基于改进RFM模型的社交电商细分研究
首发时间:2020-09-24
摘要:在社交电商高速发展的背景下,社交电商服务平台也随之兴起。本文根据社交电商的交易数据,首次提出建立基于RFM模型改进的RCFM模型对社交电商进行细分。该模型在引入社交电商一定时间内的交易人数属性的基础上,利用层次分析法优化社交电商价值得分的指标权重,最后通过优化的Kmeans聚类算法实现对社交电商的细分。结果表明基于RCFM模型对社交电商的细分能充分体现出社交电商的商业价值,对社交电商服务平台构建社交电商用户画像,实现个性化服务具有积极的实践意义。
关键词: 计算机科学与技术 用户细分 RFM模型 层次分析法 Kmeans
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Research on Social E-commerce Segmentation Based on Improved RFM Model
Abstract:In the context of rapid development of social e-commerce, social e-commerce service platforms have also emerged. Based on the transaction data of social e-commerce, this paper first proposes to establish an improved RCFM model based on RFM model to subdivide social e-commerce. This model introduces the attribute of the number of traders in social e-commerce within a certain period of time, uses the analytic hierarchy process to optimize the index weight of social e-commerce value score, and finally implements the subdivision of social e-commerce through the optimized Kmeans clustering algorithm. The results show that the segmentation of social e-commerce based on the RCFM model can fully reflect the commercial value of social e-commerce, and it has positive practical significance for the construction of social e-commerce user portraits on the social e-commerce service platform and the realization of personalized services.
Keywords: computer science and Technology user segmentation RFM model analytic hierarchy process Kmeans
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