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

Research output: Journal PublicationsJournal Article (refereed)

12 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)341-374
Number of pages34
JournalJournal of Management Information Systems
Volume29
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

Fingerprint Dive into the research topics of 'Cost-sensitive learning via priority sampling to improve the return on marketing and CRM investment'. Together they form a unique fingerprint.

  • Cite this