本文選題：數(shù)據(jù)倉(cāng)庫(kù) + 客戶(hù)畫(huà)像　； 參考：《中國(guó)科學(xué)院大學(xué)(工程管理與信息技術(shù)學(xué)院)》2016年碩士論文

【摘要】：隨著近幾年互聯(lián)網(wǎng)的迅猛發(fā)展,大量企業(yè)進(jìn)入到電子商務(wù)領(lǐng)域,借助電商平臺(tái)來(lái)進(jìn)行產(chǎn)品的營(yíng)銷(xiāo)和推廣。信息技術(shù)借助互聯(lián)網(wǎng)快速發(fā)展,互聯(lián)網(wǎng)金融模式逐漸興起。大數(shù)據(jù)時(shí)代的到來(lái)對(duì)于給金融機(jī)構(gòu)既是挑戰(zhàn),也是機(jī)遇�；ヂ�(lián)網(wǎng)金融不是簡(jiǎn)單字面上的通過(guò)互聯(lián)網(wǎng)來(lái)提供金融服務(wù),這只是表面上的形式而已,背后還需要大量數(shù)據(jù)的積累和強(qiáng)大的數(shù)據(jù)處理能力,也是互聯(lián)網(wǎng)金融的兩個(gè)關(guān)鍵基礎(chǔ)因素：大數(shù)據(jù)和云計(jì)算�；ヂ�(lián)網(wǎng)金融依托于大數(shù)據(jù)和云計(jì)算為客戶(hù)提供一系列的互聯(lián)網(wǎng)金融服務(wù)。而本文研究的基于互聯(lián)網(wǎng)金融屬性的券商電商平臺(tái)是結(jié)合了產(chǎn)品銷(xiāo)售、咨詢(xún)服務(wù)、投資顧問(wèn)簽約、證券交易以及依托于大數(shù)據(jù)和云計(jì)算的綜合型平臺(tái)。目前而言,還沒(méi)有具體針對(duì)券商電商客戶(hù)精確化分類(lèi)的金融平臺(tái),客戶(hù)畫(huà)像還只是用于簡(jiǎn)單的描述用戶(hù)信息,本文將根據(jù)用戶(hù)的基礎(chǔ)信息、資產(chǎn)信息、交易記錄、平臺(tái)活動(dòng)軌跡等行為數(shù)據(jù)通過(guò)云計(jì)算來(lái)進(jìn)行數(shù)據(jù)建模,在客戶(hù)畫(huà)像的基礎(chǔ)上對(duì)用戶(hù)進(jìn)行聚類(lèi)分析建立數(shù)據(jù)分類(lèi)模型,將客戶(hù)進(jìn)行分層,然后針對(duì)各層次的客戶(hù)進(jìn)行制定個(gè)性化營(yíng)銷(xiāo)方案,從而更有針對(duì)性的進(jìn)行產(chǎn)品的營(yíng)銷(xiāo)和推廣。客戶(hù)分層分類(lèi)通常使用聚類(lèi)算法來(lái)實(shí)現(xiàn),而K-means算法是最為常用的數(shù)據(jù)挖掘算法之一,通過(guò)對(duì)K-means算法的深入分析,作者發(fā)現(xiàn)選擇適當(dāng)?shù)某跏假|(zhì)心是K-means算法執(zhí)行過(guò)程的關(guān)鍵,一般情況下會(huì)采用隨機(jī)選取質(zhì)心來(lái)解決人為干預(yù)的因素,但是這樣會(huì)導(dǎo)致不同的運(yùn)行產(chǎn)生不同的總誤差平方和(Sum of the Squared Error,簡(jiǎn)稱(chēng)SSE),最終影響結(jié)果的準(zhǔn)確性和穩(wěn)定性。為了克服隨機(jī)選取質(zhì)心的缺陷,美國(guó)學(xué)者Pang-Ning Tan提出了二分K-means算法,這種算法的基本思想是將所有點(diǎn)的集合分裂成兩個(gè)簇,從這兩個(gè)簇中根據(jù)條件篩出選取一個(gè)繼續(xù)分裂,如此下去產(chǎn)生K個(gè)簇。根據(jù)實(shí)際實(shí)驗(yàn)結(jié)果得出結(jié)論二分K-means算法受質(zhì)心影響較小,且效率和準(zhǔn)確性比K-means算法要高很多。本文則主要根據(jù)二分K-means算法進(jìn)行分析研究和應(yīng)用,通過(guò)此算法將券商客戶(hù)分類(lèi)以后,通過(guò)不同層次的客戶(hù)匹配不同風(fēng)險(xiǎn)等級(jí)的產(chǎn)品,從而在策略上達(dá)到區(qū)分客戶(hù)精準(zhǔn)營(yíng)銷(xiāo)的目的。本文完成的主要工作包括：(1)建立統(tǒng)一的數(shù)據(jù)中心,將客戶(hù)的各項(xiàng)數(shù)據(jù)進(jìn)行統(tǒng)一抽取、分類(lèi),并通過(guò)系列方法來(lái)篩選整合數(shù)據(jù),使客戶(hù)數(shù)據(jù)達(dá)到實(shí)驗(yàn)要求；(2)建立客戶(hù)畫(huà)像系統(tǒng),建立統(tǒng)一的客戶(hù)畫(huà)像指標(biāo)體系,通過(guò)系列指標(biāo)來(lái)篩選客戶(hù)作為客戶(hù)聚類(lèi)分析的基礎(chǔ)；(3)通過(guò)優(yōu)化的聚類(lèi)分析方法對(duì)客戶(hù)數(shù)據(jù)進(jìn)行分類(lèi),將客戶(hù)分層,制定個(gè)性化營(yíng)銷(xiāo)方案,提高客戶(hù)轉(zhuǎn)化率。基于對(duì)于目前互聯(lián)網(wǎng)金融電商平臺(tái)對(duì)客戶(hù)研究重要性的認(rèn)知,本研究在系統(tǒng)綜述經(jīng)典文獻(xiàn)研究的基礎(chǔ)上,通過(guò)云計(jì)算平臺(tái)將客戶(hù)的大數(shù)據(jù)信息通過(guò)數(shù)據(jù)建模,在客戶(hù)畫(huà)像的基礎(chǔ)上將用戶(hù)進(jìn)行分類(lèi)算法分類(lèi),精確定位用戶(hù),并通過(guò)實(shí)際的個(gè)性化營(yíng)銷(xiāo)和推廣來(lái)驗(yàn)證和修正數(shù)據(jù)模型,提高券商客戶(hù)轉(zhuǎn)化率,并達(dá)到了預(yù)期的效果。
[Abstract]:With the rapid development of the Internet in recent years, a large number of enterprises have entered the field of electronic commerce, with the help of the e-commerce platform to carry out the marketing and promotion of products. Information technology has developed rapidly with the help of the Internet, and the Internet financial model is rising gradually. The advent of the era of big data is not only a challenge but also an opportunity for the financial machinery. Simply literally, providing financial services through the Internet, which is just a surface form, requires a lot of data accumulation and powerful data processing capabilities. It is also the two key basic factor for Internet Finance: large data and cloud computing. Internet Finance provides a series of customers with large data and cloud computing. In this paper, the e-commerce platform based on the Internet financial attributes