基于AI的移动通信中端用户信用额度预测方法

A. Pallegedara, V.S. Amaratunga, R. Gopura, P.D. Jayathileka
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引用次数: 3

摘要

即使在大多数发展中国家,移动通信也已成为许多人的理所当然的事情。随着市场饱和,对现有客户的关注和保留成为移动通信网络运营商稳定收入的关键因素。我们提出了一种预测性数据挖掘模型,以减少由于不付费而导致的强制流失率:对付费用户或非付费用户的开放金额进行估计,可以防止用户超支,并最终流失,从而延长客户关系的停留时间,确保未来的收入,因此必要的预测系统将对移动通信服务提供商(SP)非常有利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Based Approach of Predicting the Credit Limits of Users to Middle Customer based Mobile Communication Services
Most of the developing countries even, mobile communication has become a matter of course for many people. As markets saturate, the care and retention of existing customers becomes a key element for revenue stabilization for mobile communication network operators. We present a predictive data mining model to reduce the rate of forced churn as a consequence of non-payment: estimations of subscribers' open amounts if being payers or non-payers allow to prevent subscribers from overspending-and ultimately churning-thus prolonging the customer relationship dwell time and securing future revenues, and hence necessary prediction system would be a great benefit to the mobile communication service providers (SP)
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