Constrained Optimization with Genetic Algorithm Improving Profitability of Targeted Marketing
Direct marketing forecasting models have focused on estimating the response probabilities of consumer purchases and neglected the profitability of customers. This study proposes a method of constrained optimization using genetic algorithm to maximize the profitability at the top deciles of a customer list. We apply this method to a direct marketing dataset using tenfold cross-validation. The results from this method compare favorably with the unconstrained model and that of the DMAX model. The method of constrained optimization has distinctive advantages in augmenting the profitability of direct marketing campaigns. We explore the implications for targeted marketing problems and for assisting management decision-making and augmenting profitability of direct marketing.
constrained optimization genetic algorithm direct marekting proftability
Geng Cui Man Leung Wong Xiang Wan
Department of Marketing and International Business Lingnan University Hong Kong Department of Computing and Decision Sciences Lingnan University Hong Kong Department of Computer Sciences Hong Kong University of Science & Technology Hong Kong
国际会议
成都
英文
26-30
2010-10-23（万方平台首次上网日期，不代表论文的发表时间）