本文選題：大數(shù)據(jù) + B2C　； 參考：《安徽理工大學(xué)》2017年碩士論文

【摘要】：互聯(lián)網(wǎng)的迅速發(fā)展使得網(wǎng)絡(luò)購物消費(fèi)快速增長(zhǎng),近年來以京東、天貓等為代表的B2C購物模式發(fā)展迅速,網(wǎng)站業(yè)務(wù)量和信息量迅速增加給企業(yè)發(fā)展帶來挑戰(zhàn)。如何從逐漸增加的非結(jié)構(gòu)化數(shù)據(jù)中提煉有效信息?如何從海量消費(fèi)數(shù)據(jù)挖掘客戶的真實(shí)需求從而提供精準(zhǔn)的個(gè)性化服務(wù),最大程度改進(jìn)客戶的購物體驗(yàn)?這些問題成為目前研究的熱點(diǎn)和難點(diǎn)。因此,運(yùn)用數(shù)據(jù)驅(qū)動(dòng)模式挖掘客戶的消費(fèi)偏好,是B2C購物網(wǎng)站精準(zhǔn)營(yíng)銷的重要保障。本文以在線評(píng)論、消費(fèi)者行為和B2C網(wǎng)站客戶消費(fèi)偏好為理論基礎(chǔ),以天貓B2C服裝類客戶消費(fèi)作為研究對(duì)象,從消費(fèi)者、平臺(tái)及商家方面分析消費(fèi)偏好影響因素,對(duì)所選定網(wǎng)售商品進(jìn)行歸類和篩選,確定了 7種服裝商品,運(yùn)用爬蟲軟件抓取2016年9-11月的在線評(píng)論信息。通過數(shù)據(jù)整理、關(guān)鍵詞提取與統(tǒng)計(jì)分析等手段,提取客戶評(píng)論信息的34個(gè)高頻關(guān)注點(diǎn),確定12個(gè)特征因素變量。運(yùn)用李克特量表的5級(jí)評(píng)分標(biāo)準(zhǔn)將評(píng)論信息轉(zhuǎn)化為結(jié)構(gòu)化數(shù)據(jù)。運(yùn)用Clementine12.0軟件將12個(gè)商品特征因素變量導(dǎo)入,建立各個(gè)因素之間的貝葉斯網(wǎng)絡(luò)模型結(jié)構(gòu)。計(jì)算各節(jié)點(diǎn)在其父節(jié)點(diǎn)條件下的條件概率分布,各特征因素重要度,建立logistic回歸模型,對(duì)比分析貝葉斯網(wǎng)絡(luò)模型的準(zhǔn)確性,對(duì)模型預(yù)測(cè)結(jié)果做出準(zhǔn)確評(píng)估。結(jié)果表明,所篩選7個(gè)商品的舒適程度、面料、質(zhì)量、顏色、合適程度、價(jià)格等,都是客戶高頻關(guān)注詞;貝葉斯網(wǎng)絡(luò)模型中因素節(jié)點(diǎn)間具有較強(qiáng)的相關(guān)性;節(jié)點(diǎn)的條件概率分布情況相似,客戶給予優(yōu)、良、中評(píng)價(jià)的概率較高;男裝和女裝的特征因素重要性程度不同,女裝較關(guān)注物流、相符程度、手感、正品、合適程度等因素,男裝則關(guān)注面料做工、色彩、物流、手感、美觀程度、舒適程度等因素。B2C網(wǎng)站可根據(jù)客戶消費(fèi)關(guān)注高頻詞,貝葉斯網(wǎng)絡(luò)因素關(guān)聯(lián),各種因素所得評(píng)價(jià)分?jǐn)?shù)的概率以及重要度分析消費(fèi)偏好,制定精準(zhǔn)營(yíng)銷策略。
[Abstract]:With the rapid development of Internet, the consumption of online shopping increases rapidly. In recent years, the B2C shopping model represented by JingDong and Tmall has developed rapidly, and the volume of business and information on the website is increasing rapidly, which brings challenges to the development of enterprises. How do you extract valid information from the increasing amount of unstructured data? How to mine the real needs of customers from mass consumption data to provide accurate personalized services to maximize the improvement of customer shopping experience? These problems have become the hot and difficult point of current research. Therefore, it is an important guarantee for accurate marketing of B2C shopping website to use data driven mode to mine customer's consumption preference. Based on online review, consumer behavior and consumer preference of B2C website, this paper takes Tmall B2C clothing consumer as the research object, analyzes the influencing factors of consumer preference from consumers, platforms and merchants. This paper classifies and selects the selected online items, determines 7 kinds of clothing products, and uses crawler software to capture the online comment information of September-November 2016. By means of data collation, keyword extraction and statistical analysis, 34 high frequency concerns of customer comment information were extracted, and 12 feature factor variables were determined. The comment information was converted into structured data using the 5-level rating scale of the Richter scale. Using Clementine12.0 software, 12 commodity feature variables are imported and the Bayesian network model structure between each factor is established. The conditional probability distribution of each node under the condition of its parent node and the importance of each characteristic factor are calculated. The logistic regression model is established and the accuracy of the Bayesian network model is compared and the prediction results of the model are evaluated accurately. The results show that the comfort, fabric, quality, color, suitability and price of the seven products are the high-frequency concern words of the customer, and there is a strong correlation among the factors nodes in the Bayesian network model. The conditional probability distribution of the node is similar, the probability of customer giving excellent, good and medium evaluation is higher; the importance of characteristic factors of men's wear and women's wear is different, and women's wear is more concerned with the factors such as logistics, match degree, hand feeling, genuine product, suitable degree and so on. Men's clothing is concerned about fabric workmanship, color, logistics, feel, beauty, comfort and other factors. B2C website can be based on customer consumption concerns about high-frequency words, Bayesian network factors related, The probability and importance of evaluation scores are analyzed and precise marketing strategies are formulated.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;F713.55

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本文編號(hào)：1853714