期刊
KNOWLEDGE-BASED SYSTEMS
卷 132, 期 -, 页码 21-29出版社
ELSEVIER
DOI: 10.1016/j.knosys.2017.05.027
关键词
CRM; RFM; Large-scale data; MCA; Time interval
资金
- National Key Project of Scientific and Technical Supporting Programs of China [2014BAH26F02]
- Special Fund for Beijing Common Construction Project
Conventional customer relationship management (CRM) is typically based on RFM model, whose parameters are the recency, frequency and monetary aspects of target customers. The latest comprehensive analysis has enabled CRM to present parameters with time series. For example, researchers can account for changing trends based on an RFM model for flexible marketing strategies. Such changes might inspire telecommunication service scenarios that user value relies on long-term performance. In this study, we propose a statistic-based approach to value latent users via time series segmenting time interval of RFM in large scale data set. Apart from utilizing in Spark platform, we integrate multiple corresponding analysis (MCA) to regularize clustering results by the RFM model and extend these approaches to multiple levels. A comprehensive set of experiments, revealed interesting observations regarding the co-existence of time interval and RFM model. First, the clustering method along time interval in three dimensions of the RFM model outperforms the method along the three dimensions in each interval. Subsequently, the cooperation of RFM and MCA provides a convenient methodology for exploring CRM in large-scale data. Therefore, the RFM model with time intervals integrated with MCA in CRM are essential. (C) 2017 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据