Measuring churner influence on pre-paid subscribers using fuzzy logic

Abstract

In the last decades, mobile phones have become the major medium for communication between humans. The site effect is the loss of subscribers. Consequently, Telecoms operators invest in developing algorithms for quantifying the risk to churn and to influence other subscribers to churn. The objective is to prioritize the retention of subscribers in their network due to the cost of obtaining a new subscriber is four times more expensive than retaining subscribers. Hence, we use Extremely Random Forest to classify churners and non-churners obtaining a Lift value at 10% of 5.5. Then, we rely on graph-based measures such as Degree of Centrality and Page rank to measure emitted and received influence in the social network of the carrier. Our methodology allows summarising churn risk score, relying on a Fuzzy Logic system, combining the churn probability and the risk of the churner to leave the network with other subscribers.

Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems

“…We included demographics, call information, network usage, billing information, and customer satisfaction data in our dataset since they are considered influential factors in the CCP process. 13 , 14 A total of 22 features were extracted after careful aggregation, i.e., new features were created based on the original data and some unnecessary features were deleted from it, and features are listed in Table 2 .…”