Telco Churn Prediction with Big Data

Yiqing Huang, Fangzhou Zhu, Mingxuan Yuan, K. Deng, Yanhua Li, Bing Ni, Wenyuan Dai, Qiang Yang, Jia Zeng
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引用次数: 110

Abstract

We show that telco big data can make churn prediction much more easier from the $3$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having $0.96$ precision for the top $50000$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.
利用大数据预测电信客户流失
我们的研究表明,从3个V的角度来看,电信公司的大数据可以让客户流失预测变得更加容易:数量、种类和速度。实验结果证实,通过使用大量的训练数据、来自业务支持系统(BSS)和运营支持系统(OSS)的大量特征,以及对新数据的高速处理,预测性能得到了显著提高。我们已经在中国最大的移动运营商之一部署了这个流失预测系统。从数百万活跃用户中,该系统可以提供下个月最有可能流失的预付费用户列表,对于列表中预测流失的前5万名用户,该系统的精确度为0.96美元。自动匹配留存率活动与目标潜在流失者显著提高他们的充值率,从而产生巨大的商业价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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