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A Non-sequential Representation of Sequential Data for Churn Prediction

  • Mark Eastwood
  • Bogdan Gabrys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5711)

Abstract

We investigate the length of event sequence giving best predictions when using a continuous HMM approach to churn prediction from sequential data. Motivated by observations that predictions based on only the few most recent events seem to be the most accurate, a non-sequential dataset is constructed from customer event histories by averaging features of the last few events. A simple K-nearest neighbor algorithm on this dataset is found to give significantly improved performance. It is quite intuitive to think that most people will react only to events in the fairly recent past. Events related to telecommunications occurring months or years ago are unlikely to have a large impact on a customer’s future behaviour, and these results bear this out. Methods that deal with sequential data also tend to be much more complex than those dealing with simple non-temporal data, giving an added benefit to expressing the recent information in a non-sequential manner.

Keywords

Hide Markov Model Event History Recent Event Combination Method Hide State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mark Eastwood
    • 1
  • Bogdan Gabrys
    • 1
  1. 1.Computational Intelligence Research Group, School of Design, Engineering and ComputingBournemouth UniversityUK

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