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Application of Computational Intelligence to Predict Churn and Non-Churn of Customers in Indian Telecommunication

机译:计算智能在预测印度电信客户流失和非流失中的应用

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In the modern society, mobile communication became the leading medium of communication. Now the public policies and standardization of mobile communication allows customers to switch from one service provider to another service provider easily. One of the most critical challenges in data and voice telecommunication service industry is retaining customers. The cost of retaining an existing customer is lesser than the cost of getting a new customer. So service providers now shifted their focus from customer acquisition to customer retention. As a result, churn prediction has emerged as the most essential Business Intelligence (BI) application that aims to identify the customers who are about to transfer their service to a competitor i.e. To churn. In this paper, we proposed Counter Propagation Neural Networks (CPNN), Classification and Regression Trees (CART), J48 and fuzzy ARTMAP to predict customer churn and non-churn in telecommunication sector. The dataset analyzed is taken from Indian Telecommunication Service Industry.
机译:在现代社会中,移动通信成为沟通的领先媒介。现在,移动通信的公共政策和标准化允许客户轻松地从一个服务提供商切换到另一个服务提供商。数据和语音电信服务业最关键的挑战之一是留住客户。保留现有客户的成本低于获得新客户的成本。因此,服务提供商现在将重点从客户获取转移到客户保留。因此,流失预测已成为最重要的商业智能(BI)申请,旨在识别即将将其服务转移到竞争对手的客户。在本文中,我们提出了对抗传播神经网络(CPNN),分类和回归树(推车),J48和模糊艺术图,以预测客户流失和电信扇区的非流失。分析的数据集采用印度电信服务业。

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