Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment
Document Type
Journal article
Source Publication
Journal of Management Information Systems
Publication Date
1-1-2012
Volume
29
Issue
1
First Page
341
Last Page
374
Publisher
Taylor & Francis Inc.
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
Print ISSN
07421222
E-ISSN
1557928X
Publisher Statement
Copyright © 2012 M.E. Sharpe, Inc.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
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