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基于RFBC模型与聚类分析的电商用户细分研究
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Abstract:
在电子商务领域，消费者的行为数据具有高维度和复杂性。针对传统RFM模型的局限性，本研究提出了一种改进的RFBC模型。该模型结合了购买商品品牌数和购买商品类别数两个新维度，采用k-means++算法进行用户细分，并根据手肘法来确定最佳的聚类数k。由此得到具有不同购买行为特征的六类用户群体，基于这些群体特征，制定出个性化营销策略，使企业在激烈的市场竞争中获取优势。
In the field of e-commerce, consumer behavior data has a high dimension and complexity. Aiming at the limitations of the traditional RFM model, an improved RFBC model is proposed in this paper. The model combines two new dimensions, the number of brands purchased and the number of categories purchased, and uses the k-means++ algorithm to subdivide users, determining the optimal clustering number k according to the elbow method. Thus, six types of user groups with different purchasing behavior characteristics are obtained. Based on these group characteristics, personalized marketing strategies are formulated to enable enterprises to gain advantages in the fierce market competition.
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