数据挖掘在电商运营策略中的应用研究

发布时间：2018-01-16 04:28

本文关键词：数据挖掘在电商运营策略中的应用研究　出处：《南昌航空大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着互联网的迅猛发展,越来越多的互联网企业重视应用数据挖掘技术来研究网络购物、电商运营策略等方面,通过研究发现规律、预测趋势,辅助企业运营者制定精准的营销策略,有利于互联网、电子商务健康有序的发展。依托电子商务企业——恒大鞋类专营天猫店铺的经营数据,借助SPSS Molder工具,运用关联规则、Recency Frequency Monetary(RFM)分析、聚类分析三种数据挖掘技术来研究店铺关联营销、客户细分、客户关系管理三个方面,得出分析结论和提出运营建议:(1)应用关联规则对企业店铺中用户购买相关产品规律进行分析,得出网面鞋和网布鞋、休闲鞋和凉鞋等关联效果较好,有利于企业区分强关联、弱关联产品,制定精准的关联营销方案,提高客单价,从而增加销售额。根据关联规则分析结论来制定货品规划运营策略、陈列策略和推广策略,使电子商务第三方平台、消费者和企业三方均受益。(2)应用RFM客户细分方法,对近3年的用户购买订单进行分析,得出客户的RFM得分,为客户细分提供依据,有利于客户关系管理策略的制定。争取留住大客户,并对潜在客户进行客户提升,对一般客户进行少量投入,可以调整客户关系管理的资金投入分配比例。(3)应用聚类分析方法对RFM客户细分结论进行深层次的数据挖掘,区分为潜在客户、活跃客户和沉默客户等群体。第一,面向潜在客户进行交易环节中互动营销、会员体系正向激励和定期调研等运营建议;第二,面向活跃型客户,除拥有潜在客户的运营建议之外,拥有特权和兑换,制定差异化营销方式;第三,面向沉默型客户,采取定期沟通计划运营策略。完善客户关系管理策略,降低企业客户维护成本。
[Abstract]:With the rapid development of the Internet, more and more Internet enterprises pay more and more attention to the application of data mining technology to study online shopping, e-commerce operation strategy and other aspects. Assist enterprise operators to formulate accurate marketing strategies, conducive to the Internet, e-commerce healthy and orderly development. Rely on e-commerce enterprises-Evergrande shoes franchise Tmall business data. With the help of SPSS Molder tool, the association rule is used to analyze. Clustering analysis of three kinds of data mining technology to study shop marketing, customer segmentation, customer relationship management three aspects. Get the analysis conclusion and put forward the operation suggestion: 1) apply the association rule to analyze the law of the user buying the related products in the enterprise store, draw the conclusion that the net top shoes and the net cloth shoes, casual shoes and sandals and so on the correlation effect is better. It is helpful for enterprises to distinguish strong association products, weak related products, develop accurate association marketing plan, improve customer unit price, thus increase sales. According to the association rules analysis conclusion to formulate the goods planning operation strategy. Display strategy and promotion strategy, so that e-commerce third-party platform, consumers and enterprises benefit from. 2) the application of RFM customer segmentation method, the nearly 3 years of customer purchase orders are analyzed. Get the customer's RFM score, provide the basis for customer segmentation, is conducive to the formulation of customer relationship management strategy. Strive to retain large customers, and potential customers for customer upgrading, a small amount of input to the general customers. We can adjust the ratio of investment allocation of customer relationship management. 3) apply cluster analysis method to RFM customer segmentation conclusion of deep-level data mining, divided into potential customers. Active customers and silent customers and other groups. First, targeted at potential customers in the transaction links of interactive marketing, the membership system is positive to encourage and regular research and other operational recommendations; Second, for active customers, in addition to the operation advice of potential customers, with privileges and exchange, develop differentiated marketing; Third, for silent customers, take regular communication plan operation strategy, improve customer relationship management strategy, reduce the cost of customer maintenance.
【学位授予单位】：南昌航空大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.6;TP311.13

【参考文献】