Remora Optimization with Machine Learning Driven Churn Prediction for Business Improvement

Sumita Kumar, P. Baruah, S. Kirubakaran, A. S. Kumar, Kamlesh Singh, M. V. J. Reddy
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Abstract

Customer Relationship Management (CRM) is a complete approach to constructing, handling, and establishing loyal and long-lasting customer relationships. It is mostly acknowledged and widely executed for distinct domains, e.g., telecom, retail market, banking and insurance, and so on. A major objective is customer retention. The churn methods drive to recognize early churn signals and identify customers with an enhanced possibility to leave voluntarily. Machine learning (ML) techniques are presented for tackling the churning prediction difficult. This paper presents a Remora Optimization with Machine Learning Driven Churn Prediction for Business Improvement (ROML-CPBI) technique. The aim of the ROML-CPBI technique is to forecast the possibility of customer churns in the business sector. The working of the ROML-CPBI technique encompasses two major processes namely prediction and parameter tuning. At the initial stage, the ROML-CPBI technique utilizes multi-kernel extreme learning machine (MKELM) technique for churn prediction purposes. Secondly, the RO algorithm is applied for adjusting the parameters related to the MKELM model and thereby results in enhanced predictive outcomes. For validating the greater performance of the ROML-CPBI technique, an extensive range of experiments were performed. The experimental values signified the improved outcomes of the ROML-CPBI technique over other ones.
remoa优化与机器学习驱动的客户流失预测业务改进
客户关系管理(CRM)是一个完整的方法来构建,处理和建立忠诚和持久的客户关系。它在不同的领域得到了广泛的认可和执行,例如电信、零售市场、银行和保险等。一个主要的目标是留住客户。流失方法驱动识别早期流失信号,并识别客户与提高自愿离开的可能性。提出了机器学习(ML)技术来解决搅拌预测难题。提出了一种基于机器学习驱动的客户流失预测的业务改进(ROML-CPBI)技术。ROML-CPBI技术的目的是预测商业部门客户流失的可能性。ROML-CPBI技术的工作包括两个主要过程,即预测和参数调优。在初始阶段,ROML-CPBI技术利用多核极限学习机(MKELM)技术进行客户流失预测。其次,利用RO算法对MKELM模型相关参数进行调整,提高预测结果。为了验证ROML-CPBI技术的更高性能,进行了广泛的实验。实验结果表明,ROML-CPBI技术的治疗效果优于其他方法。
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