Title
Constrained optimization with genetic algorithm : improving profitability of targeted marketing
Document Type
Conference paper
Source Publication
Proceedings - 2010 International Conference on Management of e-Commerce and e-Government, ICMeCG 2010
Publication Date
1-1-2010
First Page
26
Last Page
30
Keywords
Constrained optimization, Direct marekting, Genetic algorithm, Proftability
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.
DOI
10.1109/ICMeCG.2010.14
ISBN
9780769542454
Publisher Statement
Copyright © 2010 IEEE. Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Cui, G., Wong, M. L., & Wan, X. (2011). Constrained optimization with genetic algorithm : improving profitability of targeted marketing. In 2010 International Conference on Management of e-Commerce and e-Government, Chengdu, 2010 (pp.26-30). doi: 10.1109/ICMeCG.2010.14
