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Targeting high value customers while under resource constraint : partial order constrained optimization with genetic algorithm
Journal of Interactive Marketing
  • Geng CUI, Lingnan University, Hong Kong, and Guangdong University of Foreign Studies, China
  • Man Leung WONG, Lingnan University, Hong Kong
  • Xiang WAN, Hong Kong Baptist University
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Journal article
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To maximize sales or profit given a fixed budget, direct marketing targets a preset top percentage of consumers who are the most likely to respond and purchase a greater amount. Existing forecasting models, however, largely ignore the resource constraint and render sup-optimal performance in maximizing profit given the budget constraint. This study proposes a model of partial order constrained optimization (POCO) using a penalty weight that represents the marginal penalty for selecting one more customer. Genetic algorithms as a tool of stochastic optimization help to select models that maximize the total sales at the top deciles of a customer list. The results of cross-validation with a direct marketing dataset indicate that the POCO model outperforms the competing methods in maximizing sales under the resource constraint and has distinctive advantages in augmenting the profitability of direct marketing.

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Copyright © 2014 Published by Elsevier Inc

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Cui, G., Wong, M. L., & Wan, X. (2015). Targeting high value customers while under resource constraint: Partial order constrained optimization with genetic algorithm. Journal of Interactive Marketing, 29(1), 27-37. doi: 10.1016/j.intmar.2014.09.001