Improving prediction of customer behavior in nonstationary environments

L. Yan, David Jonathan Miller, M. C. Mozer, R. Wolniewicz

    Research output: Contribution to conferencePaper

    28 Citations (Scopus)

    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.

    Original languageEnglish (US)
    Pages2258-2263
    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

    Other

    OtherInternational Joint Conference on Neural Networks (IJCNN'01)
    CountryUnited States
    CityWashington, DC
    Period7/15/017/19/01

    Fingerprint

    Telecommunication industry
    Learning systems
    Costs

    All Science Journal Classification (ASJC) codes

    • Software
    • Artificial Intelligence

    Cite this

    Yan, L., Miller, D. J., Mozer, M. C., & Wolniewicz, R. (2001). Improving prediction of customer behavior in nonstationary environments. 2258-2263. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.
    Yan, L. ; Miller, David Jonathan ; Mozer, M. C. ; Wolniewicz, R. / Improving prediction of customer behavior in nonstationary environments. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.6 p.
    @conference{32e8f126099947779e66eb4e45c87642,
    title = "Improving prediction of customer behavior in nonstationary environments",
    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.",
    author = "L. Yan and Miller, {David Jonathan} and Mozer, {M. C.} and R. Wolniewicz",
    year = "2001",
    month = "1",
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    pages = "2258--2263",
    note = "International Joint Conference on Neural Networks (IJCNN'01) ; Conference date: 15-07-2001 Through 19-07-2001",

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    Yan, L, Miller, DJ, Mozer, MC & Wolniewicz, R 2001, 'Improving prediction of customer behavior in nonstationary environments' Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States, 7/15/01 - 7/19/01, pp. 2258-2263.

    Improving prediction of customer behavior in nonstationary environments. / Yan, L.; Miller, David Jonathan; Mozer, M. C.; Wolniewicz, R.

    2001. 2258-2263 Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.

    Research output: Contribution to conferencePaper

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    Yan L, Miller DJ, Mozer MC, Wolniewicz R. Improving prediction of customer behavior in nonstationary environments. 2001. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.