Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment.

Author List: Cui, Geng; Wong, Man Leung; Wan, Xiang;
Journal of Management Information Systems, 2012, Volume 29, Issue 1, Page 341-374.
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.
Keywords: cost-sensitive learning; customer relationship management; direct marketing; forecasting; priority sampling
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List of Topics

#215 0.251 data classification statistical regression mining models neural methods using analysis techniques performance predictive networks accuracy method variables prediction problem measure
#288 0.195 customer customers crm relationship study loyalty marketing management profitability service offer retention it-enabled web-based interactions operations sales strategy channels set
#271 0.146 technology investments investment information firm firms profitability value performance impact data higher evidence diversification industry payoff return findings decisions greater
#95 0.123 learning mental conceptual new learn situated development working assumptions improve ess existing investigates capture advanced proposes types context building acquisition
#20 0.081 procurement firms strategy marketing unified customers needs products strategies availability informedness proprietary purchase resonance policies open-source compatible competitors differentiation involve
#97 0.060 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing