 Kohonen SOM+K-Means聚類分析模型。Kohonen SOM首先進(jìn)行一次初始聚類,確定K值的個(gè)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(xì)分成了"普通人"、"社交王"、"閱讀迷"、"生活控'"和"購(gòu)物狂" 5類,將非上網(wǎng)客戶細(xì)分成了 6類,分別是:"不活躍客戶群"、"長(zhǎng)途夜間活躍客戶群"、"低端主動(dòng)客戶群"、"高語(yǔ)音親情網(wǎng)內(nèi)客戶群"、"較高消費(fèi)本地客戶群"和"高消費(fèi)漫游客戶群'"。根據(jù)不同客戶群的客戶特征,提出了針對(duì)該電信運(yùn)營(yíng)公司精準(zhǔn)營(yíng)銷的策略及建議。本文還基于聚類結(jié)果篩選出的高價(jià)值客戶對(duì)客戶的APP使用利用關(guān)聯(lián)挖掘進(jìn)行了拓展研究。通過(guò)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(duì)客戶的APP使用進(jìn)行了關(guān)聯(lián)推薦。文章最后,對(duì)全文的主要工作進(jìn)行了總結(jié),并結(jié)合本文存在的不足,對(duì)后續(xù)的研究進(jìn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F626;F274

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本文編號(hào)：2020498