结构方程模型Pls与Rfm (current, Frequency And Monetary)模型改善银行平均余额的混合模型

Jerry Heikal, Vitto Rialialie, Deva Rivelino, Ign Agus Supriyono
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引用次数: 28

摘要

作为商业参与者,企业家当然需要银行的产品和支持,这些产品和支持可以在印度尼西亚广泛的网络上提供快速简便的服务。在本研究中,结构方程模型(SEM)确定影响平均余额的交易。对所选交易进行RFM细分的目的是了解客户细分得分,并为每个具有不同忠诚度水平的细分建立营销策略,以获得较高的平均余额的财务结果。分割结果发现三个驱动类别,高近时性,中近时性和低近时性类别。High Recency被认为是活跃的客户,活动类别可以根据他们的频率和货币类别进行交叉/向上销售和促销。中期近期类别被认为是有风险的客户,活动类别可以根据他们的频率和金钱来制定留存计划。最后,低近时性被认为是流失客户,活动类别是进行再激活。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Model Of Structural Equation Modeling Pls And Rfm (Recency, Frequency And Monetary) Model To Improve Bank Average Balance
As a business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. In this study, Structural Equation Model (SEM) identify the transaction that influence the average balance. The objects of the RFM segmentation on the selected transaction is to understand customer segment score and build a marketing strategy for each segment with different levels of loyalty for the Financial result of higher Average Balance.  The segmentation results found three driver categories, High Recency, Mid Recency and Low Recency category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary category. Mid Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered already Churn customer where campaign category is to conduct reactivation.
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