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K Nearest Sequence Method and Its Application to Churn Prediction

  • Dymitr Ruta
  • Detlef Nauck
  • Ben Azvine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

In telecom industry high installation and marketing costs make it between six to ten times more expensive to acquire a new customer than it is to retain the existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is lots of related temporal data sequences generated as a result of customer interaction with the service provider. Existing churn prediction methods like decision tree typically just classify customers into churners or non-churners while completely ignoring the timing of churn event. Given histories of other customers and the current customer’s data, the presented model proposes a new k nearest sequence (kNS) algorithm along with temporal sequence fusion technique to predict the whole remaining customer data sequence path up to the churn event. It is experimentally demonstrated that the new model better exploits time-ordered customer data sequences and surpasses the existing churn prediction methods in terms of performance and offered capabilities.

Keywords

Gain Measure Customer Data Customer Interaction Customer Lifetime British Telecom 
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 2006

Authors and Affiliations

  • Dymitr Ruta
    • 1
  • Detlef Nauck
    • 1
  • Ben Azvine
    • 1
  1. 1.British Telecom (BT) GroupChief Technology OfficeIpswichUK

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