發(fā)布時(shí)間：2018-10-26 07:32

【摘要】：伴隨著三網(wǎng)融合，網(wǎng)絡(luò)技術(shù)日益成熟和普及所產(chǎn)生的大量互聯(lián)網(wǎng)信息數(shù)據(jù)逐漸被人們所關(guān)注，海量信息數(shù)據(jù)所蘊(yùn)含的巨大價(jià)值也受到越來越多的重視，，而數(shù)據(jù)挖掘作為解決海量信息數(shù)據(jù)價(jià)值轉(zhuǎn)換的一個(gè)重要手段，已成為當(dāng)前最熱門的研究領(lǐng)域。從巨大的、復(fù)雜的數(shù)據(jù)中獲取隱藏的信息的過程，就是數(shù)據(jù)挖掘，比如說對(duì)客戶進(jìn)行分類、聚類、識(shí)別欺詐行為、挖掘潛在顧客等，大多應(yīng)用在零售業(yè)、金融業(yè)、醫(yī)療機(jī)構(gòu)、政府機(jī)構(gòu)、公司財(cái)務(wù)等領(lǐng)域。但是，海量的信息數(shù)據(jù)在展示出巨大的商業(yè)活動(dòng)信息同時(shí)也帶來了一系列挑戰(zhàn)：一是海量信息數(shù)據(jù)大得無法想象，難以被有效的利用起來；二是難以辨別信息真?zhèn)�，而虛假信息的產(chǎn)生源于互聯(lián)網(wǎng)數(shù)據(jù)過于開放；三是由于信息表現(xiàn)形式不一致，導(dǎo)致難以對(duì)其進(jìn)行統(tǒng)一處理，正是這些挑戰(zhàn)推動(dòng)著數(shù)據(jù)挖掘技術(shù)的革新和完善。 呼叫中心在我國起源于上世紀(jì)80年代，并成為企業(yè)與顧客之間最直接的溝通渠道，市場(chǎng)經(jīng)濟(jì)迅速發(fā)展，呼叫中心產(chǎn)生的數(shù)據(jù)越來越繁雜和巨大。但通過調(diào)查發(fā)現(xiàn)，大量企業(yè)對(duì)呼叫中心業(yè)務(wù)數(shù)據(jù)僅僅是進(jìn)行簡(jiǎn)單的備份和存儲(chǔ)，忽視了這些數(shù)據(jù)中隱藏的客戶價(jià)值，并未對(duì)數(shù)據(jù)信息進(jìn)行有效的開發(fā)和利用。面對(duì)日益激烈的市場(chǎng)競(jìng)爭(zhēng)，企業(yè)如何利用這些數(shù)據(jù)進(jìn)一步挖掘高質(zhì)量目標(biāo)客戶、精細(xì)化企業(yè)客戶分類、制定精準(zhǔn)的營銷策略、提高核心競(jìng)爭(zhēng)力，從而為企業(yè)管理決策提供有效的支持，已成為各大企業(yè)的當(dāng)務(wù)之急。 本文在大量閱讀了國內(nèi)外文獻(xiàn)和進(jìn)行企業(yè)實(shí)例調(diào)查的基礎(chǔ)上，結(jié)合前人研究成果，進(jìn)一步完善了數(shù)據(jù)挖掘技術(shù)在呼叫中心領(lǐng)域的運(yùn)用。首先介紹了數(shù)據(jù)挖掘常用算法的原理，并就呼叫中心應(yīng)用中如何開發(fā)數(shù)據(jù)挖掘工具進(jìn)行描述和說明。然后在對(duì)國內(nèi)外數(shù)據(jù)挖掘應(yīng)用研究進(jìn)行歸納總結(jié)的基礎(chǔ)上，根據(jù)呼叫中心數(shù)據(jù)特點(diǎn)找到一種高效的K-means聚類算法，設(shè)計(jì)出符合呼叫中心業(yè)務(wù)數(shù)據(jù)特點(diǎn)的挖掘系統(tǒng)。最后，論文以移動(dòng)話費(fèi)營銷呼叫中心為例通過該系統(tǒng)對(duì)呼叫中心數(shù)據(jù)進(jìn)行了有效準(zhǔn)確的分析，以客戶的數(shù)據(jù)業(yè)務(wù)消費(fèi)信息為對(duì)象進(jìn)行數(shù)據(jù)挖掘，找出了可能的高價(jià)值客戶信息。
[Abstract]:With the integration of three networks, a large number of Internet information data, which is produced by the increasingly mature and popularization of network technology, has gradually been concerned by people, and the huge value of massive information data has also been paid more and more attention. As an important means to solve the value conversion of massive information data, data mining has become the most popular research field. The process of extracting hidden information from huge, complex data is data mining, such as categorizing customers, clustering, identifying fraud, mining potential customers, etc., mostly in retail, financial, medical, etc. Government agencies, corporate finance, etc. However, the huge amount of information data also brings a series of challenges: first, the massive information data is too big to be imagined, and it is difficult to be used effectively; Second, it is difficult to distinguish the authenticity of information, and the generation of false information from the Internet data is too open; The third is that it is difficult to deal with information in a uniform way because of the inconsistent forms of information expression. It is these challenges that promote the innovation and improvement of data mining technology. Call center originated in 1980s in our country and has become the most direct communication channel between enterprises and customers. With the rapid development of market economy, the data generated by call center is more and more complicated and huge. However, through the investigation, it is found that a large number of enterprises only backup and store the call center business data simply, ignoring the customer value hidden in these data, and not effectively developing and utilizing the data information. In the face of increasingly fierce market competition, how to use these data to further tap high quality target customers, refine the classification of enterprise customers, formulate accurate marketing strategies, improve the core competitiveness, In order to provide effective support for enterprise management decisions, has become an urgent matter for major enterprises. On the basis of reading a large number of domestic and foreign literature and carrying out enterprise case investigation, this paper further consummates the application of data mining technology in the field of call center, combined with the previous research results. This paper first introduces the principle of common algorithms of data mining, and describes how to develop data mining tools in the application of call center. Then, on the basis of summarizing the domestic and foreign data mining application research, we find an efficient K-means clustering algorithm according to the characteristics of call center data, and design a mining system which accords with the characteristics of call center business data. Finally, the paper takes the mobile call center as an example to analyze the data of the call center effectively and accurately, and takes the customer's data consumption information as the object to mine the data, and finds out the possible high-value customer information.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP311.13

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