本文選題：貨運(yùn)數(shù)據(jù)　切入點(diǎn)：數(shù)據(jù)挖掘　出處：《中南大學(xué)》2012年碩士論文

【摘要】：隨著貨運(yùn)市場(chǎng)競(jìng)爭(zhēng)的不斷加劇、鐵路信息化進(jìn)程的逐步推進(jìn),鐵路信息系統(tǒng)積累了大量的貨運(yùn)數(shù)據(jù)信息。貨運(yùn)數(shù)據(jù)具有信息量大、結(jié)構(gòu)復(fù)雜、多層次等特征。應(yīng)用數(shù)據(jù)挖掘相關(guān)技術(shù),研究鐵路貨運(yùn)市場(chǎng)目標(biāo)客戶、生命周期、客戶價(jià)值及發(fā)展等相關(guān)問(wèn)題,是當(dāng)前鐵路貨運(yùn)管理研究的熱點(diǎn)話題。論文研究了我國(guó)鐵路貨運(yùn)數(shù)據(jù)挖掘問(wèn)題,所做主要工作如下： (1)分析了鐵路貨運(yùn)數(shù)據(jù)的組成、特點(diǎn)和層次結(jié)構(gòu),并對(duì)鐵路貨運(yùn)數(shù)據(jù)進(jìn)行了整理和分類。 (2)深度挖掘鐵路貨運(yùn)數(shù)據(jù)關(guān)聯(lián)規(guī)則,進(jìn)行知識(shí)發(fā)現(xiàn)；對(duì)鐵路貨運(yùn)數(shù)據(jù)進(jìn)行聚類分析,探尋鐵路貨運(yùn)目標(biāo)客戶；利用ARIMA模型,提出基于時(shí)間序列的鐵路貨運(yùn)量預(yù)測(cè)方法。 (3)系統(tǒng)分析鐵路貨運(yùn)客戶關(guān)系生滅過(guò)程,合理劃分鐵路貨運(yùn)客戶生命周期的典型階段,揭示不同階段特征變化和客戶忠誠(chéng)發(fā)展演變規(guī)律,構(gòu)建基于數(shù)據(jù)挖掘的鐵路貨運(yùn)客戶生命周期階段判定模型,提出鐵路貨運(yùn)客戶生命周期階段判定過(guò)程與方法。 (4)深入分析不同生命周期階段鐵路貨運(yùn)客戶利潤(rùn)曲線特征,構(gòu)建不同階段的鐵路貨運(yùn)客戶利潤(rùn)擬合函數(shù),并提出基于數(shù)據(jù)挖掘的鐵路貨運(yùn)客戶價(jià)值細(xì)分算法,對(duì)鐵路貨運(yùn)客戶進(jìn)行細(xì)分。 (5)全面分析營(yíng)銷成本、客戶類型、期望收益等鐵路貨運(yùn)客戶發(fā)展影響因素,構(gòu)建潛在型客戶發(fā)展模型、競(jìng)爭(zhēng)型客戶發(fā)展模型以及保持型客戶發(fā)展模型等一系列客戶發(fā)展模型,在此基礎(chǔ)上,提出不同類型不同階段的鐵路貨運(yùn)客戶關(guān)系管理策略,并給出實(shí)例分析。
[Abstract]:With the increasing competition of freight transportation market and the gradual advancement of railway informatization process, railway information system has accumulated a large amount of freight data information, which has a large amount of information and complex structure. Using data mining technology to study the target customer, life cycle, customer value and development of railway freight market. It is a hot topic in the research of railway freight management. This paper studies the data mining problem of railway freight transport in China. The main work is as follows:. 1) the composition, characteristics and hierarchical structure of railway freight data are analyzed, and the railway freight data are arranged and classified. (2) deeply mining the association rules of railway freight data, making knowledge discovery; clustering analysis of railway freight data, searching for target customers of railway freight; using ARIMA model, a method of railway freight volume prediction based on time series is proposed. 3) systematically analyzing the birth and death process of railway freight transport customer relationship, reasonably dividing the typical stages of railway freight customer life cycle, revealing the characteristics of different stages and the evolution law of customer loyalty development. Based on data mining, a decision model of railway freight customer life cycle stage is built, and the process and method of railway freight customer life cycle phase determination are proposed. 4) deeply analyzing the characteristics of railway freight customer profit curve in different life cycle stages, constructing the railway freight customer profit fitting function in different stages, and putting forward the railway freight customer value subdivision algorithm based on data mining. Subdivide railway freight customers. 5) analyzing the influence factors of railway freight customer development, such as marketing cost, customer type and expected income, and constructing a series of customer development models, such as potential customer development model, competitive customer development model and maintenance customer development model, etc. On this basis, the paper puts forward different types and different stages of railway freight customer relationship management strategy, and gives an example analysis.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F252;F532;F224

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本文編號(hào)：1677697