一种博弈论辅助的机器学习算法检测用户流失行为

A game theory-assisted machine learning methodology for subscriber churn behaviors detection

中国在2019年11月底正式实施已经试行了9年的携号转网政策。该政策会加强通信市场的流动性和竞争性,使运营商用户流失的问题更加突出。提出、验证并产品化了一种博弈论辅助的机器学习方案,以帮助运营商主动应对携号转网市场的竞争。所提方案为运营商提供了一种机器学习模型,检测用户的携转倾向,并给予差异化待遇。实验结果证明,所提方案能够指引运营商制定有针对性的携号转网策略,准确识别出有携入或者携出倾向的“异常”用户。此外,所提方案已被成功地产品化,极大地提高了运营商的营销效率,增加了用户的满意度,为中国某主要运营商减少了大约50%的用户流失。

At the end of November 2019,China officially implemented the number portability policy(MNP)that has been in trial for 9 years.The policy will strengthen the liquidity and competitiveness of the telecommunication market,making the problem of subscriber churn more prominent.A game theory-assisted machine learning methodology was proposed,verified and commercialized timely,which could help mobile network operator(MNO)actively respond to competition in the MNP market.The proposed methodology provides MNO with a machine learning model to detect subscriber portability and give differentiated treatment.Experimental results show that the proposed methodology can guide MNOs to make a targeted MNP strategy,and precisely identify“abnormal”subscribers who tend to churn-out and potential new subscribers who may churn-in.In addition,the proposed methodology has been successfully commercialized,greatly improving the marketing efficiency of operators,increasing user satisfaction,and reducing the loss of users by about 50%for a tier-1 MNO in China.