Constrained optimization with genetic algorithm : improving profitability of targeted marketing

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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.
Original languageEnglish
Title of host publicationProceedings - 2010 International Conference on Management of e-Commerce and e-Government, ICMeCG 2010
Number of pages5
Publication statusPublished - 1 Jan 2010


  • Constrained optimization
  • Direct marekting
  • Genetic algorithm
  • Proftability


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