The most valuable asset for a company is its customers’ base. As a result, customer relationship management (CRM) is an important task that drives companies. By identifying and understanding the valuable customer segments, appropriate marketing strategies can be used to enhance customer satisfaction and maintain loyalty, as well as increase company retention. Predicting customer turnover is an important tool for companies to stay competitive in a fast-growing market. In this paper, we use the recurrent nerve sketch to predict rejection based on the time series of the lifetime of the customer. In anticipation, a key aspect of identifying key triggers is to turn off. To overcome the weakness of recurrent neural networks, the research model of the combination of LRFMP with the neural network has been used. In this paper, it was found that clustering by LRFMP can be used to perform a more comprehensive analysis of customers’ turnover. In this solution, LRFMP is used to execute customer segregation. The objective is to provide a new framework for LRFMP for macrodata and macrodata analysis in order to increase the problem of business problem solving and customer depreciation. The results of the research show that the neural networks are capable of predicting the LRFMP precursors of the customers in an effective way. This model can be used in advocacy systems for advertising and loyalty programs management. In the previous research, the LRFM and RFM algorithms along with the neural network and the machine learning algorithm, etc., have been used, and in the proposed solution, the use of the LRFMP algorithm increases the accuracy of the desired.

中文翻译：

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使用递归神经网络预测客户流失率

对公司而言，最宝贵的资产是其客户群。因此，客户关系管理 (CRM) 是推动公司发展的一项重要任务。通过识别和了解有价值的客户群，可以使用适当的营销策略来提高客户满意度和保持忠诚度，以及增加公司保留率。预测客户流失率是公司在快速增长的市场中保持竞争力的重要工具。在本文中，我们使用递归神经草图根据客户生命周期的时间序列来预测拒绝。在预期中，识别关键触发器的一个关键方面是关闭。为了克服循环神经网络的弱点，采用了LRFMP与神经网络相结合的研究模型。在本文中，结果发现，LRFMP 的聚类可用于对客户的营业额进行更全面的分析。在此解决方案中，LRFMP 用于执行客户隔离。目标是为宏观数据和宏观数据分析提供一个新的 LRFMP 框架，以增加解决业务问题和客户折旧的问题。研究结果表明，神经网络能够有效地预测客户的 LRFMP 前兆。该模型可用于宣传系统中的广告和忠诚度计划管理。在之前的研究中，LRFM 和 RFM 算法以及神经网络和机器学习算法等都被使用，并且在所提出的解决方案中，LRFMP 算法的使用提高了所需的精度。