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Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm

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  • Cui, Geng
  • Wong, Man Leung
  • Wan, Xiang

Abstract

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.

Suggested Citation

  • Cui, Geng & Wong, Man Leung & Wan, Xiang, 2015. "Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 27-37.
    • Handle: RePEc:eee:joinma:v:29:y:2015:i:c:p:27-37
    DOI: 10.1016/j.intmar.2014.09.001
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    Cited by:

    1. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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