Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1006676015154
Title: Customer retention via data mining
Authors: Ng, K.
Liu, H. 
Keywords: Customer retention
Data mining
Deviation analysis
Feature selection
Multiple level association rules
Issue Date: 2000
Citation: Ng, K., Liu, H. (2000). Customer retention via data mining. Artificial Intelligence Review 14 (6) : 569-590. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1006676015154
Abstract: "Customer Retention" is an increasingly pressing issue in today's ever-competitive commercial arena. This is especially relevant and important for sales and services related industries. Motivated by a real-world problem faced by a large company, we proposed a solution that integrates various techniques of data mining, such as feature selection via induction, deviation analysis, and mining multiple concept-level association rules to form an intuitive and novel approach to gauging customer loyalty and predicting their likelihood of defection. Immediate action triggered by these "early-warnings" resulting from data mining is often the key to eventual customer retention.
Source Title: Artificial Intelligence Review
URI: http://scholarbank.nus.edu.sg/handle/10635/39270
ISSN: 02692821
DOI: 10.1023/A:1006676015154
Appears in Collections:Staff Publications

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