Improving prediction of customer behavior in nonstationary environments

L. Yan, D. J. Miller, M. C. Mozer, R. Wolniewicz

Research output: Contribution to conferencePaperpeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Number of pages6
StatePublished - Jan 1 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001


OtherInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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