Title :
Improving prediction of customer behavior in nonstationary environments
Author :
Yan, Lian ; Miller, David J. ; Mozer, Michael C. ; Wolniewicz, Richard
Author_Institution :
Athene Software Inc., Boulder, CO, USA
Abstract :
Customer churn, switching from one service provider to another, costs the wireless telecommunications industry $4 billion each year in North America and Europe. To proactively build lasting relationships with customers, it is thus crucial to predict customer behavior. Machine learning has been applied to churn prediction, using historical data such as usage, billing, customer service, and demographics. However, because customer behavior is often nonstationary, training a model based on data extracted from a window of time in the past yields poor performance on the present. We propose two distinct approaches, using more historical data or new, unlabeled data, to improve the results for this real-world, large-scale, nonstationary problem. A new ensemble classification method, with combination weights learned from both labeled and unlabeled data, is also proposed, and it outperforms bagging and mixture of experts
Keywords :
learning (artificial intelligence); neural nets; pattern classification; service industries; telecommunication services; bagging; billing; customer behavior prediction; customer churn; customer service; demographics; ensemble classification method; expert mixture; labeled data; nonstationary environments; real-world large-scale nonstationary problem; unlabeled data; usage; wireless telecommunications industry; Communication industry; Costs; Customer service; Data mining; Demography; Europe; Industrial relations; Machine learning; North America; Telecommunication switching;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938518