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基于大数据分析的服装电商精准营销策略
Precision Marketing Strategy of Clothing E-Commerce Based on Big Data Analysis

DOI: 10.12677/MOM.2022.123007, PP. 53-64

Keywords: 精准营销,决策树,K-Means聚类,电子商务
Precision Marketing
, Decision Tree, K-Means Clustering, Electronic Commerce

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Abstract:

本文运用大数据技术采集某中小型服装商家数据,通过CHAID决策树分析,发现顾客购买该服装商家的第一层决策因素是性别,主要为女性,第二层决策因素是消费者年龄,第三层决策因素是消费者所在城市。再通过K-means将顾客订单分成三个聚类,第一类是一般订单,每一订单购买数量少且总金额不高;第二类是重要订单,每一订单购买数量多且总金额高;第三类是可提高订单,每一订单购买数量虽然不多,但是总金额高。最后根据决策树分析与聚类分析结果,提出相应的精准营销策略。
This paper uses big data technology to collect and analysis the data of one small and medium clothing e-commerce enterprise, through CHAID decision tree analysis, it is found that the first level decision-making factor for customers to buy the clothing is gender, the main customers are women, the second level decision-making factor is the age range of consumers, and the third level decision-making factor is the city where consumers are located. Then we use k-means cluster analysis to divide customer orders into three clusters. The first category is general orders. Each order has a less purchase quantity and a low total amount. The second category is important orders. Each order has a large purchase quantity and a high total amount. The third category is orders that can be increased. Each order has a less purchase quantity but the total amount is high. Finally, according to the results of decision tree analysis and cluster analysis, we put forward corresponding precision marketing strategies for all types of customers.

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