本文選題：大數(shù)據(jù) + 客戶細分��； 參考：《北京交通大學(xué)》2015年碩士論文

【摘要】：近年來,我國鐵路貨運信息化建設(shè)取得了很大的突破和成果,但沉淀的大量貨運數(shù)據(jù)缺乏有效的管理利用,開展大數(shù)據(jù)技術(shù)在鐵路貨運業(yè)務(wù)上的數(shù)據(jù)挖掘研究具有重要的應(yīng)用價值�？蛻艏毞质秦涍\營銷的基礎(chǔ),能夠更好地識別客戶群體,合理地配置企業(yè)資源,為企業(yè)創(chuàng)造更大的利潤。但目前鐵路貨運的客戶細分采用基于經(jīng)驗和統(tǒng)計的簡單劃分的方法,不能準確區(qū)分客戶類別,無法有效地支持營銷決策。本文將客戶細分的常用方法RFM模型做出改進,并與聚類挖掘算法相結(jié)合,為鐵路貨運海量數(shù)據(jù)下復(fù)雜的客戶細分問題提供了新的解決方法。 本文的主要工作包含以下幾個方面： (1)針對鐵路貨運的特點,對傳統(tǒng)的客戶細分方法RFM模型做了改進,提出了KFM模型。 (2)由于傳統(tǒng)的K-means聚類算法存在對初始聚類中心敏感且容易陷入局部最優(yōu)的缺點,本文提出了改進的K-means聚類算法。實驗表明改進后的算法提高了客戶細分的準確率。 (3)將KFM模型與改進后的K-means聚類算法相結(jié)合,利用鐵路電子商務(wù)系統(tǒng)的貨運數(shù)據(jù)進行了客戶細分。細分結(jié)果很好地展現(xiàn)了各類客戶的特征,彌補了傳統(tǒng)的基于RFM模型的客戶細分對數(shù)據(jù)挖掘不夠深入的缺陷。 (4)在Hadoop大數(shù)據(jù)平臺下,實現(xiàn)了數(shù)據(jù)標準化方法和K-means聚類算法基于MapReduce的并行化。實驗表明基于MapReduce的并行化提升了算法的性能,能勝任大量數(shù)據(jù)分析處理任務(wù)。 本文將聚類挖掘技術(shù)應(yīng)用于鐵路貨運大數(shù)據(jù)平臺下的客戶細分,確定不同價值和行為傾向的客戶類別,為企業(yè)展現(xiàn)出客戶所屬類別,從而進行針對性管理,有利于貨運部門的精準化營銷決策。
[Abstract]:In recent years, great breakthroughs and achievements have been made in the construction of railway freight information in China, but a large number of freight data precipitated lack of effective management and utilization. It has important application value to develop data mining research of big data technology in railway freight business. Customer segmentation is the basis of freight marketing, which can better identify customer groups, reasonably allocate enterprise resources, and create greater profits for enterprises. However, the current customer segmentation of railway freight is based on the simple division method based on experience and statistics, which can not accurately distinguish customer categories, and can not effectively support marketing decisions. In this paper, the RFM model of customer segmentation is improved and combined with clustering mining algorithm, which provides a new solution to the complex customer segmentation problem under the massive data of railway freight transport. The main work of this paper includes the following aspects: 1) according to the characteristics of railway freight, the traditional customer segmentation method RFM model is improved. Because the traditional K-means clustering algorithm is sensitive to the initial clustering center and easy to fall into local optimum, this paper proposes an improved K-means clustering algorithm. Experiments show that the improved algorithm improves the accuracy of customer segmentation. (3) the KFM model is combined with the improved K-means clustering algorithm, and the freight data of railway e-commerce system is used to segment customers. The segmentation results show the characteristics of all kinds of customers and make up for the defects of traditional RFM-based customer segmentation which is not deep enough for data mining. Data standardization method and K-means clustering algorithm are implemented based on MapReduce parallelization. Experiments show that the parallelization based on MapReduce can improve the performance of the algorithm and be able to deal with a large number of data analysis tasks. In this paper, clustering mining technology is applied to customer segmentation of railway freight big data platform, and customer categories with different values and behavioral tendencies are determined to show customer categories for enterprises, so as to carry out targeted management. It is beneficial to the precision marketing decision of freight department.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP311.13

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本文編號：2020146