Sequential manifold learning for efficient churn prediction

Authors:
Kyoungok Kim;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang 790-784, South Korea;Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Nowadays, thanks to the rapid evolvement of information technology, an explosively large amount of information with very high-dimensional features for customers is being accumulated in companies. These companies, in turn, are exerting every effort to develop more efficient churn prediction models for managing customer relationships effectively. In this paper, a novel method is proposed to deal with a high-dimensional large data set for constructing better churn prediction models. The proposed method starts by partitioning a data set into small-sized data subsets, and applies sequential manifold learning to reduce high-dimensional features and give consistent results for combined data subsets. The performance of the constructed churn prediction model using the proposed method is tested using an E-commerce data set by comparing it with other existing methods. The proposed method works better and is much faster for high-dimensional large data sets without the need for retraining the original data set to reduce the dimensions of new test samples.