is a combination of product sales, consulting services, investment consulting, securities trading and integrated platform based on large data and cloud computing. At present, there are no specific financial platforms for the precise classification of securities business customers. Customer portrait is also used to simply describe user information. This article will model the data according to the user's basic information, asset information, transaction record, platform activity track and other behavioral data through cloud computing. Personalized marketing programs are made to customers at all levels, which are more targeted to the marketing and promotion of products. Customer stratification classification is usually implemented using clustering algorithms. The K-means algorithm is one of the most commonly used data mining algorithms. By deep analysis of the K-means algorithm, the author finds that the appropriate initial centroid is selected. It is the key to the execution of the K-means algorithm. In general, a random selection of centroids will be used to solve the factors of human intervention. However, this will lead to different running of the total error square sum (Sum of the Squared Error, for short, SSE), and ultimately affect the accuracy and stability of the result. In order to overcome the defect of random selection of the centroid The American scholar Pang-Ning Tan proposed a two point K-means algorithm. The basic idea of this algorithm is to divide the set of all points into two clusters, and select one to continue splitting from the two clusters according to the conditions, and then produce K clusters. According to the actual experimental results, the conclusion is that the effect of the centroid is smaller and the efficiency is less effective. And the accuracy is much higher than the K-means algorithm. This paper is mainly based on the analysis and application of the two point K-means algorithm. After classifying the broker customers, this algorithm can match the products of different risk levels through different levels of customers, so as to achieve the purpose of distinguishing the customers' accurate marketing in the strategy. The main work done in this paper is the main work of this paper. Including: (1) to establish a unified data center to unify and classify the customer's data, and to select the integrated data through a series of methods to make the customer data meet the requirements of the experiment; (2) establish a customer portrait system, establish a unified customer portrait index system, and screen customers as customer clustering analysis through a series of indicators. 3. (3) classifying customer data by optimizing clustering analysis method, delamination of customers, formulate personalized marketing schemes and improve customer conversion rate. Based on the understanding of the importance of Internet financial e-commerce platform to customer research, this research is based on the system overview of classic literature and through the cloud computing platform The customer's large data information is modeled by the data, the classification algorithm is classified on the basis of customer portrait, the user is accurately positioned, and the data model is verified and modified through the actual personalized marketing and promotion to improve the conversion rate of the customers and achieve the expected effect.
【學(xué)位授予單位】：中國(guó)科學(xué)院大學(xué)(工程管理與信息技術(shù)學(xué)院)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP311.13

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本文編號(hào)：1993457