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Article
Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment
Journal of Management Information Systems
  • Geng CUI, Lingnan University
  • Man Leung WONG, Lingnan University, Hong Kong
  • Xiang WAN, Hong Kong Babtist University, Hong Kong
Document Type
Journal article
Publication Date
1-1-2012
Abstract
Because of the unbalanced class and skewed profit distribution in customer purchase data, the unknown and variant costs of false negative errors are a common problem for predicting the high-value customers in marketing operations. Incorporating cost-sensitive learning into forecasting models can improve the return on investment under resource constraint. This study proposes a cost-sensitive learning algorithm via priority sampling that gives greater weight to the high-value customers. We apply the method to three data sets and compare its performance with that of competing solutions. The results suggest that priority sampling compares favorably with the alternative methods in augmenting profitability. The learning algorithm can be implemented in decision support systems to assist marketing operations and to strengthen the strategic competitiveness of organizations.
DOI
10.2753/MIS0742-1222290110
E-ISSN
1557928X
Publisher Statement

Copyright © 2012 M.E. Sharpe, Inc.

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Citation Information
Cui, G., Wong, M. L., & Wan, X. (2012). Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment. Journal of Management Information Systems, 29(1), 341-374. doi: 10.2753/MIS0742-1222290110