摘要
大数据时代,数据挖掘算法的作用日益凸显。近年来,面对电子商务移动化的事态,精准营销这一新兴概念同样引领着目前市场营销方式的变革。主要研究了数据挖掘算法在精准营销中的具体应用方式。介绍了数据挖掘技术与精准营销技术的发展历程和先进成果。此后分析了市场营销中客户信息的分类和特征。通过比较朴素贝叶斯分类算法、K-最近邻域分类算法和K-means聚类算法的复杂度、分类或聚类效果等特性,确定了各自的优缺点和适用范围,并依此确定了以K-means聚类算法制定精准营销策略的方针。以该策略在房产营销中的应用为例,通过详述从客户数据处理到客户画像描绘,再到基于客户画像描绘的精准营销策略等实践环节,完整地描绘了基于数据挖掘技术的精准营销策略在现实背景下的实现方法和显著优势。
In the era of big data,the role of data mining algorithms is becoming increasingly prominent.In recent years,facing the mobile situation of e-commerce,the emerging concept of precision marketing also leads the reform of current marketing methods.The purpose of this paper is to study the specific application of data mining algorithm in precision marketing.Firstly,it introduces the development process and advanced achievements of data mining technology and precision marketing technology.Then it analyzes the classification and characteristics of customer information in marketing.Then,by comparing the complexity,classification or clustering effect of naive Bayesian classification algorithm,K-nearest neighbor classification algorithm and K-means clustering algorithm,their respective advantages,disadvantages and application scope are determined.Based on this,the policy of formulating precision marketing strategy with K-means clustering algorithm is determined.Finally,taking the application of this strategy in real estate marketing as an example,by detailing the practical links from customer data processing to customer portrait description,and then to the precision marketing strategy based on customer portrait description,this paper completely describes the implementation methods and significant advantages of the precision marketing strategy based on data mining technology in the real context.
作者
吴翰
章翔
WU Han;ZHANG Xiang(School of International Studies,Shenyang University,Shenyang 110003,China;Information Engineering College,Shenyang University,Shenyang 110003,China)
出处
《计算机与网络》
2022年第10期68-73,共6页
Computer & Network
关键词
精准营销
数据挖掘
分类算法
聚类算法
precise marketing
data mining
classification algorithm
clustering algorithm
