基于数据挖掘的淘宝精准营销策略研究

Research on Taobao Precision Marketing Strategy Based on Data Mining

针对淘宝平台存在的用户定位模糊,无差别且低效率的营销问题,基于海量的淘宝用户行为特征数据,综合利用Weka、R数据挖掘软件,运用K-Means聚类算法对淘宝用户进行精准动态画像,构建用户标签化模型.同时利用Stata数据分析软件,建立二元Logistic回归模型预测淘宝特定用户的支付行为,进而建立支付行为预测体系.从精准营销的角度,就淘宝的营销方案设计提出合理化建议.

Aiming at the problem of fuzzy user positioning,indiscriminate and inefficient marketing on the Taobao platform,based on massive Taobao user behavior characteristic data,using Weka and R data mining software,and using K-Means clustering algorithm to make accurate dynamic portraits of Taobao users,a user labeling model is built,and using Stata data analysis software,a binary logistic regression model is established to predict the payment behavior of specific users on Taobao,and then a payment behavior prediction system is built.From the perspective of precision marketing,the reasonable suggestions for the design of Taobao’s marketing plan are put foward.