Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Analysing customer Churn in insurance data: a case study
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A Non-sequential Representation of Sequential Data for Churn Prediction
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Generalised bottom-up pruning: A model level combination of decision trees
Expert Systems with Applications: An International Journal
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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.