Telecommunications Science ›› 2020, Vol. 36 ›› Issue (6): 79-89.doi: 10.11959/j.issn.1000-0801.2020164

• Research and Development • Previous Articles     Next Articles

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

Ye OUYANG1,Aidong YANG1,Fanyu MENG2   

  1. 1 Telco Artificial Intelligence Labs,AsiaInfo Technologies (China) Co.,Ltd.,Beijing 100193,China
    2 Electronical Engineering &Computer Science Department,University of California,Berkeley 94720,US
  • Revised:2020-06-03 Online:2020-06-20 Published:2020-06-18


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.

Key words: subscriber churn, mobile number portability, game theory, machine learning

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