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Estimating customer future value of different customer segments based on adapted RFM model in retail banking context

机译:在零售银行业务环境中基于适应的RFM模型估算不同客户群的客户未来价值

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One of the important challenges in customer-based organizations is customer cognition, understanding difference between them, and ranking them. Customer need-based segmentation was common in past years, but recently customer value as a quantifiable parameter could be used for customer segmentation. In this regard, customer segmentation based on customer lifetime value (CLV) and estimating the value of each segment would be useful for making decision in marketing and customer relationship management (CRM) program which can be adapted with the characteristics of each segment. Customer future value as a part of customer lifetime value can be estimated based on customer segmentation. In this regard, this study provide a framework for estimating customer future value based on adapted weighted RFM analysis which is a CLV calculating model, for each segment of customer in retail banking scope.
机译:基于客户的组织中的重要挑战之一是客户认知,了解它们之间的差异并对它们进行排名。基于客户需求的细分在过去几年中很普遍,但是最近客户价值作为可量化的参数可以用于客户细分。在这方面,基于客户生命周期价值(CLV)的客户细分并估算每个细分的价值将有助于市场营销和客户关系管理(CRM)程序中的决策,该方案可与每个细分的特征相适应。作为客户生命周期价值一部分的客户未来价值可以根据客户细分进行估算。在这方面,本研究为零售银行业务范围内的每个细分客户提供了一个基于自适应加权RFM分析(CLV计算模型)的客户未来价值估算框架。

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