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

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

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