Advertisement

KNNs and Sequence Alignment for Churn Prediction

  • Mai Le
  • Detlef Nauck
  • Bogdan Gabrys
  • Trevor Martin
Conference paper

Abstract

Large companies interact with their customers to provide a variety of services to them. Customer service is one of the key differentiators for companies. The ability to predict if a customer will leave in order to intervene at the right time can be essential for pre-empting problems and providing high level of customer service. The problem becomes more complex as customer behaviour data is sequential and can be very diverse. We are presenting an efficient sequential forecasting methodology that can cope with the diversity of the customer behaviour data. Our approach uses a combination of KNN (K nearest neighbour) and sequence alignment. Temporal categorical features of the extracted data is exploited to predict churn by using sequence alignment technique. We address the diversity aspect of the data by considering subsets of similar sequences based on KNNs. Via empirical experiments, it can be demonstrated that our model offers better results when compared with original KNNs which implies that it outperforms hidden Markov models (HMMs) because original KNNs and HMMs applied to the same data set are equivalent in terms of performance as reported in another paper.

References

  1. 1.
    J. Hadden, A. Tiwari, R. Roy, D. Ruta, International Journal of Intelligent Technology 1(1), 104 (2006)Google Scholar
  2. 2.
    C. Archaux, H. Laanaya, A. Martin, A. Khenchaf, in International Conference on Information & Communication Technologies: from Theory to Applications (ICTTA) (2004), pp. 19–23Google Scholar
  3. 3.
    M. Eastwood, B. Gabrys, in Proceedings of the KES2009 Conference (Santiago, Chile, 2009)Google Scholar
  4. 4.
    B. Lariviere, D. Van den Poel, Expert Systems with Applications 29(2), 472 (2005)Google Scholar
  5. 5.
    A. Lemmens, C. Croux, Journal of Marketing Research 43(2), 276 (2006)Google Scholar
  6. 6.
    D. Ruta, D. Nauck, B. Azvine, in Intelligent Data Engineering and Automated Learning IDEAL 2006, Lecture Notes in Computer Science, vol. 4224, ed. by E. Corchado, H. Yin, V. Botti, C. Fyfe (Springer, Berlin, 2006), pp. 207–215Google Scholar
  7. 7.
    C. Wei, I. Chiu, Expert Systems with Applications 23(2), 103 (2002)Google Scholar
  8. 8.
    Churn Prediction using Complaints Data.Google Scholar
  9. 9.
    S. Nam, P. Manchanda, P. Chintagunta, Ann Arbor 1001, 48 (2007)Google Scholar
  10. 10.
    R. Duda, P. Hart, D. Stork, Pattern Classification (Wiley, New York, 2001)Google Scholar
  11. 11.
    M. Berry, G. Linoff, Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management (Wiley, Newyork, 2004)Google Scholar
  12. 12.
    M. Waterman, Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 344, 383 (1994)Google Scholar
  13. 13.
    S. Needleman, C. Wunsch, Journal of Molecular Biology 48, 443 (1970)Google Scholar
  14. 14.
    T. Smith, M. Waterman, Journal of Molecular Biology 147, 195 (1981)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mai Le
    • 1
  • Detlef Nauck
    • 2
  • Bogdan Gabrys
    • 1
  • Trevor Martin
    • 3
  1. 1.Bournemouth UniversitySchool of Design, Engineering and ComputingBournemouthUK
  2. 2.BT, Research and TechnologyIpswichUK
  3. 3.Bristol UniversityBristolUK

Personalised recommendations