A Non-Sequential Representation of Sequential Data for Churn Prediction.

Eastwood, M. and Gabrys, B., 2009. A Non-Sequential Representation of Sequential Data for Churn Prediction. In: Knowledge-Based and Intelligent Information and Engineering Systems: 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part I. Heidelberg: Springer, pp. 209-218.

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Official URL: http://www.springerlink.com/content/80275171564rxv...

DOI: 10.1007/978-3-642-04595-0_26

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 nontemporal data, giving an added benefit to expressing the recent information in a non-sequential manner.

Item Type:Book Section
ISBN:978-3-642-04594-3
Series Name:Lecture Notes in Computer Science
Volume:5711
Number of Pages:381
Series Name:Lecture Notes in Computer Science
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Generalities > Computer Science and Informatics
Group:School of Design, Engineering & Computing > Smart Technology Research Centre
ID Code:9965
Deposited By:Professor Bogdan Gabrys
Deposited On:12 Jun 2009 01:06
Last Modified:07 Mar 2013 15:08

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