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A data mining framework for product and service migration analysis

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  • Siu-Tong Au
  • Rong Duan
  • Wei Jiang

Abstract

With new technologies or products invented, customers migrate from a legacy product to a new product from time to time. This paper discusses a time series data mining framework for product and service migration analysis. In order to identify who migrate, how migrations look like, and the relationship between the legacy product and the new product, we first discuss certain characteristics of customer spending data associated with product migration. By exploring interesting patterns and defining a number of features that capture the associations between the spending time series, we develop a co-integration-based classifier to identify customers associated with migration and summarize their time series patterns before, during and after the migration. Customers can then be scored based on the migration index that integrates the statistical significance and business impact of migration customers. We illustrate the research through a case study of internet protocol (IP) migration in telecommunications and compare it with likelihood-ratio-based tests for change point detections. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Siu-Tong Au & Rong Duan & Wei Jiang, 2012. "A data mining framework for product and service migration analysis," Annals of Operations Research, Springer, vol. 192(1), pages 105-121, January.
    • Handle: RePEc:spr:annopr:v:192:y:2012:i:1:p:105-121:10.1007/s10479-011-0904-5
    DOI: 10.1007/s10479-011-0904-5
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    4. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    5. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
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