本文關(guān)鍵詞： 電商促銷活動(dòng) 活動(dòng)銷量預(yù)測(cè) 活動(dòng)效應(yīng) 支持向量回歸機(jī) 關(guān)聯(lián)規(guī)則　出處：《東華大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來(lái),隨著互聯(lián)網(wǎng)及電子商務(wù)的快速發(fā)展,商家彼此之間的競(jìng)爭(zhēng)越來(lái)越激烈,電商平臺(tái)和商家都會(huì)采用各種各樣的銷售運(yùn)營(yíng)手段來(lái)?yè)屨际袌?chǎng)。為了促進(jìn)銷售,平臺(tái)會(huì)提供和舉辦的各類型促銷活動(dòng)供商家進(jìn)行參與,同時(shí)隨著信息化的飛速發(fā)展,商家的數(shù)據(jù)積累也已達(dá)到一定的規(guī)模。在這樣的時(shí)代背景下,本文致力于通過歷史數(shù)據(jù),基于數(shù)據(jù)挖掘和數(shù)據(jù)分析的角度對(duì)電商促銷活動(dòng)效應(yīng)和活動(dòng)銷量進(jìn)行分析和預(yù)測(cè),通過數(shù)據(jù)挖掘和分析,從而提高企業(yè)管理經(jīng)營(yíng)決策的科學(xué)化程度和智能化程度,使本文研究工作在具有一定理論意義的同時(shí)又具有重要的現(xiàn)實(shí)意義。首先,本文對(duì)電商促銷活動(dòng)短期效應(yīng)進(jìn)行分析,主要分析商家較為關(guān)心的銷售和客流兩方面,并且對(duì)銷售和客流兩方面在活動(dòng)期間和活動(dòng)前后期的效應(yīng)展開分析�；谀程熵埰炫灥甑臍v史數(shù)據(jù),通過T檢驗(yàn)和回歸等統(tǒng)計(jì)方法分析得出,在活動(dòng)銷售效應(yīng)方面,活動(dòng)期間商品的銷量會(huì)顯著上升,同時(shí)隨著活動(dòng)時(shí)間的進(jìn)行,其刺激銷售的能力在逐漸減弱;而活動(dòng)前后期商品的銷量會(huì)有一定的回落。在客流效應(yīng)方面,活動(dòng)期間客流量會(huì)顯著上升,而活動(dòng)前后期客流量不會(huì)有明顯的變化,銷量的降低主要是因?yàn)檫@段時(shí)間轉(zhuǎn)換率較低。其次,結(jié)合上述分析,針對(duì)企業(yè)是否要參加某次活動(dòng)的管理經(jīng)營(yíng)決策問題,基于整體利潤(rùn)的角度進(jìn)行決策建模分析,需綜合考慮參加活動(dòng)的商品和未參加活動(dòng)的商品在活動(dòng)期間的收益和活動(dòng)前后期的損失,以及平臺(tái)收取的傭金費(fèi)用。通過對(duì)各決策變量的分析發(fā)現(xiàn),商品的日常期間和活動(dòng)前后期的銷量變化通常會(huì)相對(duì)平穩(wěn),且較容易確定,可以采用移動(dòng)平均法進(jìn)行計(jì)算。但商品在活動(dòng)期間的銷售變化相對(duì)較大,對(duì)此本文提出采用支持向量機(jī)為基準(zhǔn)模型,結(jié)合粒子群參數(shù)優(yōu)化和灰色綜合關(guān)聯(lián)因素分析的綜合預(yù)測(cè)模型對(duì)參加活動(dòng)的商品進(jìn)行銷售預(yù)測(cè);同時(shí)采用基于興趣度約減的關(guān)聯(lián)規(guī)則分析未參加活動(dòng)商品與參加活動(dòng)商品之間的交互影響。再次,對(duì)本文基于歷史數(shù)據(jù)分析中的數(shù)據(jù)準(zhǔn)備工作進(jìn)行闡述,包括數(shù)據(jù)源分析、數(shù)據(jù)倉(cāng)庫(kù)設(shè)計(jì)以及數(shù)據(jù)ETL實(shí)現(xiàn)。然后結(jié)合本文模型方法進(jìn)行實(shí)例分析,得出綜合預(yù)測(cè)模型相對(duì)于單一支持向量機(jī)預(yù)測(cè)模型在預(yù)測(cè)精度上有一定的提升,結(jié)合興趣度可以更好的發(fā)現(xiàn)有效的關(guān)聯(lián)規(guī)則,同時(shí)考慮活動(dòng)前后期利潤(rùn)損失等的活動(dòng)決策模型較為全面和客觀,有助于企業(yè)更好地進(jìn)行管理決策。最后,對(duì)本文的研究?jī)?nèi)容進(jìn)行了總結(jié),針對(duì)本文研究中的不足進(jìn)行分析和展望。
[Abstract]:In recent years, with the rapid development of the Internet and e-commerce, the competition between businesses is becoming more and more fierce, e-commerce platforms and businesses will use a variety of sales operations to seize the market in order to promote sales. The platform will provide and organize various types of promotional activities for merchants to participate in, and with the rapid development of information technology, the business data accumulation has reached a certain scale. Through historical data, based on data mining and data analysis, this paper analyzes and predicts the effect of e-commerce promotion activities and activity sales, and through data mining and analysis. In order to improve the scientific and intelligent degree of enterprise management and management decision-making, the research work in this paper has a certain theoretical significance but also has important practical significance. This paper analyzes the short-term effect of e-commerce promotion activities, mainly analyzes the two aspects of sales and passenger flow that are more concerned by merchants. Based on the historical data of a Tmall flagship store, this paper analyzes the effects of sales and passenger flow during the activities and before and after the event, through T-test and regression statistical methods. In the aspect of activity sales effect, the sales volume of goods will increase significantly during the activity period, and the ability to stimulate sales will weaken gradually with the time of activity. In the aspect of passenger flow effect, the passenger flow will increase significantly during the activity, but there will be no obvious change in the passenger flow before and after the activity. The decrease in sales volume is mainly due to the low conversion rate of this period of time. Secondly, combined with the above analysis, whether the enterprise should participate in a certain activity management decision. Based on the analysis of overall profit, it is necessary to consider the profit of the participating commodity and the loss of the former and the later period of the activity. Through the analysis of each decision variable, it is found that the change of sales volume during the daily period and before and after the activity is usually relatively stable and easy to determine. The moving average method can be used to calculate, but the sales of goods during the activity is relatively large, so the support vector machine (SVM) is used as the benchmark model in this paper. Combined with particle swarm optimization and grey comprehensive correlation factor analysis, the comprehensive forecasting model was used to predict the sales of the participating commodities. At the same time, the association rules based on interest reduction are used to analyze the interaction between non-participating and participating commodities. Thirdly, the data preparation in this paper based on historical data analysis is described. Including data source analysis, data warehouse design and data ETL implementation. Compared with the single support vector machine prediction model, the comprehensive prediction model has a certain improvement in the prediction accuracy, combined with interest can better find the effective association rules. At the same time, considering the loss of profit before and after the activities, the activity decision-making model is more comprehensive and objective, which is helpful for enterprises to make better management decisions. Finally, the research content of this paper is summarized. In view of the deficiency of this paper, the analysis and prospect are carried out.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;F274

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