基于RFM与K-Means的智能家电消费者画像与营销策略研究——以石头科技京东平台数据为例

Research on Consumer Profiling and Marketing Strategy of Smart Home Appliances Based on RFM and K-Means—A Case Study of Roborock Technology’s JD Platform Data

摘要: 随着智能家居市场的快速发展，扫地机器人作为代表性智能家电产品，其消费群体规模日益扩大，消费需求呈现多样化与分层化趋势。本研究基于石头科技扫地机器人在京东平台的消费数据，结合RFM客户价值模型与K-Means聚类算法对消费者行为特征进行分析，以探究消费者的行为差异，为企业精准营销提供数据支撑。研究发现：消费者可划分为高价值品质偏好型、中等性价比导向型与低价格敏感型三类群体，并基于评论内容、购买时间偏好及对促销活动的响应差异，分析各类群体的行为特征。研究结论为智能家电企业实施精准营销、优化产品定位及加强客户关系管理提供了数据支撑与方法参考。

Abstract: With the rapid development of the smart home market, robotic vacuum cleaners, as representative smart home appliances, have seen a continuous expansion in their consumer base, while consumption demands have become increasingly diversified and stratified. Based on the consumption data of Roborock robotic vacuum cleaners on the JD platform, this study combines the RFM customer value model and the K-Means clustering algorithm to analyze consumer behavior characteristics and explore behavioral differences among consumers, so as to provide data support for enterprises to implement precision marketing. The study finds that consumers can be divided into three categories: high-value quality-oriented consumers, medium-value cost-performance-oriented consumers, and low-value price-sensitive consumers. This paper further analyzes the behavioral characteristics of each group based on review content, purchase time preferences, and differential responses to promotional activities. The research conclusions provide data support and methodological references for smart home appliance enterprises to carry out precision marketing, optimize product positioning, and strengthen customer relationship management.