Paper Info
Title
Not too late to identify potential churners: early churn prediction in telecommunication industry.
Abstract
Churn prediction, which is to identify who are prone to abandon the subscription, is of high significance for the operators to retain the potential churners. It should be noted that, in practice, the earlier the churners are identified, the more effective strategies the operators can develop to retain them. While the earlier prediction of customer churn in telecommunication industry has not been well investigated and the predicting accuracy of previous work degrades unacceptably when the interval between observed attributes and predicted labels is prolonged. In this paper, from a different perspective, we study the effectiveness to find the churners as early as possible with the accuracy being high enough, which we define as Early Churn Prediction. The predictive performance of the proposed model, which takes time series attributes and influence of churning contacts in social network into consideration, is investigated. We evaluate the method using a 12-month-long dataset collected by one of the largest operators in China. The results show that our model significantly outperforms the previous work especially when the prediction interval is larger than 3 months.
Year
DOI
Venue
2016
10.1145/3006299.3006324
Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Keywords
Field
DocType
Churn prediction, Telecommunication industry, Data mining
Time series,Data mining,Telecommunications,Social network,Computer science,Prediction interval,Artificial intelligence,Operator (computer programming),Artificial neural network,Market research,Churning,Information and Communications Technology,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-4468-9
0
0.34
References 
Authors
7
5
Name
Order
Citations
PageRank
Jingjiao Zhang100.34
Jiaqing Fu200.34
Chunhong Zhang3146.37
Xin Ke440.75
Zheng Hu5506.50