Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment

Geng Cui*, Man Wong, Xiang WAN

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)341-374
Number of pages34
JournalJournal of Management Information Systems
Issue number1
Publication statusPublished - 1 Jul 2012

Scopus Subject Areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management

User-Defined Keywords

  • cost-sensitive learning
  • customer relationship management
  • direct marketing
  • forecasting
  • priority sampling


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