DocumentCode :
2959871
Title :
Constrained Optimization with Genetic Algorithm: Improving Profitability of Targeted Marketing
Author :
Cui, Geng ; Wong, Man Leung ; Wan, Xiang
Author_Institution :
Dept. of Marketing & Int. Bus., Lingnan Univ., Hong Kong, China
fYear :
2010
fDate :
23-24 Oct. 2010
Firstpage :
26
Lastpage :
30
Abstract :
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.
Keywords :
decision making; forecasting theory; genetic algorithms; marketing; profitability; DMAX model; constrained optimization; consumer purchases; direct marketing forecasting models; genetic algorithm; management decision-making; profitability; response probabilities; targeted marketing; tenfold cross validation; Classification algorithms; Forecasting; Gallium; Genetic algorithms; Optimization; Predictive models; Profitability; constrained optimization; direct marekting; genetic algorithm; proftability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Management of e-Commerce and e-Government (ICMeCG), 2010 Fourth International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8507-9
Type :
conf
DOI :
10.1109/ICMeCG.2010.14
Filename :
5628625
Link To Document :
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